SlideShare a Scribd company logo
1 of 6
Download to read offline
Analyzing Enterprise Social Media Networks
                                                Marc Smith, Derek L. Hansen, Eric Gleave


Abstract— Broadening adoption of social media applications            illustrate the ways people specialize in different patterns of
within the enterprise offers a new and valuable data source for       connection, identifying that person as occupying and
insight into the social structure of organizations. Social media      performing a particular "role" (or roles) in the organization.
applications generate networks when employees use features to         Examples of roles include “answer people" [3] who
create "friends" or "contact" networks, reply to messages from        disproportionately provide the answers to questions asked in
other users, edit the same documents as others, or mention the        message board environments, "discussion people" who engage
same or similar topics. The resulting networks can be analyzed        in extended exchanges of messages in large and populous
to reveal basic insights into an organization's structure and         threaded discussions [4], "discussion starters" who demonstrate
dynamics. The creation and analysis of sample social media
                                                                      influence over the topics discussed by the "discussion people"
network datasets is described to illustrate types of enterprise
networks and considerations for their analysis.
                                                                      [5], "influential" people who are well connected to others who
                                                                      are more highly connected than they are, and boundary
Keywords- Social Network, Enterprise, Social Media, Analysis,         spanners who bridge between unconnected subgroups [6]. The
Social Graph, Organization                                            ability to identify individuals within an organization with
                                                                      particular network properties can be applied to improve
                          I.     INTRODUCTION                         enterprise search applications, provide better reporting and
     Social network data sets are generated by many tools, not        ranking services, and can be used to guide management by
just those called "social networking services." A rich new            producing reports on the rates of internal connection within and
source of social network data is created as enterprises and           across groups in an organization. Changes over time can also
organizations adopt social media tools like message boards,           be tracked to better understand the impact of external or
blogs, wikis, friend and contact networks, activity streams and       internal events such as changes in employee size or makeup.
file, photo, and video shares. Enterprise social media                    Following a review of related work, we document a
applications bring applications found on the public Internet into     growing number of different social networks being generated
the confines of an institution's firewall. These tools serve as       as enterprises adopt social media applications. We note the
detailed records of social networks, providing maps of the            various properties of these networks and describe the
structure of social connections within an organization. Data          construction and application of social network metrics for
captured from social media tools also create networks                 reporting on the quality, health and leadership of social media
connecting individuals with artifacts (e.g., digital artifacts) and   spaces. We conclude with a discussion of emerging forms of
groups. While social networks have always existed in human            social network data sets, potential concerns with using this
institutions, only recently have such richly detailed networks        data, and the directions for future work.
been made available in machine readable format as a natural
byproduct of interaction between people and their artifacts.                               II.   RELATED WORK
    Extracting, processing, and analyzing these networks can             Social network analysis has a long history and connection
reveal important patterns in the structure and dynamics of the        to the study of organizations and businesses. Early social
institutions that adopt these tools. Network analysis is the          network literature was built on manually collected and
sociological application of mathematical graph theory to data         processed data about social ties. Researchers would typically
representing sets of relationships. Social network structures are     observe or survey organization members, asking each to list
created when people connect to one another through a range of         those they came in contact with regularly for a variety of tasks
ties. Network analysis is a set of methods for calculating            and purposes. The prohibitive cost of this approach was a
measures that describe a population of "nodes" (representing          major limiting factor in the widespread application of social
people and other entities) and their "edges" or relationships to      network analysis in enterprises and organizations. Despite these
one another. These metrics can describe the overall shape and         challenges, a healthy subfield developed around social network
size of a network as well as describe the location and                analysis of large organizations, as evidenced by the growing
connection pattern of each node (or vertex) in the network.           body of literature written for business audiences [7-9]. Social
Tools that visualize networks can reveal additional insights,         network analysis and metrics are described in [10].
particularly when they integrate visual properties (e.g., node            Researchers and practitioners in the subfield of enterprise
size or color) with network metrics (e.g., degree, betweenness        social network analysis have documented the impact social
centrality) [1,2].                                                    structure has on outcomes of interest to organizations and
    When applied to enterprise social media networks, network         employees. For example, Cross and colleagues illustrate
analysis metrics and visualizations can highlight important           several practical applications of social network analysis for
people, events, and subgroups within an organization. Each            corporations and other large organizations, highlighting
person in the organization is connected to a potentially              differences between healthy and under-performing divisions
different number and set of other people. Network metrics             and the value of organization spanning connections [7-8].
   Identify applicable sponsor/s here
Others like Burt provide compelling evidence that individuals       never meet or occupy the same locations at the same time [16].
who bridge structural holes are promoted faster than others [6].    The term “hyperties” echoes the attention Goffman gave to “tie
                                                                    signs” that indicated a connection between two people.
    Researchers and practitioners have begun to use
                                                                    Hyperties, unlike analog tie signs, are machine authored based
automatically captured social media data to create and analyze      on a range of statistical associations and similarities.
social networks. The everyday use of social media tools for
day-to-day activities generates rich network data without               To date, analysis of social networks has remained largely
having to explicitly ask each employee about their personal         an academic endeavor conducted by PhDs trained to apply its
connections. Researchers interested in enterprise social            specialized concepts and software to answer hypotheses of
networks have begun to explore these networks. For example,         interest primarily to academics. We envision a time when
Perer and Smith [11] provided a tool that allowed employees to      social network analysis is also used by organizational
visualize their corporate email collection. The resulting images    managers, online community administrators, and interested
highlight structures of communication activity across the           individuals to inform their decisions about practical real-world
organizational chart of the corporation. Others have related        problems. For this to become a reality, tools and strategies must
email usage and network characteristics to individual               be developed that support the complex process of making sense
productivity [12, 13]. Researchers can now move beyond email        of the many networks that are created by social media tools.
and the very real concerns about research use of private
communication to take full advantage of the network data                In this paper we provide a framework for characterizing the
                                                                    many types of enterprise social media networks. Understanding
created as a byproduct of the use of other more explicitly
public enterprise social media tools such as wikis, forums,         these networks is vital in educating future network analysts and
                                                                    in designing tools that support their analysis and visualization.
blogs, microblogs, and status updates.
                                                                    We next discuss methods of analyzing and using that data in
    Although literature on enterprise social media networks is      the goal of contributing to the development of additional
relatively limited, there has been a surge of research that         network metrics, analytical tools, and theories that take full
examines social networks based on the use of social media           advantage of the many novel social media data sources
tools in non-enterprise settings. A series of papers have           available. We conclude with a review of concerns about the
documented the ways contributors to social media repositories       potential misuse of these types of data.
(e.g., Usenet) have distinct patterns of contribution and
connection to other contributors [3-5]. These patterns are               III.   TYPES OF ENTERPRISE SOCIAL MEDIA NETWORKS
evidence of specialization of behavior in these social spaces.          The use of social media generates a number of networks.
    A separate line of research has focused on providing tools      In most organizations, people adopt a patchwork quilt of social
that help make sense of social network data through                 media tools. The use of each tool may generate one or more
visualizations. Some tools such as NodeXL automatically             social networks of interest. Public and private email
extract social media networks and allow users to perform a set      discussions, message boards, "friends", "buddies", and
of core operations that measure and map the resulting dataset       "contacts", wikis, blogs, and file and photos stores, along with
[1]. Other tools developed by Viegas and colleagues use non-        comments and readership records, (among others) all generate
graph-based visualizations to make sense of social media data       graph structures that can be extracted and analyzed through the
such as email [14] and wikis [15].                                  core methods and measures of social network analysis. The
                                                                    resulting social media networks can vary in important ways. In
    Eagle and Macy have been analyzing the “call graph”             this section we outline a few important dimensions on which
created when people use their telephones and mobile phones to       these networks vary including: unimodal/multimodal,
call one another. Their research on a data set containing all the   direct/indirect, symmetry, and weighted/unweighted.
phone calls in the United Kingdom for a six-week period
highlights different patterns of calling. They find that            A. Network Properties Related to Nodes (i.e., Vertices)
economically prosperous regions display higher diversity of             Unimodal networks link only one type of entity (i.e., nodes)
connections than economically marginal regions.                     together. In standard social network analysis all nodes
                                                                    represent people. However, unimodal networks may connect
    A growing area of research involves the application of          objects to objects (e.g., wiki pages), collections to collections
mobile or location based sensors to capture evidence of social      (e.g., playlists), groups to groups (e.g., corporations), terms to
connections and, by extension, social networks. Sandy               terms, locations to locations, activities to activities, etc. The
Pentland and the SenseNetworks company are building tools           links of unimodal networks may be based on direct associations
that use cell phone towers’ ability to collect data from cell       (e.g., wiki pages linked together based on hypertext) or indirect
phones to map the locations of groups of people over time.          associations (e.g., wiki pages linked together based on number
The resulting patterns are used to group people into “tribes”       of co-editors). A number of standard network metrics such as
based on common, overlapping habits. Even if two people have        degree, betweenness centrality, eigenvector centrality,
never met, their common use of certain kinds of spaces and          closeness centrality, and measures of network density can be
transit systems soon build a link based on their shared visits to   used to characterize these networks.
the same kinds of restaurants, theaters, office buildings, and
highways or rail lines. These connections are examples of what          Multimodal networks are composed of mixtures of entities.
Counts and Smith call “hyperties,” or links that are created        For example, people, documents, and companies might all co-
between people when machines find associations between              exist in the same network graph. A common type of
people based on common locations or activities even if people       multimodal network is a bimodal network that connects people
to a set of similar objects. An example of a bimodal network is                        Many of the standard network metrics do not apply to
shown in Figure 1, where people are linked to wiki pages they                       multimodal networks (e.g., betweenness centrality), although
have edited.                                                                        some apply if appropriately interpreted (e.g., degree) and other,
                                                                                    more specialized, metrics have been devised for common types
                                                                                    of multimodal networks such as bimodal networks.
                                                                                       Multimodal networks can be reduced to uni-modal
                                                                                    networks. For example, "people to document to people"
                                                                                    networks can be transformed into "people to people" networks
                                                                                    and/or "document to document" networks. An example of a
                                                                                    network transformed from a bi-modal to a single mode is
                                                                                    shown in Figures 1 and 2. Figure 2 shows the person to person
                                                                                    network based on wiki page co-edits. A complementary graph
                                                                                    could be created that connects wiki pages to wiki pages based
                                                                                    on the number of people who have edited both pages. More
                                                                                    generally, this approach can be used to relate objects of all
                                                                                    types (e.g., books, photos, audio recordings) based on social
                                                                                    network ties rather than content indexes or keyword similarity.
                                                                                    Such networks are the raw material for recommender systems:
                                                                                    queries that generate the results of "people who linked to this
                                                                                    document also linked to these documents" or "if you link to this
                                                                                    document, you may want to link to these people".
                                                                                    B. Network Properties Related to Ties (i.e., Edges)
                                                                                        Networks differ in several ways based on the type of ties
                                                                                    that connect nodes to one another, whether the networks are
                                                                                    unimodal or multimodal. These distinctions affect the metrics
 Figure 1. Bimodal Wiki Page and Editor Graph. This bimodal graph shows             and maps generated from them, as well as their interpretation.
    employees (circles) and wiki pages (squares). Edge thickness indicates          Although these graph types are described in social network
  number of edits to a page. Node size is based on degree (i.e., large squares
   have been edited by many people; large circles have edited many pages).          analysis textbooks, they have not been related to social media
 Nodes with less than 2 degrees have been removed. Thus, employee e_2132            network datasets in any systematic way.
has a high degree, but only edits pages that nobody else has edited. In contrast,
          employee 2105 edits pages that are edited by many people.                     Direct graphs describe explicit relationships between
                                                                                    entities such as social networking site’s ability to friend or
                                                                                    follow another person. Indirect graphs describe relationships
                                                                                    between entities based on implicit relationships such as the
                                                                                    relationship between people based on wiki page co-edits as
                                                                                    shown in Figure 2. Another example would be two people
                                                                                    connected because they are members of the same group even
                                                                                    though they may not know one another.
                                                                                        In symmetric graphs, the relationship tie is reciprocal
                                                                                    between the two entities that are connected. For example, in
                                                                                    many social networking sites (e.g., Facebook) a Friendship
                                                                                    relationship is symmetric since if Person A has “friended”
                                                                                    Person B then Person B has also “friended” Person A. In
                                                                                    contrast, other networks are asymmetric, suggesting that a tie
                                                                                    from Person A to Person B is not necessarily reciprocated. For
                                                                                    example, Person A may follow Person B on the Twitter micro-
                                                                                    blogging service without Person B following Person A.
                                                                                    Another example is the reply network created in discussion
                                                                                    forums and email lists. Asymmetric graphs are often
                                                                                    represented using arrows on the edges of a graph.
 Figure 2. Unimodal Wiki Page Editor Graph. This unimodal graph shows
                                                                                        Weighted graphs allow for the differentiation of edges
  only people. It is based on a transformed version of the data represented in
   Figure 1. People who have coedited at least 5 of the same wiki pages are         based on some criteria of interest (e.g., number of messages
connected with an edge. Thicker edges represent more co-edited pages. Node          exchanged or posted). They are often represented visually as
radius is based on degree. Nodes are darker if they have a higher betweenness       thicker or darker lines connecting nodes as was done in Figures
 centrality. Note that e_2105 shows up prominently in this graph, but e_2132        1 and 2. All edges in an unweighted graph are the same. An
doesn't show up at all, since he only edits pages others have not edited. Thus,     example of an unweighted edge would be a friendship tie in a
   each graph highlights different individuals who may play important and
            distinct roles, as well as their relationship to each other.            social network site.
Table I. Social Network Types (for person to person unimodal networks)
  Direct /   Symmetry     Edge           Examples
  Indirect                Weight
  Direct     Symmetric    Unweighted     Person A & B are friends or contacts (e.g., social networking friends as in Facebook or LinkedIn)
  Direct     Symmetric    Weighted       Person A & B are friends and friendship “strength” (i.e., weight) is measured by number of communications.
  Direct     Asymmetric   Unweighted     Person A follows person B (e.g., Twitter); Person A rated person B
  Direct     Asymmetric   Weighted       Person A reads or comments on Person B’s content (e.g., blog, forum post). Weight = # of reads or comments.
  Indirect   Symmetric    Unweighted     Person A & B are both members of a group or network
  Indirect   Symmetric    Weighted       Person A & B have both edited a document (e.g., wiki page) or visited a location; Weight = # of edits or visits.
  Indirect   Asymmetric   Unweighted     Person A joined a group before Person B joined the group
  Indirect   Asymmetric   Weighted       Person A edited a document before Person B edited the same document

