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ContentsArticlesservise 1 Social network 1Servise 12 Social networking service 12 Facebook 27 Twitter 48 Skype 67References Article Sources and Contributors 85 Image Sources, Licenses and Contributors 86Article Licenses License 87
1 serviseSocial networkA social network is a social structure made up of individuals (or organizations) called "nodes", which are tied(connected) by one or more specific types of interdependency, such as friendship, kinship, common interest,financial exchange, dislike, sexual relationships, or relationships of beliefs, knowledge or prestige.Social network analysis (SNA) views social relationships in terms of network theory consisting of nodes and ties(also called edges, links, or connections). Nodes are the individual actors within the networks, and ties are therelationships between the actors. The resulting graph-based structures are often very complex. There can be manykinds of ties between the nodes. Research in a number of academic fields has shown that social networks operate onmany levels, from families up to the level of nations, and play a critical role in determining the way problems aresolved, organizations are run, and the degree to which individuals succeed in achieving their goals.In its simplest form, a social network is a map of specified ties, such as friendship, between the nodes being studied.The nodes to which an individual is thus connected are the social contacts of that individual. The network can alsobe used to measure social capital – the value that an individual gets from the social network. These concepts areoften displayed in a social network diagram, where nodes are the points and ties are the lines.
Social network 2 Social network analysis Social network analysis (related to network theory) has emerged as a key technique in modern sociology. It has also gained a significant following in anthropology, biology, communication studies, economics, geography, information science, organizational studies, social psychology, and sociolinguistics, and has become a popular topic of speculation and study. People have used the idea of "social network" loosely for over a century to connote complex sets of relationships between members of social systems at all scales, from interpersonal to international. In 1954, J. A. Barnes started using the term systematically to denote patterns of ties, encompassing concepts traditionally used by the public and those used by social scientists: bounded groups (e.g., tribes, families) and social categories (e.g., gender, ethnicity). Scholars such as S.D. Berkowitz, Stephen Borgatti, Ronald Burt, Kathleen An example of a social network diagram. The node with the highest betweenness Carley, Martin Everett, Katherine Faust, centrality is marked in yellow. Linton Freeman, Mark Granovetter, David Knoke, David Krackhardt, Peter Marsden, Nicholas Mullins, Anatol Rapoport, Stanley Wasserman, Barry Wellman, Douglas R. White, and Harrison White expanded the use of systematic social network analysis. Social network analysis has now moved from being a suggestive metaphor to an analytic approach to a paradigm, with its own theoretical statements, methods, social network analysis software, and researchers. Analysts reason from whole to part; from structure to relation to individual; from behavior to attitude. They typically either study whole networks (also known as complete networks), all of the ties containing specified relations in a defined population, or personal networks (also known as egocentric networks), the ties that specified people have, such as their "personal communities". In the latter case, the ties are said to go from egos, who are the focal actors who are being analyzed, to their alters. The distinction between whole/complete networks and personal/egocentric networks has depended largely on how analysts were able to gather data. That is, for groups such as companies, schools, or membership societies, the analyst was expected to have complete information about who was in the network, all participants being both potential egos and alters. Personal/egocentric studies were typically conducted when identities of egos were known, but not their alters. These studies rely on the egos to provide information about the identities of alters and there is no expectation that the various egos or sets of alters will be tied to each other. A snowball network refers to the idea that the alters identified in an egocentric survey then become egos themselves and are able in turn to nominate additional alters. While there are severe logistic limits to conducting snowball network studies, a method for examining hybrid networks has recently been developed in which egos in complete networks can nominate alters otherwise not listed who are then available for all subsequent egos to see. The hybrid network may be valuable for examining whole/complete networks that are expected to include important players beyond those who are formally identified. For example, employees of a company often work with non-company consultants who may be part of a network that cannot fully be defined prior to data collection.
Social network 3 Several analytic tendencies distinguish social network analysis: There is no assumption that groups are the building blocks of society: the approach is open to studying less-bounded social systems, from nonlocal communities to links among websites. Rather than treating individuals (persons, organizations, states) as discrete units of analysis, it focuses on how the structure of ties affects individuals and their relationships. In contrast to analyses that assume that socialization into norms determines behavior, network analysis looks to see the extent to which the structure and composition of ties affect norms. The shape of a social network helps determine a networks usefulness to its individuals. Smaller, tighter networks can be less useful to their members than networks with lots of loose connections (weak ties) to individuals outside the main network. More open networks, with many weak ties and social connections, are more likely to introduce new ideas and opportunities to their members than closed networks with many redundant ties. In other words, a group of friends who only do things with each other already share the same knowledge and opportunities. A group of individuals with connections to other social worlds is likely to have access to a wider range of information. It is better for individual success to have connections to a variety of networks rather than many connections within a single network. Similarly, individuals can exercise influence or act as brokers within their social networks by bridging two networks that are not directly linked (called filling structural holes). The power of social network analysis stems from its difference from traditional social scientific studies, which assume that it is the attributes of individual actors—whether they are friendly or unfriendly, smart or dumb, etc.—that matter. Social network analysis produces an alternate view, where the attributes of individuals are less important than their relationships and ties with other actors within the network. This approach has turned out to be useful for explaining many real-world phenomena, but leaves less room for individual agency, the ability for individuals to influence their success, because so much of it rests within the structure of their network. Social networks have also been used to examine how organizations interact with each other, characterizing the many informal connections that link executives together, as well as associations and connections between individual employees at different organizations. For example, power within organizations often comes more from the degree to which an individual within a network is at the center of many relationships than actual job title. Social networks also play a key role in hiring, in business success, and in job performance. Networks provide ways for companies to gather information, deter competition, and collude in setting prices or policies. History of social network analysis A summary of the progress of social networks and social network analysis has been written by Linton Freeman. Precursors of social networks in the late 1800s include Émile Durkheim and Ferdinand Tönnies. Tönnies argued that social groups can exist as personal and direct social ties that either link individuals who share values and belief (gemeinschaft) or impersonal, formal, and instrumental social links (gesellschaft). Durkheim gave a non-individualistic explanation of social facts arguing that social phenomena arise when interacting individuals constitute a reality that can no longer be accounted for in terms of the properties of individual actors. He distinguished between a traditional society – "mechanical solidarity" – which prevails if individual differences are minimized, and the modern society – "organic solidarity" – that develops out of cooperation between differentiated individuals with independent roles. Georg Simmel, writing at the turn of the twentieth century, was the first scholar to think directly in social network terms. His essays pointed to the nature of network size on interaction and to the likelihood of interaction in ramified, loosely-knit networks rather than groups (Simmel, 1908/1971). After a hiatus in the first decades of the twentieth century, three main traditions in social networks appeared. In the 1930s, J.L. Moreno pioneered the systematic recording and analysis of social interaction in small groups, especially classrooms and work groups (sociometry), while a Harvard group led by W. Lloyd Warner and Elton Mayo explored
Social network 4 interpersonal relations at work. In 1940, A.R. Radcliffe-Browns presidential address to British anthropologists urged the systematic study of networks. However, it took about 15 years before this call was followed-up systematically. Social network analysis developed with the kinship studies of Elizabeth Bott in England in the 1950s and the 1950s–1960s urbanization studies of the University of Manchester group of anthropologists (centered around Max Gluckman and later J. Clyde Mitchell) investigating community networks in southern Africa, India and the United Kingdom. Concomitantly, British anthropologist S.F. Nadel codified a theory of social structure that was influential in later network analysis. In the 1960s-1970s, a growing number of scholars worked to combine the different tracks and traditions. One group was centered around Harrison White and his students at the Harvard University Department of Social Relations: Ivan Chase, Bonnie Erickson, Harriet Friedmann, Mark Granovetter, Nancy Howell, Joel Levine, Nicholas Mullins, John Padgett, Michael Schwartz and Barry Wellman. Also independently active in the Harvard Social Relations department at the time were Charles Tilly, who focused on networks in political and community sociology and social movements, and Stanley Milgram, who developed the "six degrees of separation" thesis. Mark Granovetter and Barry Wellman are among the former students of White who have elaborated and popularized social network analysis. Significant independent work was also done by scholars elsewhere: University of California Irvine social scientists interested in mathematical applications, centered around Linton Freeman, including John Boyd, Susan Freeman, Kathryn Faust, A. Kimball Romney and Douglas White; quantitative analysts at the University of Chicago, including Joseph Galaskiewicz, Wendy Griswold, Edward Laumann, Peter Marsden, Martina Morris, and John Padgett; and communication scholars at Michigan State University, including Nan Lin and Everett Rogers. A substantively-oriented University of Toronto sociology group developed in the 1970s, centered on former students of Harrison White: S.D. Berkowitz, Harriet Friedmann, Nancy Leslie Howard, Nancy Howell, Lorne Tepperman and Barry Wellman, and also including noted modeler and game theorist Anatol Rapoport.In terms of theory, it critiqued methodological individualism and group-based analyses, arguing that seeing the world as social networks offered more analytic leverage. Research Social network analysis has been used in epidemiology to help understand how patterns of human contact aid or inhibit the spread of diseases such as HIV in a population. The evolution of social networks can sometimes be modeled by the use of agent based models, providing insight into the interplay between communication rules, rumor spreading and social structure. SNA may also be an effective tool for mass surveillance – for example the Total Information Awareness program was doing in-depth research on strategies to analyze social networks to determine whether or not U.S. citizens were political threats. Diffusion of innovations theory explores social networks and their role in influencing the spread of new ideas and practices. Change agents and opinion leaders often play major roles in spurring the adoption of innovations, although factors inherent to the innovations also play a role. Robin Dunbar has suggested that the typical size of an egocentric network is constrained to about 150 members due to possible limits in the capacity of the human communication channel. The rule arises from cross-cultural studies in sociology and especially anthropology of the maximum size of a village (in modern parlance most reasonably understood as an ecovillage). It is theorized in evolutionary psychology that the number may be some kind of limit of average human ability to recognize members and track emotional facts about all members of a group. However, it may be due to economics and the need to track "free riders", as it may be easier in larger groups to take advantage of the benefits of living in a community without contributing to those benefits.