    Table 1 shows examples of the various combinations of                               IV.    ANALYZING ENTERPRISE SOCIAL NETWORKS
these graph types using unimodal networks that include only
people as the nodes. However, these same properties apply to                      Making sense of the enormous amount of potential data is a
unimodal networks with other entities (e.g., wiki page to wiki                central challenge of social network analysis. It is essential that
page networks), as well as multimodal networks.                               analysts identify their primary goals and research questions in
                                                                              order to select appropriate metrics and visualizations. Some of
C. Single and Cumulative Networks                                             the most important social network related questions include:
     So far, we have focused on networks created by a single                        •    What kinds of roles are being performed within an
technical platform (e.g., a wiki, a social networking site, a                            enterprise’s social media repositories?
discussion forum). It is often useful to analyze these networks
because interpretation of the nodes and edges is fairly                             •    Which individuals play important social roles within
straightforward. However, enterprises often use many different                           an enterprise or organization?
technical platforms making it possible to create cumulative                         •    What subgroups exist? Do connections between
(i.e., hybrid) networks that describe social ties that are captured                      organizational divisions exist? Who plays the bridge
from usage of multiple technical platforms. For example, a                               roles that connect otherwise unconnected groups?
single network generated from a corporate system with a single
login can represent the ties between individuals based on their                     •    How do some people convert their contacts to adopt a
wiki co-edits, friendships, discussion forum replies, and                                new technology or practice more than others?
semantic overlap. Networks where ties can indicate multiple
                                                                                    •    How do the overall structures of an enterprise’s social
things (e.g., friendship, co-edits) are called multiplex networks.
                                                                                         networks change after a particular event (e.g., a
    Multiplex networks add significant complexity to                                     company social; a round of new hires or layoffs)?
interpretation and analysis as people, threads, blogs, forums,
                                                                                  Almost all forms of analysis and research questions will
documents, topics and keywords all exist in the same data
                                                                              require the transformation of event data into network data. For
space. The abundance of related network data makes possible
                                                                              example, logs of who replied to whose message can be
new types of analysis not historically feasible due to the costs
                                                                              transformed into an edge list with the name of a replier to a
of collecting data. The increased analytic complexity may be
                                                                              message in one column and the person she is replying to in the
justified and offset by the added insight into how people are
                                                                              next column. An edge list is a compact representation of a set
connected to others through different media (i.e. wiki,
                                                                              of relationships that in aggregate forms a network. An
discussion forum, friends). However, the growing challenge
                                                                              alternative representation of a network is an incidence matrix
imposed by managing many networks over multiple time slices
                                                                              of entities with the strength of their tie at the intersection of the
highlights the need for better tools to support the workflow and
                                                                              row and column representing people.
management of large social media network datasets.
                                                                                  With an edge list or matrix representation of a social
     Existing tools do allow for analysis of these networks,
                                                                              network in hand, the next step is to calculate a set of
although a bit of extra work is typically needed. One strategy
                                                                              descriptive network metrics. These may include the count of
for dealing with cumulative networks is to collapse several
                                                                              nodes, edges, the overall graph density, and for each node its
networks into one. A directed graph may be used where
                                                                              in-degree and out-degree (when the network is directed,
undirected ties are translated into two directed ties in opposing
                                                                              otherwise just degree), clustering coefficient, betweenness
directions. A network tie could be shown between two people
                                                                              centrality, closeness centrality, eigenvector centrality, and tie
(a) if they have co-edited a wiki page AND are friends, (b) if
                                                                              strength. Not all of these measures can be applied to every type
they have co-edited a wiki page OR are friends, or (c) the
                                                                              of graph. In particular, multimodal graphs require careful
weight of the tie could depend on the number of different
                                                                              interpretation of each term. For example, in Figure 1, the
networks that connect two individuals. Another approach to
                                                                              degree for people indicates the number of unique wiki pages
visually displaying cumulative networks is to show the ties
                                                                              they have edited, whereas the degree of each wiki page
between people (e.g., forum replies) and change the node
                                                                              indicates the number of unique authors of that page. A metric
attributes (e.g., size) based on involvement in another medium
                                                                              such as betweenness centrality does not have a clear definition
(e.g., edits to the wiki). A final approach is to create
                                                                              in such a network either, since it applies to uni-modal
complementary graphs using similar visual properties and
                                                                              networks.
labels to help compare them (as was done in Figures 1 and 2).
Table II. Social Roles and Social Metrics
                                                             Answer Person   Question Person         Discussion or           Originator        Influencer
                                                                                                    Comment Person
                           Degree (In-Degree + Out-Degree)      High               Low                  High                  High              Medium
                           Reply In-Degree                      Low                Low                  High                  High               High
                           Reply Out-Degree                     High               Low                  High             Low to Medium            Low
                           Read In-Degree                       High               Low                  High                  High               High
          Social Metrics



                           Read Out-Degree                     Medium              Low                  High             Low to medium            Low
                           Betweenness Centrality               High               Low                  High                  High               High
                           Neighbors’ Mean Degree               Low                High                 High                  High               High
                           Closeness Centrality                 Low                Low                  Low                   High               High
                           Eigenvector Centrality               High               Low                  High                  High               High
                           Clustering Coefficient               Low                Low                  High             Low to medium            Low
                           Threads Started                      Low                Low                  Low                   High                Low
                           Messages Posted                      High               Low                  High                  High                Low
                           Reply Messages                       High               Low                  High             Low to medium            Low
                           Days Active                          High               Low                  High                  High           Low to medium