Social network 5 Mark Granovetter found in one study that more numerous weak ties can be important in seeking information and innovation. Cliques have a tendency to have more homogeneous opinions as well as share many common traits. This homophilic tendency was the reason for the members of the cliques to be attracted together in the first place. However, being similar, each member of the clique would also know more or less what the other members knew. To find new information or insights, members of the clique will have to look beyond the clique to its other friends and acquaintances. This is what Granovetter called "the strength of weak ties". Guanxi (关系）is a central concept in Chinese society (and other East Asian cultures) that can be summarized as the use of personal influence. The word is usually translated as "relation," "connection" or "tie" and is used in as broad a variety of contexts as are its English counterparts. However, in the context of interpersonal relations, Guanxi (关系）is loosely analogous to "clout" or "pull" in the West. Guanxi can be studied from a social network approach. The small world phenomenon is the hypothesis that the chain of social acquaintances required to connect one arbitrary person to another arbitrary person anywhere in the world is generally short. The concept gave rise to the famous phrase six degrees of separation after a 1967 small world experiment by psychologist Stanley Milgram. In Milgrams experiment, a sample of US individuals were asked to reach a particular target person by passing a message along a chain of acquaintances. The average length of successful chains turned out to be about five intermediaries or six separation steps (the majority of chains in that study actually failed to complete). The methods (and ethics as well) of Milgrams experiment were later questioned by an American scholar, and some further research to replicate Milgrams findings found that the degrees of connection needed could be higher. Academic researchers continue to explore this phenomenon as Internet-based communication technology has supplemented the phone and postal systems available during the times of Milgram. A recent electronic small world experiment at Columbia University found that about five to seven degrees of separation are sufficient for connecting any two people through e-mail. Collaboration graphs can be used to illustrate good and bad relationships between humans. A positive edge between two nodes denotes a positive relationship (friendship, alliance, dating) and a negative edge between two nodes denotes a negative relationship (hatred, anger). Signed social network graphs can be used to predict the future evolution of the graph. In signed social networks, there is the concept of "balanced" and "unbalanced" cycles. A balanced cycle is defined as a cycle where the product of all the signs are positive. Balanced graphs represent a group of people who are unlikely to change their opinions of the other people in the group. Unbalanced graphs represent a group of people who are very likely to change their opinions of the people in their group. For example, a group of 3 people (A, B, and C) where A and B have a positive relationship, B and C have a positive relationship, but C and A have a negative relationship is an unbalanced cycle. This group is very likely to morph into a balanced cycle, such as one where B only has a good relationship with A, and both A and B have a negative relationship with C. By using the concept of balances and unbalanced cycles, the evolution of signed social network graphs can be predicted. One study has found that happiness tends to be correlated in social networks. When a person is happy, nearby friends have a 25 percent higher chance of being happy themselves. Furthermore, people at the center of a social network tend to become happier in the future than those at the periphery. Clusters of happy and unhappy people were discerned within the studied networks, with a reach of three degrees of separation: a persons happiness was associated with the level of happiness of their friends friends friends. (See also Emotional contagion.) Some researchers have suggested that human social networks may have a genetic basis. Using a sample of twins from the National Longitudinal Study of Adolescent Health, they found that in-degree (the number of times a person is named as a friend), transitivity (the probability that two friends are friends with one another), and betweenness centrality (the number of paths in the network that pass through a given person) are all significantly heritable. Existing models of network formation cannot account for this intrinsic node variation, so the researchers propose an alternative "Attract and Introduce" model that can explain heritability and many other features of human social
Social network 6 networks. Metrics (measures) in social network analysis Betweenness The extent to which a node lies between other nodes in the network. This measure takes into account the connectivity of the nodes neighbors, giving a higher value for nodes which bridge clusters. The measure reflects the number of people who a person is connecting indirectly through their direct links. Bridge An edge is said to be a bridge if deleting it would cause its endpoints to lie in different components of a graph. Centrality This measure gives a rough indication of the social power of a node based on how well they "connect" the network. "Betweenness," "Closeness," and "Degree" are all measures of centrality. Centralization The difference between the number of links for each node divided by maximum possible sum of differences. A centralized network will have many of its links dispersed around one or a few nodes, while a decentralized network is one in which there is little variation between the number of links each node possesses. Closeness The degree an individual is near all other individuals in a network (directly or indirectly). It reflects the ability to access information through the "grapevine" of network members. Thus, closeness is the inverse of the sum of the shortest distances between each individual and every other person in the network. (See also: Proxemics) The shortest path may also be known as the "geodesic distance." Clustering coefficient A measure of the likelihood that two associates of a node are associates themselves. A higher clustering coefficient indicates a greater cliquishness. Cohesion The degree to which actors are connected directly to each other by cohesive bonds. Groups are identified as ‘cliques’ if every individual is directly tied to every other individual, ‘social circles’ if there is less stringency of direct contact, which is imprecise, or as structurally cohesive blocks if precision is wanted. Degree The count of the number of ties to other actors in the network. See also degree (graph theory). (Individual-level) Density The degree a respondents ties know one another/ proportion of ties among an individuals nominees. Network or global-level density is the proportion of ties in a network relative to the total number possible (sparse versus dense networks). Efficient immunization strategy The acquaintance immunization strategy, propose to immunize friends of randomly selected nodes. It is found to be very efficient compared to random immunization. Flow betweenness centrality The degree that a node contributes to sum of maximum flow between all pairs of nodes (not that node). Eigenvector centrality A measure of the importance of a node in a network. It assigns relative scores to all nodes in the network based on the principle that connections to nodes having a high score contribute more to the score of the node
Social network 7 in question. Human interaction Links in social networks are formed through human interactions. Scaling laws in human interaction activity were found by Rybski et al. Influential Spreaders A method to identify influential spreaders is described by Kitsak et al. Local bridge An edge is a local bridge if its endpoints share no common neighbors. Unlike a bridge, a local bridge is contained in a cycle. Path length The distances between pairs of nodes in the network. Average path-length is the average of these distances between all pairs of nodes. Prestige In a directed graph prestige is the term used to describe a nodes centrality. "Degree Prestige," "Proximity Prestige," and "Status Prestige" are measures of Prestige. See also degree (graph theory). Radiality Degree an individual’s network reaches out into the network and provides novel information and influence. Reach The degree any member of a network can reach other members of the network. Second order centrality It assigns relative scores to all nodes in the network based on the observation that important nodes see a random walk (running on the network) "more regularly" than other nodes. Structural cohesion The minimum number of members who, if removed from a group, would disconnect the group. The relation between fragmentation (Structural cohesion) and percolation theory is discussed by Li et al. Structural equivalence Refers to the extent to which nodes have a common set of linkages to other nodes in the system. The nodes don’t need to have any ties to each other to be structurally equivalent. Structural hole Static holes that can be strategically filled by connecting one or more links to link together other points. Linked to ideas of social capital: if you link to two people who are not linked you can control their communication. Network analytic software Network analytic tools are used to represent the nodes (agents) and edges (relationships) in a network, and to analyze the network data. Like other software tools, the data can be saved in external files. Additional information comparing the various data input formats used by network analysis software packages is available at NetWiki. Network analysis tools allow researchers to investigate large networks like the Internet, disease transmission, etc. These tools provide mathematical functions that can be applied to the network model.
Social network 8 Visualization of networks Visual representation of social networks is important to understand the network data and convey the result of the analysis . Many of the analytic software have modules for network visualization. Exploration of the data is done through displaying nodes and ties in various layouts, and attributing colors, size and other advanced properties to nodes. Visual representations of networks may be a powerful method for conveying complex information, but care should be taken in interpreting node and graph properties from visual displays alone, as they may misrepresent structural properties better captured through quantitative analyses. Typical representation of the network data are graphs in network layout (nodes and ties). These are not very easy-to-read and do not allow an intuitive interpretation. Various new methods have been developed in order to display network data in more intuitive format (e.g. Sociomapping). Especially when using social network analysis as a tool for facilitating change, different approaches of participatory network mapping have proven useful. Here participants / interviewers provide network data by actually mapping out the network (with pen and paper or digitally) during the data collection session. One benefit of this approach is that it allows researchers to collect qualitative data and ask clarifying questions while the network data is collected. Examples of network mapping techniques are Net-Map (pen-and-paper based) and VennMaker  (digital). Patents There has been rapid growth in the number of US patent applications that cover new technologies related to social networking. The number of published applications has been growing at about 250% per year over the past five years. There are now over 2000 published applications. Only about 100 of these applications have issued as patents, however, largely due to the multi-year backlog in examination of business method patents. References Number of US social network patent applications   Linton Freeman, The Development of Social Network Analysis. Vancouver: published per year and patents issued per year Empirical Press, 2006.  Wellman, Barry and S.D. Berkowitz, eds., 1988. Social Structures: A Network Approach. Cambridge: Cambridge University Press.  Hansen, William B. and Reese, Eric L. 2009. Network Genie User Manual (https:/ / secure. networkgenie. com/ admin/ documentation/ Network_Genie_Manual. pdf). Greensboro, NC: Tanglewood Research.  Freeman, Linton. 2006. The Development of Social Network Analysis. Vancouver: Empirical Pres, 2006; Wellman, Barry and S.D. Berkowitz, eds., 1988. Social Structures: A Network Approach. Cambridge: Cambridge University Press.  Scott, John. 1991. Social Network Analysis. London: Sage.  Wasserman, Stanley, and Faust, Katherine. 1994. Social Network Analysis: Methods and Applications. Cambridge: Cambridge University Press.  The Development of Social Network Analysis Vancouver: Empirical Press.  A.R. Radcliffe-Brown, "On Social Structure," Journal of the Royal Anthropological Institute: 70 (1940): 1–12.  Nadel, SF. 1957. The Theory of Social Structure. London: Cohen and West.  The Networked Individual: A Profile of Barry Wellman (http:/ / www. semioticon. com/ semiotix/ semiotix14/ sem-14-05. html)  Mullins, Nicholas. Theories and Theory Groups in Contemporary American Sociology. New York: Harper and Row, 1973; Tilly, Charles, ed. An Urban World. Boston: Little Brown, 1974; Mark Granovetter, "Introduction for the French Reader," Sociologica 2 (2007): 1–8; Wellman, Barry. 1988. "Structural Analysis: From Method and Metaphor to Theory and Substance." Pp. 19-61 in Social Structures: A Network Approach, edited by Barry Wellman and S.D. Berkowitz. Cambridge: Cambridge University Press.  Mark Granovetter, "Introduction for the French Reader," Sociologica 2 (2007): 1–8; Wellman, Barry. 1988. "Structural Analysis: From Method and Metaphor to Theory and Substance." Pp. 19-61 in Social Structures: A Network Approach, edited by Barry Wellman and S.D. Berkowitz. Cambridge: Cambridge University Press. (see also Scott, 2000 and Freeman, 2004).  Barry Wellman, Wenhong Chen and Dong Weizhen. “Networking Guanxi." Pp. 221–41 in Social Connections in China: Institutions, Culture and the Changing Nature of Guanxi, edited by Thomas Gold, Douglas Guthrie and David Wank. Cambridge University Press, 2002.
Social network 9  Could It Be A Big World After All? (http:/ / www. judithkleinfeld. com/ ar_bigworld. html): Judith Kleinfeld article.  Six Degrees: The Science of a Connected Age, Duncan Watts.  James H. Fowler and Nicholas A. Christakis. 2008. " Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study. (http:/ / www. bmj. com/ cgi/ content/ full/ 337/ dec04_2/ a2338)" British Medical Journal. December 4, 2008: doi:10.1136/bmj.a2338. Media account for those who cannot retrieve the original: Happiness: It Really is Contagious (http:/ / www. npr. org/ templates/ story/ story. php?storyId=) Retrieved December 5, 2008.  Shishkin, Philip (January 27, 2009). "Genes and the Friends You Make" (http:/ / online. wsj. com/ article/ SB123302040874118079. html). Wall Street Journal. .  Fowler, J. H.; Dawes, CT; Christakis, NA (10 February 2009). "Model of Genetic Variation in Human Social Networks" (http:/ / jhfowler. ucsd. edu/ genes_and_social_networks. pdf) (PDF). Proceedings of the National Academy of Sciences 106 (6): 1720–1724. doi:10.1073/pnas.0806746106. PMC 2644104. PMID 19171900. .  The most comprehensive reference is: Wasserman, Stanley, & Faust, Katherine. (1994). Social Networks Analysis: Methods and Applications. Cambridge: Cambridge University Press. A short, clear basic summary is in Krebs, Valdis. (2000). "The Social Life of Routers." Internet Protocol Journal, 3 (December): 14–25.  Cohesive.blocking (http:/ / intersci. ss. uci. edu/ wiki/ index. php/ Cohesive_blocking) is the R program for computing structural cohesion according to the Moody-White (2003) algorithm. This wiki site provides numerous examples and a tutorial for use with R.  R. Cohen, S. Havlin, D. ben-Avraham (2003). "Efficient immunization strategies for computer networks and populations" (http:/ / havlin. biu. ac. il/ Publications. php?keyword=Efficient+ immunization+ strategies+ for+ computer+ networks+ and+ populations+ + & year=*& match=all). Phys. Rev. Lett 91: 247901. .  D. Rybski, S. V. Buldyrev, S. Havlin, F. Liljeros, H. A. Makse (2009). "Scaling laws of human interaction activity" (http:/ / havlin. biu. ac. il/ Publications. php?keyword=Scaling+ laws+ of+ human+ interaction+ activity+ + & year=*& match=all). PNAS 106: 12640. .  M. Kitsak, L. K. Gallos, S. Havlin, F. Liljeros, L. Muchnik, H. E. Stanley, H.A. Makse (2010). "Identification of influential spreaders in complex networks" (http:/ / havlin. biu. ac. il/ Publications. php?keyword=Identification+ of+ influential+ spreaders+ in+ complex+ networks+ + & year=*& match=all). Nature Physics 6: 888. doi:10.1038/nphys1746. .  Second order centrality: Distributed assessment of nodes criticity in complex networks, Computer Communications, Volume 34, Issue 5, 15 April 2011, Pages 619-628  Moody, James, and Douglas R. White (2003). "Structural Cohesion and Embeddedness: A Hierarchical Concept of Social Groups." American Sociological Review 68(1):103–127. Online (http:/ / www2. asanet. org/ journals/ ASRFeb03MoodyWhite. pdf): (PDF file).  Y. Chen,G. Paul, R. Cohen, S. Havlin, S. P. Borgatti, F. Liljeros, H. E. Stanley (2007). "Percolation theory applied to measures of fragmentation in social networks" (http:/ / havlin. biu. ac. il/ Publications. php?keyword=Percolation+ theory+ applied+ to+ measures+ of+ fragmentation+ in+ social+ networks+ + & year=*& match=all). Phys. Rev. E 75: 046107. .  http:/ / www. cmu. edu/ joss/ content/ articles/ volume1/ Freeman. html  McGrath, Blythe and Krackhardt. 1997. "The effect of spatial arrangement on judgements and errors in interpreting graphs”. Social Networks 19: 223-242.  Bernie Hogan, Juan-Antonio Carrasco and Barry Wellman, "Visualizing Personal Networks: Working with Participant-Aided Sociograms," Field Methods 19 (2), May 2007: 116-144.  