    Once a set of social media networks have been constructed                          people. Table III shows an example of an aggregate Discussion
and social network measurements have been calculated, the                              Person score that can be constructed from six ratios. Each of
resulting dataset can be used for many applications. For                               the terms is a mixture of social metrics, designed to range from
example, they can be used to create reports about community                            0 to 1 with larger values indicating a higher probability of
health, comparisons of subgroups, and identification of                                performing the social role of discussion person.
important individuals, as well as in applications that rank, sort,                                               Table III. Discussion Person Metrics
compare, and search for content and experts.
                                                                                       Term                 Value                            Explanation
    Sorting and visualizing social media datasets based on                             Verbosity            1– (Thread count/total posts)    More messages per thread
network attributes and metrics helps highlight important                                                                                     is better
documents and individuals. Users and their documents,                                  Initiation           1– (initiated threads/total      Ego avoids initiating
                                                                                                            threads)                         threads
messages, comments, and posts have highly variable network                             Outgoing             Outdegree/degree                 Ego replies to many
properties. For example, some people have a high degree in                             Attractiveness       Indegree/(count of replying      Many others reply to Ego
their friends graph while others are sparsely connected. Some                                               authors)
people are connected to others who are well connected to each                          Connectedness        1–1/mean(neighbors’ degree)      Ego’s alters have high
                                                                                                                                             degree, are highly
other creating a clique, while others are connected to people                                                                                connected
that are relatively unconnected. Mapping network metrics onto                          Activity             (days with a reply post) /       Ego’s participation across
visual properties as we have done in Figures 1 and 2 allows                                                 (days since last –first visit)   possible days
analysts to quickly identify important people, which can then
be explored further by looking at their content.                                           As indicated in Table II, combinations of these metrics can
    While social network analysis and visualization is largely a                       be used to identify other social roles as well. Data from other
manual process due to its complexity, common patterns can be                           networks (e.g., wikis, social networking sites, semantic terms)
automatically detected to help provide insights into common                            could also be used to augment these social roles or help
social practices and roles. New conglomerations of network                             identify roles that span multiple networks. While we have
analysis metrics can help explain many social phenomena                                focused on identifying social roles, similar techniques could be
including group formation, group cohesion, social roles,                               used to develop aggregate metrics for a host of other variables
personal influence, and community health. Currently, few such                          of interest such as community health, group cohesion,
aggregate social metrics have been rigorously developed                                boundary spanners, etc. Comparing these values across
despite increased interest in them from corporations and                               communities, groups, and individuals can help managers,
community and organizational analysts. Here we briefly                                 analysts, and individuals know how well they are performing
describe how specific social metrics map to aggregate social                           compared to others. It can also help managers know when
roles (i.e., types of participants).                                                   interventions or education may help improve performance, or
                                                                                       help identify successful individuals and groups so best
    Table II shows various social metrics that describe each                           practices can be identified and shared. Tracking metrics over
node’s activity and network position. Five different social roles                      time can help managers measure the effect of interventions.
are described in terms of these social metrics. Each social role
can be captured in a composite value that takes various metrics                                             V.      DISCUSSION & CONCLUSIONS
into consideration. Composite values must select the most                                  Social media tools provide a wealth of data that can be
important social metrics, scale them appropriately, and weigh                          transformed into insights about the structure and dynamics of
each individual factor.                                                                an enterprise or organization. These insights can be generated
    An example formula can provide a basic scoring for                                 through manual analysis and visualization techniques or
"discussion people", a type of contributor who contributes                             captured, albeit imperfectly, through aggregate social metrics.
heavily to conversations that are often started by others. These                       Managers and analysts can use these metrics to better
participants are often highly connected to other discussion                            understand organizational dynamics, allowing them to better
measure the effects of interventions and events. These metrics                                ACKNOWLEDGMENTS
can also be fed into technical systems like search and sorting        We appreciate the assistance of Telligent Systems, the
and social systems like organizational health monitoring and       NodeXL development Team, and the students of social media
human resource management. We imagine a future, for                analysis classes at the University of Maryland.
example, in which the rate of integration of new employees
into an organization’s social media practices is measured                                          REFERENCES
precisely and reported to all users of a media platform.           [1]    Smith, M., Shneiderman, B., Milic-Frayling, N., Rodrigues, E.M.,
    These data, over time, will give employees and managers               Barash, V., Dunne, C., Capone, T., Perer, A., Gleave, E. 2009.
                                                                          Analyzing Social (Media) Network Data with NodeXL. In Proceedings
better insight into the empirical patterns of activity and                of Communities and Technologies (C&T) (University Park,
connection within an organization. New paths to prominence                Pennsylvania, June 25 – 27, 2009). ACM, New York, NY.
will emerge as a result, as new kinds of skills attract more       [2]    Perer, A. and Shneiderman, B. 2008. Integrating statistics and
attention in enterprise ranking systems. These measures will              visualization: case studies of gaining clarity during exploratory data
make clear the variation between groups in terms of their                 analysis. In Proceeding of the Twenty-Sixth Annual SIGCHI Conference
cohesion and solidarity, help identify individuals who can                on Human Factors in Computing Systems (Florence, Italy, April 05 - 10,
                                                                          2008). CHI '08. ACM, New York, NY, 265-274. DOI=
bridge organizational silos, and bring to light informal                  http://doi.acm.org/10.1145/1357054.1357101
networks and communities of practice.                              [3]    Welser, H., Gleave, E., & Smith, M. Visualizing the Signatures of Social
    As more organizations and enterprises adopt social media              Roles in Online Discussion Groups. J. Soc. Struct. 8, 2, 2007.
platforms, collections of multiple organization scale data sets    [4]    Barash, V., Smith, M., Getoor, L., and Welser, H. T. Distinguishing
                                                                          Knowledge vs. Social Capital in Social Media with Roles and Context.
will allow for a quantification of the variation among different          2009. ICWSM. Association for the Advancement of Artificial
social media repositories. This will help new managers and                Intelligence.
employees benchmark their own social media usage against           [5]    Himelboim, I., Gleave, E., Smith, M. Discussion catalysts in online
others to identify weaknesses and strengths. Developing a core            political discussions: Content importers and conversation starters.
set of standard social metrics that are widely used to analyze a          Journal of Computer Mediated Communication [Forthcoming].
variety of networks would be invaluable in furthering our          [6]    Burt, R. Structural Holes: The Social Structure of Competition, Harvard
understanding of social phenomena.                                        University Press, 1995.
                                                                   [7]    Cross, R., Parker, A. The Hidden Power of Social Networks:
    Despite the potential benefits of analyzing social media              Understanding How Work Really Gets Done in Organizations, Harvard
networks, there are many potential problems with using them               Business School Press, 2004.
as a basis for mapping actual social relationships. These data     [8]    Cross, R. and Thomas, R. J. Driving Results Through Social Networks:
confound the use of tools with social relationships. An                   How Top Organizations Leverage Networks for Performance and
                                                                          Growth, Jossey-Bass, 2009.
employee may have a number of friends but if he doesn’t like
using the social networking site this fact is less apparent to     [9]    Kilduff, M. and Tsai, W. Social Networks and Organizations, Sage
                                                                          Publications Ltd, 2003.
others. If decisions are made based on social network structures
                                                                   [10]   Scott, J. P. Social Network Analysis: A Handbook, Sage Publications
(e.g., deciding who to lay-off) then people will have perverse            Ltd, 2000.
incentives to use these tools - i.e., trying to friend as many     [11]   Perer, A. and Smith, M. A. 2006. Contrasting portraits of email
people as possible without regard to building actual friendship           practices: visual approaches to reflection and analysis. In Proceedings of
ties. These problems will diminish as more and more work is               the Working Conference on Advanced Visual interfaces (Venezia, Italy,
performed via these tools, but they will surely persist.                  May 23 - 26, 2006). AVI '06. ACM, New York, NY, 389-395. DOI=
                                                                          http://doi.acm.org/10.1145/1133265.1133346
    As new social media applications are created, particularly     [12]   Bulkley, N. & Van Alstyne, M.W. An Empirical Analysis of Strategies
as mobile social software applications and devices become                 and Efficiencies in Social Networks. (February 2006). MIT Sloan
mainstream, new social network datasets become practical to               Research      Paper     No.      4682-08.     Available      at    SSRN:
construct. Social network services like Facebook made direct,             http://ssrn.com/abstract=887406
symmetrical relationship networks very popular. The recent         [13]   Aral, S., Brynjolfsson, E. and Van Alstyne, M. W. Information,
                                                                          Technology and Information Worker Productivity. MIT Sloan Research
rise of Twitter has focused attention on the creation of direct,          Paper. Available at SSRN: http://ssrn.com/abstract=942310
asymmetric networks. As location aware devices and other
                                                                   [14]   Viégas, F. B., Golder, S., and Donath, J. 2006. Visualizing email
sensors become common on mobile devices, new forms of ties                content: portraying relationships from conversational histories. In
can be detected and aggregated into new forms of social                   Proceedings of the SIGCHI Conference on Human Factors in
networks [16]. Systems like SpotMe, nTag, and SenseNet                    Computing Systems (Montréal, Québec, Canada, April 22 - 27, 2006).
illustrate the ways emerging mobile technologies are creating             R. Grinter, T. Rodden, P. Aoki, E. Cutrell, R. Jeffries, and G. Olson,
                                                                          Eds. CHI '06. ACM, New York, NY, 979-988. DOI=
new ways to detect associations between people and places.                http://doi.acm.org/10.1145/1124772.1124919
We hope this paper will help characterize these new networks
                                                                   [15]   Viégas, F. B., Wattenberg, M., and Dave, K. 2004. Studying cooperation
and provide a general strategy for turning the various types of           and conflict between authors with history flow visualizations. In
network data into useful social metrics.                                  Proceedings of the SIGCHI Conference on Human Factors in
                                                                          Computing Systems (Vienna, Austria, April 24 - 29, 2004). CHI '04.
    More enterprises will adopt social media platforms, making            ACM,          New         York,         NY,        575-582.         DOI=
the analysis of network datasets a mainstream element of many             http://doi.acm.org/10.1145/985692.985765
organizations' operations. It is our hope that enterprises will    [16]   Counts, S., Smith, M. Where We Were: Communities for Sharing
take advantage of these networks to illuminate the internal               Space-Time Trails. Proceedings of the 15th annual ACM international
structures and dynamics of organizations in novel and useful              symposium on Advances in geographic information systems (2007).
ways and that researchers will support them in these efforts.

More Related Content

What's hot

A Guide to Social Network Analysis
A Guide to Social Network AnalysisA Guide to Social Network Analysis
A Guide to Social Network AnalysisOlivier Serrat
 
Social Network Analysis for Competitive Intelligence
Social Network Analysis for Competitive IntelligenceSocial Network Analysis for Competitive Intelligence
Social Network Analysis for Competitive IntelligenceAugust Jackson
 
2009-JCMC-Discussion catalysts-Himelboim and Smith
2009-JCMC-Discussion catalysts-Himelboim and Smith2009-JCMC-Discussion catalysts-Himelboim and Smith
2009-JCMC-Discussion catalysts-Himelboim and SmithMarc Smith
 
Making the invisible visible through SNA
Making the invisible visible through SNAMaking the invisible visible through SNA
Making the invisible visible through SNAMYRA School of Business
 
Visible Effort: A Social Entropy Methodology for Managing Computer-Mediated ...
Visible Effort: A Social Entropy Methodology for  Managing Computer-Mediated ...Visible Effort: A Social Entropy Methodology for  Managing Computer-Mediated ...
Visible Effort: A Social Entropy Methodology for Managing Computer-Mediated ...Sorin Adam Matei
 
Subscriber Churn Prediction Model using Social Network Analysis In Telecommun...
Subscriber Churn Prediction Model using Social Network Analysis In Telecommun...Subscriber Churn Prediction Model using Social Network Analysis In Telecommun...
Subscriber Churn Prediction Model using Social Network Analysis In Telecommun...BAINIDA
 
Social Network Analysis power point presentation
Social Network Analysis power point presentation Social Network Analysis power point presentation
Social Network Analysis power point presentation Ratnesh Shah
 
Social network websites: best practices from leading services
Social network websites: best practices from leading servicesSocial network websites: best practices from leading services
Social network websites: best practices from leading servicesFabernovel
 
Social Network Analysis (SNA) and its implications for knowledge discovery in...
Social Network Analysis (SNA) and its implications for knowledge discovery in...Social Network Analysis (SNA) and its implications for knowledge discovery in...
Social Network Analysis (SNA) and its implications for knowledge discovery in...ACMBangalore
 
Social Network Analysis (SNA) 2018
Social Network Analysis  (SNA) 2018Social Network Analysis  (SNA) 2018
Social Network Analysis (SNA) 2018Arsalan Khan
 
Social Network Analysis Workshop
Social Network Analysis WorkshopSocial Network Analysis Workshop
Social Network Analysis WorkshopData Works MD
 
Social network analysis for modeling & tuning social media website
Social network analysis for modeling & tuning social media websiteSocial network analysis for modeling & tuning social media website
Social network analysis for modeling & tuning social media websiteEdward B. Rockower
 
A Picture Is Worth A Thousand Questions Docx
A Picture Is Worth A Thousand Questions DocxA Picture Is Worth A Thousand Questions Docx
A Picture Is Worth A Thousand Questions DocxWebometrics Class
 
05 20275 computational solution...
05 20275 computational solution...05 20275 computational solution...
05 20275 computational solution...IAESIJEECS
 
The Internet is a magnifying glass
The Internet is a magnifying glassThe Internet is a magnifying glass
The Internet is a magnifying glassSorin Adam Matei
 
Preso on social network analysis for rtp analytics unconference
Preso on social network analysis for rtp analytics unconferencePreso on social network analysis for rtp analytics unconference
Preso on social network analysis for rtp analytics unconferenceBruce Conner
 

What's hot (20)

A Guide to Social Network Analysis
A Guide to Social Network AnalysisA Guide to Social Network Analysis
A Guide to Social Network Analysis
 
Social Network Analysis for Competitive Intelligence
Social Network Analysis for Competitive IntelligenceSocial Network Analysis for Competitive Intelligence
Social Network Analysis for Competitive Intelligence
 
2009-JCMC-Discussion catalysts-Himelboim and Smith
2009-JCMC-Discussion catalysts-Himelboim and Smith2009-JCMC-Discussion catalysts-Himelboim and Smith
2009-JCMC-Discussion catalysts-Himelboim and Smith
 
Roles In Networks
Roles In NetworksRoles In Networks
Roles In Networks
 
Making the invisible visible through SNA
Making the invisible visible through SNAMaking the invisible visible through SNA
Making the invisible visible through SNA
 
Visible Effort: A Social Entropy Methodology for Managing Computer-Mediated ...
Visible Effort: A Social Entropy Methodology for  Managing Computer-Mediated ...Visible Effort: A Social Entropy Methodology for  Managing Computer-Mediated ...
Visible Effort: A Social Entropy Methodology for Managing Computer-Mediated ...
 