http:/ / www. vennmaker. com/ en/  Mark Nowotarski, "Dont Steal My Avatar! Challenges of Social Network Patents, IP Watchdog, January 23, 2011. (http:/ / ipwatchdog. com/ 2011/ 01/ 23/ donât-steal-my-avatar-challenges-of-social-networking-patents/ id=14531/ )  USPTO search on published patent applications mentioning “social network” (http:/ / appft. uspto. gov/ netacgi/ nph-Parser?Sect1=PTO2& Sect2=HITOFF& u=/ netahtml/ PTO/ search-adv. html& r=0& p=1& f=S& l=50& Query=spec/ "social+ network"& d=PG01) Further reading • Barnes, J. A. "Class and Committees in a Norwegian Island Parish", Human Relations 7:39–58 • Berkowitz, Stephen D. 1982. An Introduction to Structural Analysis: The Network Approach to Social Research. Toronto: Butterworth. ISBN 0-409-81362-1 • Brandes, Ulrik, and Thomas Erlebach (Eds.). 2005. Network Analysis: Methodological Foundations (http:// www.springeronline.com/3-540-24979-6/) Berlin, Heidelberg: Springer-Verlag. • Breiger, Ronald L. 2004. "The Analysis of Social Networks." Pp. 505–526 in Handbook of Data Analysis, edited by Melissa Hardy and Alan Bryman. London: Sage Publications. ISBN 0-7619-6652-8 Excerpts in pdf format (http://www.u.arizona.edu/~breiger/NetworkAnalysis.pdf) • Burt, Ronald S. (1992). Structural Holes: The Structure of Competition. Cambridge, MA: Harvard University Press. ISBN 0-674-84372-X • (Italian) Casaleggio, Davide (2008). TU SEI RETE. La Rivoluzione del business, del marketing e della politica attraverso le reti sociali. ISBN 88-901826-5-2
Social network 10 • Carrington, Peter J., John Scott and Stanley Wasserman (Eds.). 2005. Models and Methods in Social Network Analysis. New York: Cambridge University Press. ISBN 978-0-521-80959-7 • Christakis, Nicholas and James H. Fowler "The Spread of Obesity in a Large Social Network Over 32 Years," New England Journal of Medicine 357 (4): 370–379 (26 July 2007) • Reuven Cohen and Shlomo Havlin (2010). Complex Networks: Structure, Robustness and Function (http:// havlin.biu.ac.il/Shlomo Havlin books_com_net.php). Cambridge University Press. • Doreian, Patrick, Vladimir Batagelj, and Anuška Ferligoj. (2005). Generalized Blockmodeling. Cambridge: Cambridge University Press. ISBN 0-521-84085-6 • Freeman, Linton C. (2004) The Development of Social Network Analysis: A Study in the Sociology of Science. Vancouver: Empirical Press. ISBN 1-59457-714-5 • Hill, R. and Dunbar, R. 2002. "Social Network Size in Humans." (http://www.dur.ac.uk/r.a.hill/Hill and Dunbar 2003.pdf) Human Nature, Vol. 14, No. 1, pp. 53–72. • Jackson, Matthew O. (2003). "A Strategic Model of Social and Economic Networks". Journal of Economic Theory 71: 44–74. doi:10.1006/jeth.1996.0108. pdf (http://merlin.fae.ua.es/fvega/CourseNetworks-Alicante/ Artículos del curso/Jackson-Wolinsky-JET.pdf) • Huisman, M. and Van Duijn, M. A. J. (2005). Software for Social Network Analysis. In P J. Carrington, J. Scott, & S. Wasserman (Editors), Models and Methods in Social Network Analysis (pp. 270–316). New York: Cambridge University Press. ISBN 978-0-521-80959-7 • Krebs, Valdis (2006) Social Network Analysis, A Brief Introduction. (Includes a list of recent SNA applications Web Reference (http://www.orgnet.com/sna.html).) • Ligon, Ethan; Schechter, Laura, "The Value of Social Networks in rural Paraguay" (http://are.berkeley.edu/ seminars/network value.pdf), University of California, Berkeley, Seminar, March 25, 2009, Department of Agricultural & Resource Economics, College of Natural Resources, University of California, Berkeley • Lima, Francisco W. S., Hadzibeganovic, Tarik, and Dietrich Stauffer (2009). Evolution of ethnocentrism on undirected and directed Barabási-Albert networks. Physica A, 388(24), 4999–5004. • Lin, Nan, Ronald S. Burt and Karen Cook, eds. (2001). Social Capital: Theory and Research. New York: Aldine de Gruyter. ISBN 0-202-30643-7 • Mullins, Nicholas. 1973. Theories and Theory Groups in Contemporary American Sociology. New York: Harper and Row. ISBN 0-06-044649-8 • Müller-Prothmann, Tobias (2006): Leveraging Knowledge Communication for Innovation. Framework, Methods and Applications of Social Network Analysis in Research and Development, Frankfurt a. M. et al.: Peter Lang, ISBN 0-8204-9889-0. • Manski, Charles F. (2000). "Economic Analysis of Social Interactions". Journal of Economic Perspectives 14 (3): 115–36. doi:10.1257/jep.14.3.115. (http://links.jstor.org/sici?sici=0895-3309(200022)14:3<115:EAOSI>2.0. CO;2-I&size=LARGE&origin=JSTOR-enlargePage) via JSTOR • Moody, James, and Douglas R. White (2003). "Structural Cohesion and Embeddedness: A Hierarchical Concept of Social Groups." American Sociological Review 68(1):103–127. (http://www2.asanet.org/journals/ ASRFeb03MoodyWhite.pdf) • Newman, Mark (2003). "The Structure and Function of Complex Networks". SIAM Review 56 (2): 167–256. doi:10.1137/S003614450342480. pdf (http://www.santafe.edu/files/gems/paleofoodwebs/ Newman2003SIAM.pdf) • Nohria, Nitin and Robert Eccles (1992). Networks in Organizations. second ed. Boston: Harvard Business Press. ISBN 0-87584-324-7 • Nooy, Wouter d., A. Mrvar and Vladimir Batagelj. (2005). Exploratory Social Network Analysis with Pajek. Cambridge: Cambridge University Press. ISBN 0-521-84173-9 • Scott, John. (2000). Social Network Analysis: A Handbook. 2nd Ed. Newberry Park, CA: Sage. ISBN 0-7619-6338-3
Social network 11 • Sethi, Arjun. (2008). Valuation of Social Networking (http://fusion.dalmatech.com/~admin24/files/ socialnetworkvaluation.pdf) • Tilly, Charles. (2005). Identities, Boundaries, and Social Ties. Boulder, CO: Paradigm press. ISBN 1-59451-131-4 • Valente, Thomas W. (1995). Network Models of the Diffusion of Innovations. Cresskill, NJ: Hampton Press. ISBN 1-881303-21-7 • Wasserman, Stanley, & Faust, Katherine. (1994). Social Network Analysis: Methods and Applications. Cambridge: Cambridge University Press. ISBN 0-521-38269-6 • Watkins, Susan Cott. (2003). "Social Networks." Pp. 909–910 in Encyclopedia of Population. rev. ed. Edited by Paul George Demeny and Geoffrey McNicoll. New York: Macmillan Reference. ISBN 0-02-865677-6 • Watts, Duncan J. (2003). Small Worlds: The Dynamics of Networks between Order and Randomness. Princeton: Princeton University Press. ISBN 0-691-11704-7 • Watts, Duncan J. (2004). Six Degrees: The Science of a Connected Age. W. W. Norton & Company. ISBN 0-393-32542-3 • Wellman, Barry (1998). Networks in the Global Village: Life in Contemporary Communities. Boulder, CO: Westview Press. ISBN 0-8133-1150-0 • Wellman, Barry. 2001. "Physical Place and Cyber-Place: Changing Portals and the Rise of Networked Individualism." International Journal for Urban and Regional Research 25 (2): 227–52. • Wellman, Barry and Berkowitz, Stephen D. (1988). Social Structures: A Network Approach. Cambridge: Cambridge University Press. ISBN 0-521-24441-2 • Weng, M. (2007). A Multimedia Social-Networking Community for Mobile Devices Interactive Telecommunications Program, Tisch School of the Arts/ New York University • White, Harrison, Scott Boorman and Ronald Breiger. 1976. "Social Structure from Multiple Networks: I Blockmodels of Roles and Positions." American Journal of Sociology 81: 730–80. External links • Introduction to Stochastic Actor-Based Models for Network Dynamics - Snijder et al. (http://stat.gamma.rug. nl/SnijdersSteglichVdBunt2009.pdf) • Social Networking (http://www.dmoz.org/Computers/Internet/On_the_Web/Online_Communities/ Social_Networking/) at the Open Directory Project • The International Network for Social Network Analysis (http://www.insna.org) (INSNA) – professional society of social network analysts, with more than 1,000 members • Center for Computational Analysis of Social and Organizational Systems (CASOS) at Carnegie Mellon (http:// www.casos.cs.cmu.edu) • NetLab at the University of Toronto, studies the intersection of social, communication, information and computing networks (http://www.chass.utoronto.ca/~wellman/netlab/ABOUT/index.html) • Netwiki (http://netwiki.amath.unc.edu/) (wiki page devoted to social networks; maintained at University of North Carolina at Chapel Hill) • Building networks for learning (http://learningforsustainability.net/social_learning/networks.php) – A guide to on-line resources on strengthening social networking. • Program on Networked Governance (http://www.ksg.harvard.edu/netgov) – Program on Networked Governance, Harvard University • The International Workshop on Social Network Analysis and Mining (http://www.snakdd.com) (SNAKDD) - An annual workshop on social network analysis and mining, with participants from computer science, social science, and related disciplines. • Historical Dynamics in a time of Crisis: Late Byzantium, 1204–1453 (a discussion of social network analysis from the point of view of historical studies) (http://www.oeaw.ac.at/byzanz/historicaldynamics.htm)
12 ServiseSocial networking serviceA social networking service is an online service, platform, or site that focuses on building and reflecting of socialnetworks or social relations among people, who, for example, share interests and/or activities. A social networkservice essentially consists of a representation of each user (often a profile), his/her social links, and a variety ofadditional services. Most social network services are web based and provide means for users to interact over theInternet, such as e-mail and instant messaging. Online community services are sometimes considered as a socialnetwork service, though in a broader sense, social network service usually means an individual-centered servicewhereas online community services are group-centered. Social networking sites allow users to share ideas, activities,events, and interests within their individual networks.The main types of social networking services are those which contain category places (such as former school year orclassmates), means to connect with friends (usually with self-description pages) and a recommendation systemlinked to trust. Popular methods now combine many of these, with Facebook and Twitter widely used worldwide,Nexopia (mostly in Canada); Bebo, VKontakte, Hi5, Hyves (mostly in The Netherlands), Draugiem.lv (mostlyin Latvia), StudiVZ (mostly in Germany), iWiW (mostly in Hungary), Tuenti (mostly in Spain), Nasza-Klasa(mostly in Poland), Decayenne, Tagged, XING, Badoo and Skyrock in parts of Europe; Orkut and Hi5 inSouth America and Central America; and Mixi, Multiply, Orkut, Wretch, renren and Cyworld in Asia and thePacific Islands and LinkedIn and Orkut are very popular in India.There have been attempts to standardize these services to avoid the need to duplicate entries of friends and interests(see the FOAF standard and the Open Source Initiative). A 2011 survey found that 47% of American adults used asocial network.HistoryThe potential for computer networking to facilitate new forms of computer-mediated social interaction wassuggested early on. Efforts to support social networks via computer-mediated communication were made in manyearly online services, including Usenet, ARPANET, LISTSERV, and bulletin board services (BBS). Manyprototypical features of social networking sites were also present in online services such as America Online, Prodigy,and CompuServe. Early social networking on the World Wide Web began in the form of generalized onlinecommunities such as Theglobe.com (1995), Geocities (1994) and Tripod.com (1995). Many of these earlycommunities focused on bringing people together to interact with each other through chat rooms, and encouragedusers to share personal information and ideas via personal webpages by providing easy-to-use publishing tools andfree or inexpensive webspace. Some communities - such as Classmates.com - took a different approach by simplyhaving people link to each other via email addresses. In the late 1990s, user profiles became a central feature ofsocial networking sites, allowing users to compile lists of "friends" and search for other users with similar interests.New social networking methods were developed by the end of the 1990s, and many sites began to develop moreadvanced features for users to find and manage friends. This newer generation of social networking sites began toflourish with the emergence of SixDegrees.com in 1997, followed by Makeoutclub in 2000,  Friendster in2002, and soon became part of the Internet mainstream. Friendster was followed by MySpace and LinkedIn ayear later, and finally, Bebo. Attesting to the rapid increase in social networking sites popularity, by 2005, MySpacewas reportedly getting more page views than Google. Facebook, launched in 2004, has since become the largestsocial networking site in the world. Today, it is estimated that there are now over 200 active sites using a wide
Social networking service 13 variety of social networking models. May 2011: Based on TNS research, the global average of who access daily the social networking sites accounts is 46 percent. Social impacts Web based social networking services make it possible to connect people who share interests and activities across political, economic, and geographic borders. Through e-mail and instant messaging, online communities are created where a gift economy and reciprocal altruism are encouraged through cooperation. Information is particularly suited to gift economy, as information is a nonrival good and can be gifted at practically no cost.  Facebook and other social networking tools are increasingly the object of scholarly research. Scholars in many fields have begun to investigate the impact of social networking sites, investigating how such sites may play into issues of identity, privacy, social capital, youth culture, and education. Several websites are beginning to tap into the power of the social networking model for philanthropy. Such models provide a means for connecting otherwise fragmented industries and small organizations without the resources to reach a broader audience with interested users. Social networks are providing a different way for individuals to communicate digitally. These communities of hypertexts allow for the sharing of information and ideas, an old concept placed in a digital environment. In 2011, HCL Technologies conducted research which showed that 50% of British employers had banned the use of social networking sites/services during office hours.  Typical structure Basics Social networking sites share some conventional features. Most often, individual users are encouraged to create profiles containing various information about themselves. Users can often upload pictures of themselves to their profiles, post blog entries for others to read, search for other users with similar interests, and compile and share lists of contacts. In addition, user profiles often have a section dedicated to comments from friends and other users. To protect user privacy, social networks usually have controls that allow users to choose who can view their profile, contact them, add them to their list of contacts, and so on. In recent years, it has also become common for a wide variety of organizations to create profiles to advertise products and services. Additional features Some social networks have additional features, such as the ability to create groups that share common interests or affiliations, upload or stream live videos, and hold discussions in forums. Geosocial networking co-opts Internet mapping services to organize user participation around geographic features and their attributes. There is also a trend for more interoperability between social networks led by technologies such as OpenID and OpenSocial. Lately, mobile social networking has become popular. In most mobile communities, mobile phone users can now create their own profiles, make friends, participate in chat rooms, create chat rooms, hold private conversations, share photos and videos, and share blogs by using their mobile phone. Some companies provide wireless services which allow their customers to build their own mobile community and brand it, but one of the most popular wireless services for social networking in North America is Facebook Mobile.