Subscriber Churn Prediction Model using Social Network Analysis In Telecommun...
Subscriber Churn Prediction Model using Social Network Analysis In Telecommun...Subscriber Churn Prediction Model using Social Network Analysis In Telecommun...
Subscriber Churn Prediction Model using Social Network Analysis In Telecommun...
 
Social Network Analysis power point presentation
Social Network Analysis power point presentation Social Network Analysis power point presentation
Social Network Analysis power point presentation
 
Social network websites: best practices from leading services
Social network websites: best practices from leading servicesSocial network websites: best practices from leading services
Social network websites: best practices from leading services
 
Dv31821825
Dv31821825Dv31821825
Dv31821825
 
Social Network Analysis (SNA) and its implications for knowledge discovery in...
Social Network Analysis (SNA) and its implications for knowledge discovery in...Social Network Analysis (SNA) and its implications for knowledge discovery in...
Social Network Analysis (SNA) and its implications for knowledge discovery in...
 
Social Network Analysis
Social Network AnalysisSocial Network Analysis
Social Network Analysis
 
Social Network Analysis (SNA) 2018
Social Network Analysis  (SNA) 2018Social Network Analysis  (SNA) 2018
Social Network Analysis (SNA) 2018
 
Social Network Analysis Workshop
Social Network Analysis WorkshopSocial Network Analysis Workshop
Social Network Analysis Workshop
 
Social network analysis for modeling & tuning social media website
Social network analysis for modeling & tuning social media websiteSocial network analysis for modeling & tuning social media website
Social network analysis for modeling & tuning social media website
 
A Picture Is Worth A Thousand Questions Docx
A Picture Is Worth A Thousand Questions DocxA Picture Is Worth A Thousand Questions Docx
A Picture Is Worth A Thousand Questions Docx
 
05 20275 computational solution...
05 20275 computational solution...05 20275 computational solution...
05 20275 computational solution...
 
The Internet is a magnifying glass
The Internet is a magnifying glassThe Internet is a magnifying glass
The Internet is a magnifying glass
 
Preso on social network analysis for rtp analytics unconference
Preso on social network analysis for rtp analytics unconferencePreso on social network analysis for rtp analytics unconference
Preso on social network analysis for rtp analytics unconference
 
Web 3.0
Web 3.0Web 3.0
Web 3.0
 

Viewers also liked

2014 TheNextWeb-Mapping connections with NodeXL
2014 TheNextWeb-Mapping connections with NodeXL2014 TheNextWeb-Mapping connections with NodeXL
2014 TheNextWeb-Mapping connections with NodeXLMarc Smith
 
2015 pdf-marc smith-node xl-social media sna
2015 pdf-marc smith-node xl-social media sna2015 pdf-marc smith-node xl-social media sna
2015 pdf-marc smith-node xl-social media snaMarc Smith
 
2015 #MMeasure-Marc Smith-NodeXL Mapping social media using social network ma...
2015 #MMeasure-Marc Smith-NodeXL Mapping social media using social network ma...2015 #MMeasure-Marc Smith-NodeXL Mapping social media using social network ma...
2015 #MMeasure-Marc Smith-NodeXL Mapping social media using social network ma...Marc Smith
 
Think Link: Network Insights with No Programming Skills
Think Link: Network Insights with No Programming SkillsThink Link: Network Insights with No Programming Skills
Think Link: Network Insights with No Programming SkillsMarc Smith
 
20151001 charles university prague - marc smith - node xl-picturing political...
20151001 charles university prague - marc smith - node xl-picturing political...20151001 charles university prague - marc smith - node xl-picturing political...
20151001 charles university prague - marc smith - node xl-picturing political...Marc Smith
 
2016 SocialMedia.Org Marc Smith-NodeXL-Social Media SNA
2016 SocialMedia.Org Marc Smith-NodeXL-Social Media SNA2016 SocialMedia.Org Marc Smith-NodeXL-Social Media SNA
2016 SocialMedia.Org Marc Smith-NodeXL-Social Media SNAMarc Smith
 

Viewers also liked (6)

2014 TheNextWeb-Mapping connections with NodeXL
2014 TheNextWeb-Mapping connections with NodeXL2014 TheNextWeb-Mapping connections with NodeXL
2014 TheNextWeb-Mapping connections with NodeXL
 
2015 pdf-marc smith-node xl-social media sna
2015 pdf-marc smith-node xl-social media sna2015 pdf-marc smith-node xl-social media sna
2015 pdf-marc smith-node xl-social media sna
 
2015 #MMeasure-Marc Smith-NodeXL Mapping social media using social network ma...
2015 #MMeasure-Marc Smith-NodeXL Mapping social media using social network ma...2015 #MMeasure-Marc Smith-NodeXL Mapping social media using social network ma...
2015 #MMeasure-Marc Smith-NodeXL Mapping social media using social network ma...
 
Think Link: Network Insights with No Programming Skills
Think Link: Network Insights with No Programming SkillsThink Link: Network Insights with No Programming Skills
Think Link: Network Insights with No Programming Skills
 
20151001 charles university prague - marc smith - node xl-picturing political...
20151001 charles university prague - marc smith - node xl-picturing political...20151001 charles university prague - marc smith - node xl-picturing political...
20151001 charles university prague - marc smith - node xl-picturing political...
 
2016 SocialMedia.Org Marc Smith-NodeXL-Social Media SNA
2016 SocialMedia.Org Marc Smith-NodeXL-Social Media SNA2016 SocialMedia.Org Marc Smith-NodeXL-Social Media SNA
2016 SocialMedia.Org Marc Smith-NodeXL-Social Media SNA
 

Similar to 2009-Social computing-Analyzing social media networks

Survey of data mining techniques for social
Survey of data mining techniques for socialSurvey of data mining techniques for social
Survey of data mining techniques for socialFiras Husseini
 
Mining and Analyzing Academic Social Networks
Mining and Analyzing Academic Social NetworksMining and Analyzing Academic Social Networks
Mining and Analyzing Academic Social NetworksEditor IJCATR
 
a modified weight balanced algorithm for influential users community detectio...
a modified weight balanced algorithm for influential users community detectio...a modified weight balanced algorithm for influential users community detectio...
a modified weight balanced algorithm for influential users community detectio...INFOGAIN PUBLICATION
 
Towards Decision Support and Goal AchievementIdentifying Ac.docx
Towards Decision Support and Goal AchievementIdentifying Ac.docxTowards Decision Support and Goal AchievementIdentifying Ac.docx
Towards Decision Support and Goal AchievementIdentifying Ac.docxturveycharlyn
 
Fuzzy AndANN Based Mining Approach Testing For Social Network Analysis
Fuzzy AndANN Based Mining Approach Testing For Social Network AnalysisFuzzy AndANN Based Mining Approach Testing For Social Network Analysis
Fuzzy AndANN Based Mining Approach Testing For Social Network AnalysisIJERA Editor
 
Improving Knowledge Handling by building intellegent social systems
Improving Knowledge Handling by building intellegent social systemsImproving Knowledge Handling by building intellegent social systems
Improving Knowledge Handling by building intellegent social systemsnazeeh
 
Evolution of social developer network in oss survey
Evolution of social developer network in oss surveyEvolution of social developer network in oss survey
Evolution of social developer network in oss surveyeSAT Publishing House
 
Current trends of opinion mining and sentiment analysis in social networks
Current trends of opinion mining and sentiment analysis in social networksCurrent trends of opinion mining and sentiment analysis in social networks
Current trends of opinion mining and sentiment analysis in social networkseSAT Publishing House
 
Data mining in social network
Data mining in social networkData mining in social network
Data mining in social networkakash_mishra
 
An Empirical Study On IMDb And Its Communities Based On The Network Of Co-Rev...
An Empirical Study On IMDb And Its Communities Based On The Network Of Co-Rev...An Empirical Study On IMDb And Its Communities Based On The Network Of Co-Rev...
An Empirical Study On IMDb And Its Communities Based On The Network Of Co-Rev...Rick Vogel
 
MINING USER KNOWLEDGE FOR INVESTIGATING THE FACEBOOK.pdf
MINING USER KNOWLEDGE FOR INVESTIGATING THE FACEBOOK.pdfMINING USER KNOWLEDGE FOR INVESTIGATING THE FACEBOOK.pdf
MINING USER KNOWLEDGE FOR INVESTIGATING THE FACEBOOK.pdfWassie Getahun (PhD)
 
A Literature Survey on Ranking Tagged Web Documents in Social Bookmarking Sys...
A Literature Survey on Ranking Tagged Web Documents in Social Bookmarking Sys...A Literature Survey on Ranking Tagged Web Documents in Social Bookmarking Sys...
A Literature Survey on Ranking Tagged Web Documents in Social Bookmarking Sys...IOSR Journals
 
Las redes sociales
Las redes socialesLas redes sociales
Las redes socialesuteco
 
Authorization mechanism for multiparty data sharing in social network
Authorization mechanism for multiparty data sharing in social networkAuthorization mechanism for multiparty data sharing in social network
Authorization mechanism for multiparty data sharing in social networkeSAT Publishing House
 
Multimode network based efficient and scalable learning of collective behavior
Multimode network based efficient and scalable learning of collective behaviorMultimode network based efficient and scalable learning of collective behavior
Multimode network based efficient and scalable learning of collective behaviorIAEME Publication
 

Similar to 2009-Social computing-Analyzing social media networks (20)

Q046049397
Q046049397Q046049397
Q046049397
 
Survey of data mining techniques for social
Survey of data mining techniques for socialSurvey of data mining techniques for social
Survey of data mining techniques for social
 
Mining and Analyzing Academic Social Networks
Mining and Analyzing Academic Social NetworksMining and Analyzing Academic Social Networks
Mining and Analyzing Academic Social Networks
 
Jx2517481755
Jx2517481755Jx2517481755
Jx2517481755
 
Jx2517481755
Jx2517481755Jx2517481755
Jx2517481755
 
a modified weight balanced algorithm for influential users community detectio...
a modified weight balanced algorithm for influential users community detectio...a modified weight balanced algorithm for influential users community detectio...
a modified weight balanced algorithm for influential users community detectio...
 
Towards Decision Support and Goal AchievementIdentifying Ac.docx
Towards Decision Support and Goal AchievementIdentifying Ac.docxTowards Decision Support and Goal AchievementIdentifying Ac.docx
Towards Decision Support and Goal AchievementIdentifying Ac.docx
 
Fuzzy AndANN Based Mining Approach Testing For Social Network Analysis
Fuzzy AndANN Based Mining Approach Testing For Social Network AnalysisFuzzy AndANN Based Mining Approach Testing For Social Network Analysis
Fuzzy AndANN Based Mining Approach Testing For Social Network Analysis
 
Improving Knowledge Handling by building intellegent social systems
Improving Knowledge Handling by building intellegent social systemsImproving Knowledge Handling by building intellegent social systems
Improving Knowledge Handling by building intellegent social systems
 
Evolution of social developer network in oss survey
Evolution of social developer network in oss surveyEvolution of social developer network in oss survey
Evolution of social developer network in oss survey
 
Current trends of opinion mining and sentiment analysis in social networks
Current trends of opinion mining and sentiment analysis in social networksCurrent trends of opinion mining and sentiment analysis in social networks
Current trends of opinion mining and sentiment analysis in social networks
 
Internet
InternetInternet
Internet
 
Internet
InternetInternet
Internet
 
Data mining in social network
Data mining in social networkData mining in social network
Data mining in social network
 
An Empirical Study On IMDb And Its Communities Based On The Network Of Co-Rev...
An Empirical Study On IMDb And Its Communities Based On The Network Of Co-Rev...An Empirical Study On IMDb And Its Communities Based On The Network Of Co-Rev...
An Empirical Study On IMDb And Its Communities Based On The Network Of Co-Rev...
 