Social networking service 14 Emerging trends in social networking As the increase in popularity of social networking is on a constant rise, new uses for the technology are constantly being observed. At the forefront of emerging trends in social networking sites is the concept of "real-time web" and "location based." Real time allows users to contribute content, which is then broadcasted as it is being uploaded - the concept is analogous to live radio and television broadcasts. Twitter set the trend for "real time" services, where users can broadcast to the world what they are doing, or what is on their minds within a 140 character limit. Facebook followed suit with their "Live Feed" where users activities are streamed as soon as it happens. While Twitter focuses on words, Clixtr, another real time service, focuses on group photo sharing where users can update their photo streams with photos while at an event. Facebook, however, remains easily the greatest photo sharing site - Facebook application and photo aggregator Pixable estimates that Facebook will have 100 billion photos by Summer 2011. Companies have begun to merge business technologies and solutions, such as cloud computing, with social networking concepts. Instead of connecting individuals based on social interest, companies are developing interactive communities that connect individuals based off shared business needs or experiences. Many provide specialized networking tools and applications that can be accessed via their websites, such as LinkedIn. Others companies, such as Monster.com, have been steadily developing a more "socialized" feel to their career center sites to harness some of the power of social networking sites. These more business related sites have their own nomenclature for the most part but the most common naming conventions are "Vocational Networking Sites" or "Vocational Media Networks", with the former more closely tied to individual networking relationships based on social networking principles. Foursquare gained popularity as it allowed for users to "check-in" to places that they are frequenting at that moment. Gowalla is another such service which functions in much the same way that Foursquare does, leveraging the GPS in phones to create a location-based user experience. Clixtr, though in the real time space, is also a location based social networking site since events created by users are automatically geotagged, and users can view events occurring nearby through the Clixtr iPhone app. Recently, Yelp announced its entrance into the location based social networking space through check-ins with their mobile app; whether or not this becomes detrimental to Foursquare or Gowalla is yet to be seen as it is still considered a new space in the Internet technology industry. One popular use for this new technology is social networking between businesses. Companies have found that social networking sites such as Facebook and Twitter are great ways to build their brand image. According to Jody Nimetz, author of Marketing Jive, there are five major uses for businesses and social media: to create brand awareness, as an online reputation management tool, for recruiting, to learn about new technologies and competitors, and as a lead generation tool to intercept potential prospects. These companies are able to drive traffic to their own online sites while encouraging their consumers and clients to have discussions on how to improve or change products or services. Social networks and science One other use that is being discussed is the use of social networks in the science communities. Julia Porter Liebeskind et al. have published a study on how new biotechnology firms are using social networking sites to share exchanges in scientific knowledge. They state in their study that by sharing information and knowledge with one another, they are able to "increase both their learning and their flexibility in ways that would not be possible within a self-contained hierarchical organization." Social networking is allowing scientific groups to expand their knowledge base and share ideas, and without these new means of communicating their theories might become "isolated and irrelevant".
Social networking service 15 Social networks and education Social networks are also being used by teachers and students as a communication tool. Because many students are already using a wide-range of social networking sites, teachers have begun to familiarize themselves with this trend and are now using it to their advantage. Teachers and professors are doing everything from creating chat-room forums and groups to extend classroom discussion to posting assignments, tests and quizzes, to assisting with homework outside of the classroom setting. Social networks are also being used to foster teacher-parent communication. These sites make it possible and more convenient for parents to ask questions and voice concerns without having to meet face-to-face. The advent of social networking platforms may also be impacting the way(s) in which learners engage with technology in general. For a number of years, Prenskys (2001) dichotomy of Digital Natives and Digital Immigrants has been considered a relatively accurate representation of the ease with which people of different ages--particularly those born before and after 1980--use technology. Prenskys theory has largely been disproved not least on account of the burgeoning popularity of social networking sites and other metaphors such as White and Le Cornus Visitors and Residents (2011) are gaining greater currency. The use of online social networks by libraries is also an increasingly prevalent and growing tool that is being used to communicate with more potential library users, as well as extending the services provided by individual libraries. Social networks and grassroots organizing Social networks are being used by activists as a means of low-cost grassroots organizing. Extensive use of an array of social networking sites enabled organizers of the 2009 National Equality March to mobilize an estimated 200,000 participants to march on Washington with a cost savings of up to 85% per participant over previous methods. The August 2011 England riots were similarly considered to have escalated and been fuelled by this type of grassroots organization. Social networks and employment A final rise in social network use is being driven by college students using the services to network with professionals for internship and job opportunities. Many studies have been done on the effectiveness of networking online in a college setting, and one notable one is by Phipps Arabie and Yoram Wind published in Advances in Social Network Analysis. Social network hosting service A social network hosting service is a web hosting service that specifically hosts the user creation of web-based social networking services, alongside related applications. Such services are also known as vertical social networks due to the creation of SNSes which cater to specific user interests and niches; like larger, interest-agnostic SNSes, such niche networking services may also possess the ability to create increasingly niche groups of users. An example for this would be Ning. Business model Few social networks currently charge money for membership. In part, this may be because social networking is a relatively new service, and the value of using them has not been firmly established in customers minds. Companies such as MySpace and Facebook sell online advertising on their site. Their business model is based upon large membership count, and charging for membership would be counterproductive. Some believe that the deeper information that the sites have on each user will allow much better targeted advertising than any other site can currently provide. Social networks operate under an autonomous business model, in which a social networks members serve dual roles as both the suppliers and the consumers of content. This is in contrast to a traditional business model, where the
Social networking service 16 suppliers and consumers are distinct agents. Revenue is typically gained in the autonomous business model via advertisements, but subscription-based revenue is possible when membership and content levels are sufficiently high. Issues Privacy Privacy concerns with social networking services have been raised growing concerns amongst users on the dangers of giving out too much personal information and the threat of sexual predators. Users of these services also need to be aware of data theft or viruses. However, large services, such as MySpace and Netlog, often work with law enforcement to try to prevent such incidents. In addition, there is a perceived privacy threat in relation to placing too much personal information in the hands of large corporations or governmental bodies, allowing a profile to be produced on an individuals behavior on which decisions, detrimental to an individual, may be taken. Furthermore, there is an issue over the control of data—information that was altered or removed by the user may in fact be retained and/or passed to third parties. This danger was highlighted when the controversial social networking site Quechup harvested e-mail addresses from users e-mail accounts for use in a spamming operation. In medical and scientific research, asking subjects for information about their behaviors is normally strictly scrutinized by institutional review boards, for example, to ensure that adolescents and their parents have informed consent. It is not clear whether the same rules apply to researchers who collect data from social networking sites. These sites often contain a great deal of data that is hard to obtain via traditional means. Even though the data are public, republishing it in a research paper might be considered invasion of privacy. Privacy on social networking sites can be undermined by many factors. For example, users may disclose personal information, sites may not take adequate steps to protect user privacy, and third parties frequently use information posted on social networks for a variety of purposes. "For the Net generation, social networking sites have become the preferred forum for social interactions, from posturing and role playing to simply sounding off. However, because such forums are relatively easy to access, posted content can be reviewed by anyone with an interest in the users personal information".   Following plans by the UK government to monitor traffic on social networks schemes similar to E-mail jamming have been proposed for networks such as Twitter and Facebook. These would involve "friending" and "following" large numbers of random people to thwart attempts at network analysis. Data mining Through data mining, companies are able to improve their sales and profitability. With this data, companies create customer profiles that contain customer demographics and online behavior. A recent strategy has been the purchase and production of “network analysis software”. This software is able to sort out through the influx of social networking data for any specific company. Facebook has been especially important to marketing strategists. Facebook’s controversial and new “Social Ads” program gives companies access to the millions of profiles in order to tailor their ads to a Facebook user’s own interests and hobbies. However, rather than sell actual user information, Facebook sells tracked “social actions”. That is, they track the websites a user uses outside of Facebook through a program called “Facebook Beacon”.