MINING USER KNOWLEDGE FOR INVESTIGATING THE FACEBOOK.pdf
MINING USER KNOWLEDGE FOR INVESTIGATING THE FACEBOOK.pdfMINING USER KNOWLEDGE FOR INVESTIGATING THE FACEBOOK.pdf
MINING USER KNOWLEDGE FOR INVESTIGATING THE FACEBOOK.pdf
 
A Literature Survey on Ranking Tagged Web Documents in Social Bookmarking Sys...
A Literature Survey on Ranking Tagged Web Documents in Social Bookmarking Sys...A Literature Survey on Ranking Tagged Web Documents in Social Bookmarking Sys...
A Literature Survey on Ranking Tagged Web Documents in Social Bookmarking Sys...
 
Las redes sociales
Las redes socialesLas redes sociales
Las redes sociales
 
Authorization mechanism for multiparty data sharing in social network
Authorization mechanism for multiparty data sharing in social networkAuthorization mechanism for multiparty data sharing in social network
Authorization mechanism for multiparty data sharing in social network
 
Multimode network based efficient and scalable learning of collective behavior
Multimode network based efficient and scalable learning of collective behaviorMultimode network based efficient and scalable learning of collective behavior
Multimode network based efficient and scalable learning of collective behavior
 

More from Marc Smith

How to use social media network analysis for amplification
How to use social media network analysis for amplificationHow to use social media network analysis for amplification
How to use social media network analysis for amplificationMarc Smith
 
Think link what is an edge - NodeXL
Think link   what is an edge - NodeXLThink link   what is an edge - NodeXL
Think link what is an edge - NodeXLMarc Smith
 
2017 05-26 NodeXL Twitter search #shakeupshow
2017 05-26 NodeXL Twitter search #shakeupshow2017 05-26 NodeXL Twitter search #shakeupshow
2017 05-26 NodeXL Twitter search #shakeupshowMarc Smith
 
20130724 ted x-marc smith-digital health futures empowerment or coercion
20130724 ted x-marc smith-digital health futures empowerment or coercion20130724 ted x-marc smith-digital health futures empowerment or coercion
20130724 ted x-marc smith-digital health futures empowerment or coercionMarc Smith
 
2013 passbac-marc smith-node xl-sna-social media-formatted
2013 passbac-marc smith-node xl-sna-social media-formatted2013 passbac-marc smith-node xl-sna-social media-formatted
2013 passbac-marc smith-node xl-sna-social media-formattedMarc Smith
 
2013 NodeXL Social Media Network Analysis
2013 NodeXL Social Media Network Analysis2013 NodeXL Social Media Network Analysis
2013 NodeXL Social Media Network AnalysisMarc Smith
 
20121010 marc smith - mapping collections of connections in social media with...
20121010 marc smith - mapping collections of connections in social media with...20121010 marc smith - mapping collections of connections in social media with...
20121010 marc smith - mapping collections of connections in social media with...Marc Smith
 
20121001 pawcon 2012-marc smith - mapping collections of connections in socia...
20121001 pawcon 2012-marc smith - mapping collections of connections in socia...20121001 pawcon 2012-marc smith - mapping collections of connections in socia...
20121001 pawcon 2012-marc smith - mapping collections of connections in socia...Marc Smith
 
2012 ona practitioner-courseflyer
2012 ona practitioner-courseflyer2012 ona practitioner-courseflyer
2012 ona practitioner-courseflyerMarc Smith
 
20120622 web sci12-won-marc smith-semantic and social network analysis of …
20120622 web sci12-won-marc smith-semantic and social network analysis of …20120622 web sci12-won-marc smith-semantic and social network analysis of …
20120622 web sci12-won-marc smith-semantic and social network analysis of …Marc Smith
 
20120301 strata-marc smith-mapping social media networks with no coding using...
20120301 strata-marc smith-mapping social media networks with no coding using...20120301 strata-marc smith-mapping social media networks with no coding using...
20120301 strata-marc smith-mapping social media networks with no coding using...Marc Smith
 
20111123 mwa2011-marc smith
20111123 mwa2011-marc smith20111123 mwa2011-marc smith
20111123 mwa2011-marc smithMarc Smith
 
20111103 con tech2011-marc smith
20111103 con tech2011-marc smith20111103 con tech2011-marc smith
20111103 con tech2011-marc smithMarc Smith
 
2011 IEEE Social Computing Nodexl: Group-In-A-Box
2011 IEEE Social Computing Nodexl: Group-In-A-Box2011 IEEE Social Computing Nodexl: Group-In-A-Box
2011 IEEE Social Computing Nodexl: Group-In-A-BoxMarc Smith
 
20110830 Introducing the Social Media Research Foundation
20110830 Introducing the Social Media Research Foundation20110830 Introducing the Social Media Research Foundation
20110830 Introducing the Social Media Research FoundationMarc Smith
 
Personal Digital Archiving 2011 - Charting Collections of Connections in Soci...
Personal Digital Archiving 2011 - Charting Collections of Connections in Soci...Personal Digital Archiving 2011 - Charting Collections of Connections in Soci...
Personal Digital Archiving 2011 - Charting Collections of Connections in Soci...Marc Smith
 
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connectionsMarc Smith
 
Analyzing social media networks with NodeXL - Chapter-14 Images
Analyzing social media networks with NodeXL - Chapter-14 ImagesAnalyzing social media networks with NodeXL - Chapter-14 Images
Analyzing social media networks with NodeXL - Chapter-14 ImagesMarc Smith
 
Analyzing social media networks with NodeXL - Chapter-13 Images
Analyzing social media networks with NodeXL - Chapter-13 ImagesAnalyzing social media networks with NodeXL - Chapter-13 Images
Analyzing social media networks with NodeXL - Chapter-13 ImagesMarc Smith
 
Analyzing social media networks with NodeXL - Chapter- 12 images
Analyzing social media networks with NodeXL - Chapter- 12 imagesAnalyzing social media networks with NodeXL - Chapter- 12 images
Analyzing social media networks with NodeXL - Chapter- 12 imagesMarc Smith
 

More from Marc Smith (20)

How to use social media network analysis for amplification
How to use social media network analysis for amplificationHow to use social media network analysis for amplification
How to use social media network analysis for amplification
 
Think link what is an edge - NodeXL
Think link   what is an edge - NodeXLThink link   what is an edge - NodeXL
Think link what is an edge - NodeXL
 
2017 05-26 NodeXL Twitter search #shakeupshow
2017 05-26 NodeXL Twitter search #shakeupshow2017 05-26 NodeXL Twitter search #shakeupshow
2017 05-26 NodeXL Twitter search #shakeupshow
 
20130724 ted x-marc smith-digital health futures empowerment or coercion
20130724 ted x-marc smith-digital health futures empowerment or coercion20130724 ted x-marc smith-digital health futures empowerment or coercion
20130724 ted x-marc smith-digital health futures empowerment or coercion
 
2013 passbac-marc smith-node xl-sna-social media-formatted
2013 passbac-marc smith-node xl-sna-social media-formatted2013 passbac-marc smith-node xl-sna-social media-formatted
2013 passbac-marc smith-node xl-sna-social media-formatted
 
2013 NodeXL Social Media Network Analysis
2013 NodeXL Social Media Network Analysis2013 NodeXL Social Media Network Analysis
2013 NodeXL Social Media Network Analysis
 
20121010 marc smith - mapping collections of connections in social media with...
20121010 marc smith - mapping collections of connections in social media with...20121010 marc smith - mapping collections of connections in social media with...
20121010 marc smith - mapping collections of connections in social media with...
 
20121001 pawcon 2012-marc smith - mapping collections of connections in socia...
20121001 pawcon 2012-marc smith - mapping collections of connections in socia...20121001 pawcon 2012-marc smith - mapping collections of connections in socia...
20121001 pawcon 2012-marc smith - mapping collections of connections in socia...
 
2012 ona practitioner-courseflyer
2012 ona practitioner-courseflyer2012 ona practitioner-courseflyer
2012 ona practitioner-courseflyer
 
20120622 web sci12-won-marc smith-semantic and social network analysis of …
20120622 web sci12-won-marc smith-semantic and social network analysis of …20120622 web sci12-won-marc smith-semantic and social network analysis of …
20120622 web sci12-won-marc smith-semantic and social network analysis of …
 
20120301 strata-marc smith-mapping social media networks with no coding using...
20120301 strata-marc smith-mapping social media networks with no coding using...20120301 strata-marc smith-mapping social media networks with no coding using...
20120301 strata-marc smith-mapping social media networks with no coding using...
 
20111123 mwa2011-marc smith
20111123 mwa2011-marc smith20111123 mwa2011-marc smith
20111123 mwa2011-marc smith
 
20111103 con tech2011-marc smith
20111103 con tech2011-marc smith20111103 con tech2011-marc smith
20111103 con tech2011-marc smith
 
2011 IEEE Social Computing Nodexl: Group-In-A-Box
2011 IEEE Social Computing Nodexl: Group-In-A-Box2011 IEEE Social Computing Nodexl: Group-In-A-Box
2011 IEEE Social Computing Nodexl: Group-In-A-Box
 
20110830 Introducing the Social Media Research Foundation
20110830 Introducing the Social Media Research Foundation20110830 Introducing the Social Media Research Foundation
20110830 Introducing the Social Media Research Foundation
 
Personal Digital Archiving 2011 - Charting Collections of Connections in Soci...
Personal Digital Archiving 2011 - Charting Collections of Connections in Soci...Personal Digital Archiving 2011 - Charting Collections of Connections in Soci...
Personal Digital Archiving 2011 - Charting Collections of Connections in Soci...
 