Social networking service 18 Trolling A common misuse of social networking sites such as Facebook is that it is occasionally used to emotionally abuse individuals. Such actions are often referred to as trolling. It is not rare for confrontations in the real world to be translated online. Trolling can occur in many different forms, such as (but not limited to) defacement of deceased person(s) tribute pages, name calling, playing online pranks on volatile individuals and controversial comments with the intention to cause anger and cause arguments. Trolling is not to be confused with cyber-bullying. Online bullying Online bullying, also called cyber-bullying, is a relatively common occurrence and it can often result in emotional trauma for the victim. Depending on the networking outlet, up to 39% of users admit to being “cyber-bullied”. Danah Boyd, a researcher of social networks quotes a teenager in her article, Why Youth (Heart) Social Network Sites. The teenager expresses frustration towards networking sites like MySpace because it causes drama and too much emotional stress. There are not many limitations as to what individuals can post when online. Inherently individuals are given the power to post offensive remarks or pictures that could potentially cause a great amount of emotional pain for another individual. Interpersonal communication Interpersonal communication has been a growing issue as more and more people have turned to social networking as a means of communication. "Benniger (1987) describes how mass media has gradually replaced interpersonal communication as a socializing force. Further, social networking sites have become popular sites for youth culture to explore themselves, relationships, and share cultural artifacts". A Privacy Paradox  Many teens and social networking users may be harming their interpersonal communication by using sites such as Facebook and MySpace. Stated by Baroness Greenfield, an Oxford University Neuroscientist, "My fear is that these technologies are infantilizing the brain into the state of small children who are attracted by buzzing noises and bright lights, who have a small attention span and who live for the moment." The convenience which social network sites give users to communicate with one another can also damage their interpersonal communication. Sherry Turkle, the founder and director of the MIT Initiative on Technology and Self, stated, “Networked, we are together, but so lessened are our expectations of each other that we feel utterly alone. And there is the risk that we come to see others as objects to be accessed--and only for the parts we find useful, comforting, or amusing (Turkle 154).” Furthermore, social network sites can create insincere friendships, Turkle also noted, “They nurture friendships on social-networking sites and then wonder if they are among friends. They become confused about companionship (Turkle 17).” Psychological effects of social networking As social networking sites have risen in popularity over the past years, people have been spending an excess amount of time on social networking sites and on the Internet in general. The excessive amount of time that people spend on social networking sites has led researchers to debate the establishment of Internet addiction as an actual clinical disorder. Social networking can also affect the extent to which a person feels lonely. In a Newsweek article, Johannah Cornblatt explains “Social-networking sites like Facebook and MySpace may provide people with a false sense of connection that ultimately increases loneliness in people who feel alone.” John T. Cacioppo, a neuroscientist at the University of Chicago, claims that social networking can foster feelings of sensitivity to disconnection, which can lead to loneliness.
Social networking service 19 Patents There has been rapid growth in the number of US patent applications that cover new technologies related to social networking. The number of published applications has been growing rapidly since 2003. There are now over 3500 published applications. As many as 7000 applications may be currently on file including those that havent been published yet. Only about 400 of these applications have issued as patents, however, largely due to the multi-year backlog in examination of business method patents and the difficulty in getting these patent applications allowed. Number of US social network patent applications It has been reported that social networking patents are important for the published per year and patents issued per year establishment of new start-up companies. It has also been reported, however, that social networking patents inhibit innovation. On June 15, 2010, the United States Patent and Trademark Office awarded Amazon.com a patent for a "Social Networking System" based on its ownership of PlanetAll. The patent describes a Social Networking System as A networked computer system provides various services for assisting users in locating, and establishing contact relationships with, other users. For example, in one embodiment, users can identify other users based on their affiliations with particular schools or other organizations. The system also provides a mechanism for a user to selectively establish contact relationships or connections with other users, and to grant permissions for such other users to view personal information of the user. The system may also include features for enabling users to identify contacts of their respective contacts. In addition, the system may automatically notify users of personal information updates made by their respective contacts. The patent has garnered attention due to its similarity to the popular social networking site Facebook. Investigations Social networking services are increasingly being used in legal and criminal investigations. Information posted on sites such as MySpace and Facebook has been used by police (forensic profiling), probation, and university officials to prosecute users of said sites. In some situations, content posted on MySpace has been used in court. Facebook is increasingly being used by school administrations and law enforcement agencies as a source of evidence against student users. The site, the number one online destination for college students, allows users to create profile pages with personal details. These pages can be viewed by other registered users from the same school which often include resident assistants and campus police who have signed up for the service. One UK police force has sifted pictures from Facebook and arrested some people who had been photographed in a public place holding a weapon such as a knife (having a weapon in a public place is illegal). Application domains Government applications Social networking is more recently being used by various government agencies. Social networking tools serve as a quick and easy way for the government to get the opinion of the public and to keep the public updated on their activity. The Centers for Disease Control demonstrated the importance of vaccinations on the popular childrens site Whyville and the National Oceanic and Atmospheric Administration has a virtual island on Second Life where people can explore underground caves or explore the effects of global warming. Similarly, NASA has taken advantage of a few social networking tools, including Twitter and Flickr. They are using these tools to aid the
Social networking service 20 Review of U.S. Human Space Flight Plans Committee, whose goal it is to ensure that the nation is on a vigorous and sustainable path to achieving its boldest aspirations in space. Business applications The use of social networking services in an enterprise context presents the potential of having a major impact on the world of business and work (Fraser & Dutta 2008). Social networks connect people at low cost; this can be beneficial for entrepreneurs and small businesses looking to expand their contact bases. These networks often act as a customer relationship management tool for companies selling products and services. Companies can also use social networks for advertising in the form of banners and text ads. Since businesses operate globally, social networks can make it easier to keep in touch with contacts around the world. One example of social networking being used for business purposes is LinkedIn.com, which aims to interconnect professionals. LinkedIn has over 100 million users in over 200 countries. Another is the use of physical spaces available to members of a social network such as Hub Culture, an invitation only social network for entrepreneurs, and other business influentials, with Pavilions in major cities such as London, UK. Having a physical presence allows members to network in the real world, as well as the virtual, adding extra business value. Applications for social networking sites have extended toward businesses and brands are creating their own, high functioning sites, a sector known as brand networking. It is the idea a brand can build its consumer relationship by connecting their consumers to the brand image on a platform that provides them relative content, elements of participation, and a ranking or score system. Brand networking is a new way to capitalize on social trends as a marketing tool. Dating applications Many social networks provide an online environment for people to communicate and exchange personal information for dating purposes. Intentions can vary from looking for a one time date, short-term relationships, and long-term relationships. Most of these social networks, just like online dating services, require users to give out certain pieces of information. This usually includes a users age, gender, location, interests, and perhaps a picture. Releasing very personal information is usually discouraged for safety reasons. This allows other users to search or be searched by some sort of criteria, but at the same time people can maintain a degree of anonymity similar to most online dating services. Online dating sites are similar to social networks in the sense that users create profiles to meet and communicate with others, but their activities on such sites are for the sole purpose of finding a person of interest to date. Social networks do not necessarily have to be for dating; many users simply use it for keeping in touch with friends, and colleagues. However, an important difference between social networks and online dating services is the fact that online dating sites usually require a fee, where social networks are free. This difference is one of the reasons the online dating industry is seeing a massive decrease in revenue due to many users opting to use social networking services instead. Many popular online dating services such as Match.com, Yahoo Personals, and eHarmony.com are seeing a decrease in users, where social networks like MySpace and Facebook are experiencing an increase in users. The number of Internet users in the U.S. that visit online dating sites has fallen from a peak of 21% in 2003 to 10% in 2006. Whether its the cost of the services, the variety of users with different intentions, or any other reason, it is undeniable that social networking sites are quickly becoming the new way to find dates online.
Social networking service 21 Educational applications The National School Boards Association reports that almost 60 percent of students who use social networking talk about education topics online and, surprisingly, more than 50 percent talk specifically about schoolwork. Yet the vast majority of school districts have stringent rules against nearly all forms of social networking during the school day — even though students and parents report few problem behaviors online. Social networks focused on supporting relationships between teachers and their students are now used for learning, educator professional development, and content sharing. Ning for teachers, TermWiki, Learn Central, TeachStreet and other sites are being built to foster relationships that include educational blogs, eportfolios, formal and ad hoc communities, as well as communication such as chats, discussion threads, and synchronous forums. These sites also have content sharing and rating features. Social networks are also emerging as online yearbooks, both public and private. One such service is MyYearbook which allows anyone from the general public to register and connect. A new trend emerging are private label yearbooks only accessible by students, parents and teachers of a particular school similar to Facebook beginning within Harvard. Finance applications The use of virtual currency systems inside social networks create new opportunities for global finance. Hub Culture operates a virtual currency Ven used for global transactions among members, product sales and financial trades in commodities and carbon credits.  In May 2010, Carbon pricing contracts were introduced to the weighted basket of currencies and commodities that determine the floating exchange value of Ven. The introduction of carbon to the calculation price of the currency made Ven the first and only currency that is linked to the environment. Medical applications Social networks are beginning to be adopted by healthcare professionals as a means to manage institutional knowledge, disseminate peer to peer knowledge and to highlight individual physicians and institutions. The advantage of using a dedicated medical social networking site is that all the members are screened against the state licensing board list of practitioners. The role of social networks is especially of interest to pharmaceutical companies who spend approximately "32 percent of their marketing dollars" attempting to influence the opinion leaders of social networks. A new trend is emerging with social networks created to help its members with various physical and mental ailments. For people suffering from life altering diseases, PatientsLikeMe offers its members the chance to connect with others dealing with similar issues and research patient data related to their condition. For alcoholics and addicts, SoberCircle gives people in recovery the ability to communicate with one another and strengthen their recovery through the encouragement of others who can relate to their situation. DailyStrength is also a website that offers support groups for a wide array of topics and conditions, including the support topics offered by PatientsLikeMe and SoberCircle. SparkPeople offers community and social networking tools for peer support during weight loss. Social and political applications Social networking sites have recently showed a value in social and political movements. In the Egyptian revolution, Facebook and Twitter both played a pivotal role in keeping people connected to the revolt. Egyptian activist have credited social networking sites with providing a platform for planning protest and sharing news from Tahrir Square in real time. By presenting a platform for thousands of people to instantaneously share videos of mainly events featuring brutality, social networking proves to be a vital tool in revolutions.
Social networking service 22 Open source software There are a number of projects that aim to develop free and open source software to use for social networking services. The projects include Anahita Social Networking Engine, Diaspora, Appleseed Project , OneSocialWeb and StatusNet. These technologies are often referred to as Social engine or Social networking engine software. In the media • In December 2010, Time Magazine named Facebook CEO Mark Zuckerberg as person of the year. • The Social Network - a 2010 drama biographical film about the origin of Facebook. References • Boyd, Danah; Ellison, Nicole (2007). "Social Network Sites: Definition, History, and Scholarship" . Journal of Computer-Mediated Communication 13 (1). • Boyd, Danah (2006). "Friends, Friendsters, and MySpace Top 8: Writing Community Into Being on Social Network Sites" . First Monday 11 (12). • Ellison, Nicole B.; Steinfield, Charles; Lampe, Cliff (2007). "The benefits of Facebook "friends": Exploring the relationship between college students use of online social networks and social capital" . Journal of Computer-Mediated Communication 12 (4). • Fraser, Matthew; Dutta, Soumitra (2008). Throwing Sheep in the Boardroom: How Online Social Networking Will Transform Your Life, Work and World . Wiley. ISBN 978-0470740149. • Mazer, J. P.; Murphy, R. E.; Simonds, C. J. (2007). "Ill See You On "Facebook": The Effects of Computer-Mediated Teacher Self-Disclosure on Student Motivation, Affective Learning, and Classroom Climate"  . Communication Education 56 (1): 1–17. doi:10.1080/03634520601009710. • Prensky, Marc (2001). "Digital Natives, Digital Immigrants" . On the Horizon 9 (5). • White, D.S.; Le Cornu, A. (2011). "Visitors and Residents: A New Typology for Online Engagement" . First Monday 16 (9). Notes  "Nexopia stats on" (http:/ / www. alexa. com/ data/ details/ traffic_details/ nexopia. com). Alexa.com. . Retrieved 2011-03-13.  Bebo (http:/ / www. techcrunch. com/ 2007/ 08/ 20/ windows-live-messaging-coming-to-bebo/ ) - most popular of its kind in UK,(August 2007): TechCrunch website. Retrieved on January 15, 2008.  German Xing Plans Invasion of LinkedIn Turf (http:/ / www. marketingvox. com/ german-xing-plans-invasion-of-linkedin-turf-030727/ ): article from the MarketingVox website.  Elevator Pitch: Why Badoo wants to be the next word in social networking (http:/ / blogs. guardian. co. uk/ digitalcontent/ 2008/ 03/ elevator_pitch_why_badoo_wants. html), Mark Sweney , The Guardian, December 24, 2007 , Accessed March 2008.  Hi5 popular in Europe (http:/ / www. pbs. org/ mediashift/ 2007/ 06/ try_try_againorkut_friendster. html): article from the PBS MediaShift website. Retrieved on January 18, 2008.  "Why Users Love Orkut" (http:/ / usability. about. com/ od/ websiteaudiences/ a/ Orkut. htm) - 55% of users are Brazilian: About.com website. Retrieved on January 15, 2008,  finance.yahoo.com (http:/ / finance. yahoo. com/ family-home/ article/ 112952/ family-that-tweets-wsj?mod=family-kids_parents)  The Network Nation 2 by S. Roxanne Hiltz and Murray Turoff (Addison-Wesley, 1978, 1993)  Cotriss, David (2008-05-29). "Where are they now: TheGlobe.com" (http:/ / www. thestandard. com/ news/ 2008/ 05/ 29/ where-are-they-now-theglobe-com). The Industry Standard. .  Romm-Livermore, C. & Setzekorn, K. (2008). Social Networking Communities and E-Dating Services: Concepts and Implications. IGI Global. p.271  mcmc.indiana.edu (http:/ / jcmc. indiana. edu/ vol13/ issue1/ boyd. ellison. html)  longislandpress.com (http:/ / www. longislandpress. com/ 2010/ 09/ 30/ from-friendster-to-myspace-to-facebook-the-evolution-and-deaths-of-social-networks/ )  bnet.com (http:/ / www. bnet. com/ videos/ gibby-miller-inventing-the-social-network/ 239462)  Knapp, E. (2006). A Parents Guide to Myspace. DayDream Publishers. ISBN 1-4196-4146-8
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