20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections20110128 connected action-node xl-sea of connections
20110128 connected action-node xl-sea of connections
 
Analyzing social media networks with NodeXL - Chapter-14 Images
Analyzing social media networks with NodeXL - Chapter-14 ImagesAnalyzing social media networks with NodeXL - Chapter-14 Images
Analyzing social media networks with NodeXL - Chapter-14 Images
 
Analyzing social media networks with NodeXL - Chapter-13 Images
Analyzing social media networks with NodeXL - Chapter-13 ImagesAnalyzing social media networks with NodeXL - Chapter-13 Images
Analyzing social media networks with NodeXL - Chapter-13 Images
 
Analyzing social media networks with NodeXL - Chapter- 12 images
Analyzing social media networks with NodeXL - Chapter- 12 imagesAnalyzing social media networks with NodeXL - Chapter- 12 images
Analyzing social media networks with NodeXL - Chapter- 12 images
 

Recently uploaded

Speed Wins: From Kafka to APIs in Minutes
Speed Wins: From Kafka to APIs in MinutesSpeed Wins: From Kafka to APIs in Minutes
Speed Wins: From Kafka to APIs in Minutesconfluent
 
Structuring Teams and Portfolios for Success
Structuring Teams and Portfolios for SuccessStructuring Teams and Portfolios for Success
Structuring Teams and Portfolios for SuccessUXDXConf
 
UiPath Test Automation using UiPath Test Suite series, part 1
UiPath Test Automation using UiPath Test Suite series, part 1UiPath Test Automation using UiPath Test Suite series, part 1
UiPath Test Automation using UiPath Test Suite series, part 1DianaGray10
 
WebAssembly is Key to Better LLM Performance
WebAssembly is Key to Better LLM PerformanceWebAssembly is Key to Better LLM Performance
WebAssembly is Key to Better LLM PerformanceSamy Fodil
 
THE BEST IPTV in GERMANY for 2024: IPTVreel
THE BEST IPTV in  GERMANY for 2024: IPTVreelTHE BEST IPTV in  GERMANY for 2024: IPTVreel
THE BEST IPTV in GERMANY for 2024: IPTVreelreely ones
 
PLAI - Acceleration Program for Generative A.I. Startups
PLAI - Acceleration Program for Generative A.I. StartupsPLAI - Acceleration Program for Generative A.I. Startups
PLAI - Acceleration Program for Generative A.I. StartupsStefano
 
Buy Epson EcoTank L3210 Colour Printer Online.pdf
Buy Epson EcoTank L3210 Colour Printer Online.pdfBuy Epson EcoTank L3210 Colour Printer Online.pdf
Buy Epson EcoTank L3210 Colour Printer Online.pdfEasyPrinterHelp
 
Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024Patrick Viafore
 
Designing for Hardware Accessibility at Comcast
Designing for Hardware Accessibility at ComcastDesigning for Hardware Accessibility at Comcast
Designing for Hardware Accessibility at ComcastUXDXConf
 
Linux Foundation Edge _ Overview of FDO Software Components _ Randy at Intel.pdf
Linux Foundation Edge _ Overview of FDO Software Components _ Randy at Intel.pdfLinux Foundation Edge _ Overview of FDO Software Components _ Randy at Intel.pdf
Linux Foundation Edge _ Overview of FDO Software Components _ Randy at Intel.pdfFIDO Alliance
 
Simplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdf
Simplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdfSimplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdf
Simplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdfFIDO Alliance
 
AI revolution and Salesforce, Jiří Karpíšek
AI revolution and Salesforce, Jiří KarpíšekAI revolution and Salesforce, Jiří Karpíšek
AI revolution and Salesforce, Jiří KarpíšekCzechDreamin
 
Buy Epson EcoTank L3210 Colour Printer Online.pptx
Buy Epson EcoTank L3210 Colour Printer Online.pptxBuy Epson EcoTank L3210 Colour Printer Online.pptx
Buy Epson EcoTank L3210 Colour Printer Online.pptxEasyPrinterHelp
 
WSO2CONMay2024OpenSourceConferenceDebrief.pptx
WSO2CONMay2024OpenSourceConferenceDebrief.pptxWSO2CONMay2024OpenSourceConferenceDebrief.pptx
WSO2CONMay2024OpenSourceConferenceDebrief.pptxJennifer Lim
 
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...CzechDreamin
 
UiPath Test Automation using UiPath Test Suite series, part 2
UiPath Test Automation using UiPath Test Suite series, part 2UiPath Test Automation using UiPath Test Suite series, part 2
UiPath Test Automation using UiPath Test Suite series, part 2DianaGray10
 
IoT Analytics Company Presentation May 2024
IoT Analytics Company Presentation May 2024IoT Analytics Company Presentation May 2024
IoT Analytics Company Presentation May 2024IoTAnalytics
 
Custom Approval Process: A New Perspective, Pavel Hrbacek & Anindya Halder
Custom Approval Process: A New Perspective, Pavel Hrbacek & Anindya HalderCustom Approval Process: A New Perspective, Pavel Hrbacek & Anindya Halder
Custom Approval Process: A New Perspective, Pavel Hrbacek & Anindya HalderCzechDreamin
 
Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...
Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...
Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...FIDO Alliance
 
Salesforce Adoption – Metrics, Methods, and Motivation, Antone Kom
Salesforce Adoption – Metrics, Methods, and Motivation, Antone KomSalesforce Adoption – Metrics, Methods, and Motivation, Antone Kom
Salesforce Adoption – Metrics, Methods, and Motivation, Antone KomCzechDreamin
 

Recently uploaded (20)

Speed Wins: From Kafka to APIs in Minutes
Speed Wins: From Kafka to APIs in MinutesSpeed Wins: From Kafka to APIs in Minutes
Speed Wins: From Kafka to APIs in Minutes
 
Structuring Teams and Portfolios for Success
Structuring Teams and Portfolios for SuccessStructuring Teams and Portfolios for Success
Structuring Teams and Portfolios for Success
 
UiPath Test Automation using UiPath Test Suite series, part 1
UiPath Test Automation using UiPath Test Suite series, part 1UiPath Test Automation using UiPath Test Suite series, part 1
UiPath Test Automation using UiPath Test Suite series, part 1
 
WebAssembly is Key to Better LLM Performance
WebAssembly is Key to Better LLM PerformanceWebAssembly is Key to Better LLM Performance
WebAssembly is Key to Better LLM Performance
 
THE BEST IPTV in GERMANY for 2024: IPTVreel
THE BEST IPTV in  GERMANY for 2024: IPTVreelTHE BEST IPTV in  GERMANY for 2024: IPTVreel
THE BEST IPTV in GERMANY for 2024: IPTVreel
 
PLAI - Acceleration Program for Generative A.I. Startups
PLAI - Acceleration Program for Generative A.I. StartupsPLAI - Acceleration Program for Generative A.I. Startups
PLAI - Acceleration Program for Generative A.I. Startups
 
Buy Epson EcoTank L3210 Colour Printer Online.pdf
Buy Epson EcoTank L3210 Colour Printer Online.pdfBuy Epson EcoTank L3210 Colour Printer Online.pdf
Buy Epson EcoTank L3210 Colour Printer Online.pdf
 
Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024
 
Designing for Hardware Accessibility at Comcast
Designing for Hardware Accessibility at ComcastDesigning for Hardware Accessibility at Comcast
Designing for Hardware Accessibility at Comcast
 
Linux Foundation Edge _ Overview of FDO Software Components _ Randy at Intel.pdf
Linux Foundation Edge _ Overview of FDO Software Components _ Randy at Intel.pdfLinux Foundation Edge _ Overview of FDO Software Components _ Randy at Intel.pdf
Linux Foundation Edge _ Overview of FDO Software Components _ Randy at Intel.pdf
 
Simplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdf
Simplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdfSimplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdf
Simplified FDO Manufacturing Flow with TPMs _ Liam at Infineon.pdf
 
AI revolution and Salesforce, Jiří Karpíšek
AI revolution and Salesforce, Jiří KarpíšekAI revolution and Salesforce, Jiří Karpíšek
AI revolution and Salesforce, Jiří Karpíšek
 
Buy Epson EcoTank L3210 Colour Printer Online.pptx
Buy Epson EcoTank L3210 Colour Printer Online.pptxBuy Epson EcoTank L3210 Colour Printer Online.pptx
Buy Epson EcoTank L3210 Colour Printer Online.pptx
 
WSO2CONMay2024OpenSourceConferenceDebrief.pptx
WSO2CONMay2024OpenSourceConferenceDebrief.pptxWSO2CONMay2024OpenSourceConferenceDebrief.pptx
WSO2CONMay2024OpenSourceConferenceDebrief.pptx
 
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
 
UiPath Test Automation using UiPath Test Suite series, part 2
UiPath Test Automation using UiPath Test Suite series, part 2UiPath Test Automation using UiPath Test Suite series, part 2
UiPath Test Automation using UiPath Test Suite series, part 2
 
IoT Analytics Company Presentation May 2024
IoT Analytics Company Presentation May 2024IoT Analytics Company Presentation May 2024
IoT Analytics Company Presentation May 2024
 
Custom Approval Process: A New Perspective, Pavel Hrbacek & Anindya Halder
Custom Approval Process: A New Perspective, Pavel Hrbacek & Anindya HalderCustom Approval Process: A New Perspective, Pavel Hrbacek & Anindya Halder
Custom Approval Process: A New Perspective, Pavel Hrbacek & Anindya Halder
 
Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...
Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...
Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...
 
Salesforce Adoption – Metrics, Methods, and Motivation, Antone Kom
Salesforce Adoption – Metrics, Methods, and Motivation, Antone KomSalesforce Adoption – Metrics, Methods, and Motivation, Antone Kom
Salesforce Adoption – Metrics, Methods, and Motivation, Antone Kom
 

2009-Social computing-Analyzing social media networks

  • 1. Analyzing Enterprise Social Media Networks Marc Smith, Derek L. Hansen, Eric Gleave Abstract— Broadening adoption of social media applications illustrate the ways people specialize in different patterns of within the enterprise offers a new and valuable data source for connection, identifying that person as occupying and insight into the social structure of organizations. Social media performing a particular "role" (or roles) in the organization. applications generate networks when employees use features to Examples of roles include “answer people" [3] who create "friends" or "contact" networks, reply to messages from disproportionately provide the answers to questions asked in other users, edit the same documents as others, or mention the message board environments, "discussion people" who engage same or similar topics. The resulting networks can be analyzed in extended exchanges of messages in large and populous to reveal basic insights into an organization's structure and threaded discussions [4], "discussion starters" who demonstrate dynamics. The creation and analysis of sample social media influence over the topics discussed by the "discussion people" network datasets is described to illustrate types of enterprise networks and considerations for their analysis. [5], "influential" people who are well connected to others who are more highly connected than they are, and boundary Keywords- Social Network, Enterprise, Social Media, Analysis, spanners who bridge between unconnected subgroups [6]. The Social Graph, Organization ability to identify individuals within an organization with particular network properties can be applied to improve I. INTRODUCTION enterprise search applications, provide better reporting and Social network data sets are generated by many tools, not ranking services, and can be used to guide management by just those called "social networking services." A rich new producing reports on the rates of internal connection within and source of social network data is created as enterprises and across groups in an organization. Changes over time can also organizations adopt social media tools like message boards, be tracked to better understand the impact of external or blogs, wikis, friend and contact networks, activity streams and internal events such as changes in employee size or makeup. file, photo, and video shares. Enterprise social media Following a review of related work, we document a applications bring applications found on the public Internet into growing number of different social networks being generated the confines of an institution's firewall. These tools serve as as enterprises adopt social media applications. We note the detailed records of social networks, providing maps of the various properties of these networks and describe the structure of social connections within an organization. Data construction and application of social network metrics for captured from social media tools also create networks reporting on the quality, health and leadership of social media connecting individuals with artifacts (e.g., digital artifacts) and spaces. We conclude with a discussion of emerging forms of groups. While social networks have always existed in human social network data sets, potential concerns with using this institutions, only recently have such richly detailed networks data, and the directions for future work. been made available in machine readable format as a natural byproduct of interaction between people and their artifacts. II. RELATED WORK Extracting, processing, and analyzing these networks can Social network analysis has a long history and connection reveal important patterns in the structure and dynamics of the to the study of organizations and businesses. Early social institutions that adopt these tools. Network analysis is the network literature was built on manually collected and sociological application of mathematical graph theory to data processed data about social ties. Researchers would typically representing sets of relationships. Social network structures are observe or survey organization members, asking each to list created when people connect to one another through a range of those they came in contact with regularly for a variety of tasks ties. Network analysis is a set of methods for calculating and purposes. The prohibitive cost of this approach was a measures that describe a population of "nodes" (representing major limiting factor in the widespread application of social people and other entities) and their "edges" or relationships to network analysis in enterprises and organizations. Despite these one another. These metrics can describe the overall shape and challenges, a healthy subfield developed around social network size of a network as well as describe the location and analysis of large organizations, as evidenced by the growing connection pattern of each node (or vertex) in the network. body of literature written for business audiences [7-9]. Social Tools that visualize networks can reveal additional insights, network analysis and metrics are described in [10]. particularly when they integrate visual properties (e.g., node Researchers and practitioners in the subfield of enterprise size or color) with network metrics (e.g., degree, betweenness social network analysis have documented the impact social centrality) [1,2]. structure has on outcomes of interest to organizations and When applied to enterprise social media networks, network employees. For example, Cross and colleagues illustrate analysis metrics and visualizations can highlight important several practical applications of social network analysis for people, events, and subgroups within an organization. Each corporations and other large organizations, highlighting person in the organization is connected to a potentially differences between healthy and under-performing divisions different number and set of other people. Network metrics and the value of organization spanning connections [7-8]. Identify applicable sponsor/s here
  • 2. Others like Burt provide compelling evidence that individuals never meet or occupy the same locations at the same time [16]. who bridge structural holes are promoted faster than others [6]. The term “hyperties” echoes the attention Goffman gave to “tie signs” that indicated a connection between two people. Researchers and practitioners have begun to use Hyperties, unlike analog tie signs, are machine authored based automatically captured social media data to create and analyze on a range of statistical associations and similarities. social networks. The everyday use of social media tools for day-to-day activities generates rich network data without To date, analysis of social networks has remained largely having to explicitly ask each employee about their personal an academic endeavor conducted by PhDs trained to apply its connections. Researchers interested in enterprise social specialized concepts and software to answer hypotheses of networks have begun to explore these networks. For example, interest primarily to academics. We envision a time when Perer and Smith [11] provided a tool that allowed employees to social network analysis is also used by organizational visualize their corporate email collection. The resulting images managers, online community administrators, and interested highlight structures of communication activity across the individuals to inform their decisions about practical real-world organizational chart of the corporation. Others have related problems. For this to become a reality, tools and strategies must email usage and network characteristics to individual be developed that support the complex process of making sense productivity [12, 13]. Researchers can now move beyond email of the many networks that are created by social media tools. and the very real concerns about research use of private communication to take full advantage of the network data In this paper we provide a framework for characterizing the many types of enterprise social media networks. Understanding created as a byproduct of the use of other more explicitly public enterprise social media tools such as wikis, forums, these networks is vital in educating future network analysts and in designing tools that support their analysis and visualization. blogs, microblogs, and status updates. We next discuss methods of analyzing and using that data in Although literature on enterprise social media networks is the goal of contributing to the development of additional relatively limited, there has been a surge of research that network metrics, analytical tools, and theories that take full examines social networks based on the use of social media advantage of the many novel social media data sources tools in non-enterprise settings. A series of papers have available. We conclude with a review of concerns about the documented the ways contributors to social media repositories potential misuse of these types of data. (e.g., Usenet) have distinct patterns of contribution and connection to other contributors [3-5]. These patterns are III. TYPES OF ENTERPRISE SOCIAL MEDIA NETWORKS evidence of specialization of behavior in these social spaces. The use of social media generates a number of networks. A separate line of research has focused on providing tools In most organizations, people adopt a patchwork quilt of social that help make sense of social network data through media tools. The use of each tool may generate one or more visualizations. Some tools such as NodeXL automatically social networks of interest. Public and private email extract social media networks and allow users to perform a set discussions, message boards, "friends", "buddies", and of core operations that measure and map the resulting dataset "contacts", wikis, blogs, and file and photos stores, along with [1]. Other tools developed by Viegas and colleagues use non- comments and readership records, (among others) all generate graph-based visualizations to make sense of social media data graph structures that can be extracted and analyzed through the such as email [14] and wikis [15]. core methods and measures of social network analysis. The resulting social media networks can vary in important ways. In Eagle and Macy have been analyzing the “call graph” this section we outline a few important dimensions on which created when people use their telephones and mobile phones to these networks vary including: unimodal/multimodal, call one another. Their research on a data set containing all the direct/indirect, symmetry, and weighted/unweighted. phone calls in the United Kingdom for a six-week period highlights different patterns of calling. They find that A. Network Properties Related to Nodes (i.e., Vertices) economically prosperous regions display higher diversity of Unimodal networks link only one type of entity (i.e., nodes) connections than economically marginal regions. together. In standard social network analysis all nodes represent people. However, unimodal networks may connect A growing area of research involves the application of objects to objects (e.g., wiki pages), collections to collections mobile or location based sensors to capture evidence of social (e.g., playlists), groups to groups (e.g., corporations), terms to connections and, by extension, social networks. Sandy terms, locations to locations, activities to activities, etc. The Pentland and the SenseNetworks company are building tools links of unimodal networks may be based on direct associations that use cell phone towers’ ability to collect data from cell (e.g., wiki pages linked together based on hypertext) or indirect phones to map the locations of groups of people over time. associations (e.g., wiki pages linked together based on number The resulting patterns are used to group people into “tribes” of co-editors). A number of standard network metrics such as based on common, overlapping habits. Even if two people have degree, betweenness centrality, eigenvector centrality, never met, their common use of certain kinds of spaces and closeness centrality, and measures of network density can be transit systems soon build a link based on their shared visits to used to characterize these networks. the same kinds of restaurants, theaters, office buildings, and highways or rail lines. These connections are examples of what Multimodal networks are composed of mixtures of entities. Counts and Smith call “hyperties,” or links that are created For example, people, documents, and companies might all co- between people when machines find associations between exist in the same network graph. A common type of people based on common locations or activities even if people multimodal network is a bimodal network that connects people
  • 3. to a set of similar objects. An example of a bimodal network is Many of the standard network metrics do not apply to shown in Figure 1, where people are linked to wiki pages they multimodal networks (e.g., betweenness centrality), although have edited. some apply if appropriately interpreted (e.g., degree) and other, more specialized, metrics have been devised for common types of multimodal networks such as bimodal networks. Multimodal networks can be reduced to uni-modal networks. For example, "people to document to people" networks can be transformed into "people to people" networks and/or "document to document" networks. An example of a network transformed from a bi-modal to a single mode is shown in Figures 1 and 2. Figure 2 shows the person to person network based on wiki page co-edits. A complementary graph could be created that connects wiki pages to wiki pages based on the number of people who have edited both pages. More generally, this approach can be used to relate objects of all types (e.g., books, photos, audio recordings) based on social network ties rather than content indexes or keyword similarity. Such networks are the raw material for recommender systems: queries that generate the results of "people who linked to this document also linked to these documents" or "if you link to this document, you may want to link to these people". B. Network Properties Related to Ties (i.e., Edges) Networks differ in several ways based on the type of ties that connect nodes to one another, whether the networks are unimodal or multimodal. These distinctions affect the metrics Figure 1. Bimodal Wiki Page and Editor Graph. This bimodal graph shows and maps generated from them, as well as their interpretation. employees (circles) and wiki pages (squares). Edge thickness indicates Although these graph types are described in social network number of edits to a page. Node size is based on degree (i.e., large squares have been edited by many people; large circles have edited many pages). analysis textbooks, they have not been related to social media Nodes with less than 2 degrees have been removed. Thus, employee e_2132 network datasets in any systematic way. has a high degree, but only edits pages that nobody else has edited. In contrast, employee 2105 edits pages that are edited by many people. Direct graphs describe explicit relationships between entities such as social networking site’s ability to friend or follow another person. Indirect graphs describe relationships between entities based on implicit relationships such as the relationship between people based on wiki page co-edits as shown in Figure 2. Another example would be two people connected because they are members of the same group even though they may not know one another. In symmetric graphs, the relationship tie is reciprocal between the two entities that are connected. For example, in many social networking sites (e.g., Facebook) a Friendship relationship is symmetric since if Person A has “friended” Person B then Person B has also “friended” Person A. In contrast, other networks are asymmetric, suggesting that a tie from Person A to Person B is not necessarily reciprocated. For example, Person A may follow Person B on the Twitter micro- blogging service without Person B following Person A. Another example is the reply network created in discussion forums and email lists. Asymmetric graphs are often represented using arrows on the edges of a graph. Figure 2. Unimodal Wiki Page Editor Graph. This unimodal graph shows Weighted graphs allow for the differentiation of edges only people. It is based on a transformed version of the data represented in Figure 1. People who have coedited at least 5 of the same wiki pages are based on some criteria of interest (e.g., number of messages connected with an edge. Thicker edges represent more co-edited pages. Node exchanged or posted). They are often represented visually as radius is based on degree. Nodes are darker if they have a higher betweenness thicker or darker lines connecting nodes as was done in Figures centrality. Note that e_2105 shows up prominently in this graph, but e_2132 1 and 2. All edges in an unweighted graph are the same. An doesn't show up at all, since he only edits pages others have not edited. Thus, example of an unweighted edge would be a friendship tie in a each graph highlights different individuals who may play important and distinct roles, as well as their relationship to each other. social network site.
  • 4. Table I. Social Network Types (for person to person unimodal networks) Direct / Symmetry Edge Examples Indirect Weight Direct Symmetric Unweighted Person A & B are friends or contacts (e.g., social networking friends as in Facebook or LinkedIn) Direct Symmetric Weighted Person A & B are friends and friendship “strength” (i.e., weight) is measured by number of communications. Direct Asymmetric Unweighted Person A follows person B (e.g., Twitter); Person A rated person B Direct Asymmetric Weighted Person A reads or comments on Person B’s content (e.g., blog, forum post). Weight = # of reads or comments. Indirect Symmetric Unweighted Person A & B are both members of a group or network Indirect Symmetric Weighted Person A & B have both edited a document (e.g., wiki page) or visited a location; Weight = # of edits or visits. Indirect Asymmetric Unweighted Person A joined a group before Person B joined the group Indirect Asymmetric Weighted Person A edited a document before Person B edited the same document Table 1 shows examples of the various combinations of IV. ANALYZING ENTERPRISE SOCIAL NETWORKS these graph types using unimodal networks that include only people as the nodes. However, these same properties apply to Making sense of the enormous amount of potential data is a unimodal networks with other entities (e.g., wiki page to wiki central challenge of social network analysis. It is essential that page networks), as well as multimodal networks. analysts identify their primary goals and research questions in order to select appropriate metrics and visualizations. Some of C. Single and Cumulative Networks the most important social network related questions include: So far, we have focused on networks created by a single • What kinds of roles are being performed within an technical platform (e.g., a wiki, a social networking site, a enterprise’s social media repositories? discussion forum). It is often useful to analyze these networks because interpretation of the nodes and edges is fairly • Which individuals play important social roles within straightforward. However, enterprises often use many different an enterprise or organization? technical platforms making it possible to create cumulative • What subgroups exist? Do connections between (i.e., hybrid) networks that describe social ties that are captured organizational divisions exist? Who plays the bridge from usage of multiple technical platforms. For example, a roles that connect otherwise unconnected groups? single network generated from a corporate system with a single login can represent the ties between individuals based on their • How do some people convert their contacts to adopt a wiki co-edits, friendships, discussion forum replies, and new technology or practice more than others? semantic overlap. Networks where ties can indicate multiple • How do the overall structures of an enterprise’s social things (e.g., friendship, co-edits) are called multiplex networks. networks change after a particular event (e.g., a Multiplex networks add significant complexity to company social; a round of new hires or layoffs)? interpretation and analysis as people, threads, blogs, forums, Almost all forms of analysis and research questions will documents, topics and keywords all exist in the same data require the transformation of event data into network data. For space. The abundance of related network data makes possible example, logs of who replied to whose message can be new types of analysis not historically feasible due to the costs transformed into an edge list with the name of a replier to a of collecting data. The increased analytic complexity may be message in one column and the person she is replying to in the justified and offset by the added insight into how people are next column. An edge list is a compact representation of a set connected to others through different media (i.e. wiki, of relationships that in aggregate forms a network. An discussion forum, friends). However, the growing challenge alternative representation of a network is an incidence matrix imposed by managing many networks over multiple time slices of entities with the strength of their tie at the intersection of the highlights the need for better tools to support the workflow and row and column representing people. management of large social media network datasets. With an edge list or matrix representation of a social Existing tools do allow for analysis of these networks, network in hand, the next step is to calculate a set of although a bit of extra work is typically needed. One strategy descriptive network metrics. These may include the count of for dealing with cumulative networks is to collapse several nodes, edges, the overall graph density, and for each node its networks into one. A directed graph may be used where in-degree and out-degree (when the network is directed, undirected ties are translated into two directed ties in opposing otherwise just degree), clustering coefficient, betweenness directions. A network tie could be shown between two people centrality, closeness centrality, eigenvector centrality, and tie (a) if they have co-edited a wiki page AND are friends, (b) if strength. Not all of these measures can be applied to every type they have co-edited a wiki page OR are friends, or (c) the of graph. In particular, multimodal graphs require careful weight of the tie could depend on the number of different interpretation of each term. For example, in Figure 1, the networks that connect two individuals. Another approach to degree for people indicates the number of unique wiki pages visually displaying cumulative networks is to show the ties they have edited, whereas the degree of each wiki page between people (e.g., forum replies) and change the node indicates the number of unique authors of that page. A metric attributes (e.g., size) based on involvement in another medium such as betweenness centrality does not have a clear definition (e.g., edits to the wiki). A final approach is to create in such a network either, since it applies to uni-modal complementary graphs using similar visual properties and networks. labels to help compare them (as was done in Figures 1 and 2).
  • 5. Table II. Social Roles and Social Metrics Answer Person Question Person Discussion or Originator Influencer Comment Person Degree (In-Degree + Out-Degree) High Low High High Medium Reply In-Degree Low Low High High High Reply Out-Degree High Low High Low to Medium Low Read In-Degree High Low High High High Social Metrics Read Out-Degree Medium Low High Low to medium Low Betweenness Centrality High Low High High High Neighbors’ Mean Degree Low High High High High Closeness Centrality Low Low Low High High Eigenvector Centrality High Low High High High Clustering Coefficient Low Low High Low to medium Low Threads Started Low Low Low High Low Messages Posted High Low High High Low Reply Messages High Low High Low to medium Low Days Active High Low High High Low to medium Once a set of social media networks have been constructed people. Table III shows an example of an aggregate Discussion and social network measurements have been calculated, the Person score that can be constructed from six ratios. Each of resulting dataset can be used for many applications. For the terms is a mixture of social metrics, designed to range from example, they can be used to create reports about community 0 to 1 with larger values indicating a higher probability of health, comparisons of subgroups, and identification of performing the social role of discussion person. important individuals, as well as in applications that rank, sort, Table III. Discussion Person Metrics compare, and search for content and experts. Term Value Explanation Sorting and visualizing social media datasets based on Verbosity 1– (Thread count/total posts) More messages per thread network attributes and metrics helps highlight important is better documents and individuals. Users and their documents, Initiation 1– (initiated threads/total Ego avoids initiating threads) threads messages, comments, and posts have highly variable network Outgoing Outdegree/degree Ego replies to many properties. For example, some people have a high degree in Attractiveness Indegree/(count of replying Many others reply to Ego their friends graph while others are sparsely connected. Some authors) people are connected to others who are well connected to each Connectedness 1–1/mean(neighbors’ degree) Ego’s alters have high degree, are highly other creating a clique, while others are connected to people connected that are relatively unconnected. Mapping network metrics onto Activity (days with a reply post) / Ego’s participation across visual properties as we have done in Figures 1 and 2 allows (days since last –first visit) possible days analysts to quickly identify important people, which can then be explored further by looking at their content. As indicated in Table II, combinations of these metrics can While social network analysis and visualization is largely a be used to identify other social roles as well. Data from other manual process due to its complexity, common patterns can be networks (e.g., wikis, social networking sites, semantic terms) automatically detected to help provide insights into common could also be used to augment these social roles or help social practices and roles. New conglomerations of network identify roles that span multiple networks. While we have analysis metrics can help explain many social phenomena focused on identifying social roles, similar techniques could be including group formation, group cohesion, social roles, used to develop aggregate metrics for a host of other variables personal influence, and community health. Currently, few such of interest such as community health, group cohesion, aggregate social metrics have been rigorously developed boundary spanners, etc. Comparing these values across despite increased interest in them from corporations and communities, groups, and individuals can help managers, community and organizational analysts. Here we briefly analysts, and individuals know how well they are performing describe how specific social metrics map to aggregate social compared to others. It can also help managers know when roles (i.e., types of participants). interventions or education may help improve performance, or help identify successful individuals and groups so best Table II shows various social metrics that describe each practices can be identified and shared. Tracking metrics over node’s activity and network position. Five different social roles time can help managers measure the effect of interventions. are described in terms of these social metrics. Each social role can be captured in a composite value that takes various metrics V. DISCUSSION & CONCLUSIONS into consideration. Composite values must select the most Social media tools provide a wealth of data that can be important social metrics, scale them appropriately, and weigh transformed into insights about the structure and dynamics of each individual factor. an enterprise or organization. These insights can be generated An example formula can provide a basic scoring for through manual analysis and visualization techniques or "discussion people", a type of contributor who contributes captured, albeit imperfectly, through aggregate social metrics. heavily to conversations that are often started by others. These Managers and analysts can use these metrics to better participants are often highly connected to other discussion understand organizational dynamics, allowing them to better
  • 6. measure the effects of interventions and events. These metrics ACKNOWLEDGMENTS can also be fed into technical systems like search and sorting We appreciate the assistance of Telligent Systems, the and social systems like organizational health monitoring and NodeXL development Team, and the students of social media human resource management. We imagine a future, for analysis classes at the University of Maryland. example, in which the rate of integration of new employees into an organization’s social media practices is measured REFERENCES precisely and reported to all users of a media platform. [1] Smith, M., Shneiderman, B., Milic-Frayling, N., Rodrigues, E.M., These data, over time, will give employees and managers Barash, V., Dunne, C., Capone, T., Perer, A., Gleave, E. 2009. Analyzing Social (Media) Network Data with NodeXL. In Proceedings better insight into the empirical patterns of activity and of Communities and Technologies (C&T) (University Park, connection within an organization. New paths to prominence Pennsylvania, June 25 – 27, 2009). ACM, New York, NY. will emerge as a result, as new kinds of skills attract more [2] Perer, A. and Shneiderman, B. 2008. Integrating statistics and attention in enterprise ranking systems. These measures will visualization: case studies of gaining clarity during exploratory data make clear the variation between groups in terms of their analysis. In Proceeding of the Twenty-Sixth Annual SIGCHI Conference cohesion and solidarity, help identify individuals who can on Human Factors in Computing Systems (Florence, Italy, April 05 - 10, 2008). CHI '08. ACM, New York, NY, 265-274. DOI= bridge organizational silos, and bring to light informal http://doi.acm.org/10.1145/1357054.1357101 networks and communities of practice. [3] Welser, H., Gleave, E., & Smith, M. Visualizing the Signatures of Social As more organizations and enterprises adopt social media Roles in Online Discussion Groups. J. Soc. Struct. 8, 2, 2007. platforms, collections of multiple organization scale data sets [4] Barash, V., Smith, M., Getoor, L., and Welser, H. T. Distinguishing Knowledge vs. Social Capital in Social Media with Roles and Context. will allow for a quantification of the variation among different 2009. ICWSM. Association for the Advancement of Artificial social media repositories. This will help new managers and Intelligence. employees benchmark their own social media usage against [5] Himelboim, I., Gleave, E., Smith, M. Discussion catalysts in online others to identify weaknesses and strengths. Developing a core political discussions: Content importers and conversation starters. set of standard social metrics that are widely used to analyze a Journal of Computer Mediated Communication [Forthcoming]. variety of networks would be invaluable in furthering our [6] Burt, R. Structural Holes: The Social Structure of Competition, Harvard understanding of social phenomena. University Press, 1995. [7] Cross, R., Parker, A. The Hidden Power of Social Networks: Despite the potential benefits of analyzing social media Understanding How Work Really Gets Done in Organizations, Harvard networks, there are many potential problems with using them Business School Press, 2004. as a basis for mapping actual social relationships. These data [8] Cross, R. and Thomas, R. J. Driving Results Through Social Networks: confound the use of tools with social relationships. An How Top Organizations Leverage Networks for Performance and Growth, Jossey-Bass, 2009. employee may have a number of friends but if he doesn’t like using the social networking site this fact is less apparent to [9] Kilduff, M. and Tsai, W. Social Networks and Organizations, Sage Publications Ltd, 2003. others. If decisions are made based on social network structures [10] Scott, J. P. Social Network Analysis: A Handbook, Sage Publications (e.g., deciding who to lay-off) then people will have perverse Ltd, 2000. incentives to use these tools - i.e., trying to friend as many [11] Perer, A. and Smith, M. A. 2006. Contrasting portraits of email people as possible without regard to building actual friendship practices: visual approaches to reflection and analysis. In Proceedings of ties. These problems will diminish as more and more work is the Working Conference on Advanced Visual interfaces (Venezia, Italy, performed via these tools, but they will surely persist. May 23 - 26, 2006). AVI '06. ACM, New York, NY, 389-395. DOI= http://doi.acm.org/10.1145/1133265.1133346 As new social media applications are created, particularly [12] Bulkley, N. & Van Alstyne, M.W. An Empirical Analysis of Strategies as mobile social software applications and devices become and Efficiencies in Social Networks. (February 2006). MIT Sloan mainstream, new social network datasets become practical to Research Paper No. 4682-08. Available at SSRN: construct. Social network services like Facebook made direct, http://ssrn.com/abstract=887406 symmetrical relationship networks very popular. The recent [13] Aral, S., Brynjolfsson, E. and Van Alstyne, M. W. Information, Technology and Information Worker Productivity. MIT Sloan Research rise of Twitter has focused attention on the creation of direct, Paper. Available at SSRN: http://ssrn.com/abstract=942310 asymmetric networks. As location aware devices and other [14] Viégas, F. B., Golder, S., and Donath, J. 2006. Visualizing email sensors become common on mobile devices, new forms of ties content: portraying relationships from conversational histories. In can be detected and aggregated into new forms of social Proceedings of the SIGCHI Conference on Human Factors in networks [16]. Systems like SpotMe, nTag, and SenseNet Computing Systems (Montréal, Québec, Canada, April 22 - 27, 2006). illustrate the ways emerging mobile technologies are creating R. Grinter, T. Rodden, P. Aoki, E. Cutrell, R. Jeffries, and G. Olson, Eds. CHI '06. ACM, New York, NY, 979-988. DOI= new ways to detect associations between people and places. http://doi.acm.org/10.1145/1124772.1124919 We hope this paper will help characterize these new networks [15] Viégas, F. B., Wattenberg, M., and Dave, K. 2004. Studying cooperation and provide a general strategy for turning the various types of and conflict between authors with history flow visualizations. In network data into useful social metrics. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Vienna, Austria, April 24 - 29, 2004). CHI '04. More enterprises will adopt social media platforms, making ACM, New York, NY, 575-582. DOI= the analysis of network datasets a mainstream element of many http://doi.acm.org/10.1145/985692.985765 organizations' operations. It is our hope that enterprises will [16] Counts, S., Smith, M. Where We Were: Communities for Sharing take advantage of these networks to illuminate the internal Space-Time Trails. Proceedings of the 15th annual ACM international structures and dynamics of organizations in novel and useful symposium on Advances in geographic information systems (2007). ways and that researchers will support them in these efforts.