Social computation of emergent networks on user generated content

Knowledge Management Institute Social Computation of Emergent Networks on User-Generated Content GI Workshop on “Web-Science” at Informatik 2010 der 40. Jahrestagung der Gesellschaft für Informatik 2010, 40 Leipzig, Germany Markus Strohmaier Assistant Professor Knowledge Management Institute g g Graz University of Technology, Austria e-mail: markus.strohmaier@tugraz.at web: http://www.kmi.tugraz.at/staff/markus Markus Strohmaier 2010 1

Knowledge Management Institute Social-Computational Systems … is the title of a new National Science Foundation (NSF) Program. ( ) g the genesis of a new class of computational systems, which generate emergent behaviors that arise out of the complex and dynamic interactions among people and computers. Source: National Science Foundation http://www.nsf.gov/pubs/2010/nsf10600/nsf10600.htm p g p 3 observations: • Rise of User Generated Content • 5 out of the top 10 websites in the world have a focus on user-generated-content (Alexa.com 2010) • Rise of Online Social Networks – More than 500 million active Facebook users, 50% log on any given day (Facebook 2010) • Integration of user data and system functionality • User data becomes an integral part of system functions Markus Strohmaier 2010 (Facebook 2010) https://www.facebook.com/press/info.php?statistics 2

Knowledge Management Institute Social Computational Systems Interaction between individuals and computational systems is mediated by the aggregate behavior of y gg g users. Markus Strohmaier 2010 3

Knowledge Management Institute Social Computation p influences system properties (X) X=Findability X=Utility It is through the process of social computation, i.e. the combination of social behavior and algorithmic computation, that system properties and functions emerge. X=Navigability X Navigability X=Relevance X R l Markus Strohmaier 2010 4

Knowledge Management Institute System Properties of Social-Computational Systems • Findability: • the ease at which a document can be found by a user • Utility: U ili • the degree to which a system maximizes usefulness of its functions for users • Navigability: • the th ease at which a user can navigate f t hi h i t from A t B to • Relevance: • the extent to which offered information is considered relevant • Privacy: • the extent to which private information is kept private • Profit: • The extent to which functions can be monetized • … influenced by social computation processes Markus Strohmaier 2010 5

Knowledge Management Institute Agenda 1. Social-Computational S t 1 S i lC t ti l Systems 2. Navigability of Social-Computational Systems 3. Semantics in Social-Computational Systems 4. Social-Computational Systems & the Future Markus Strohmaier 2010 6

Knowledge Management Institute Example: X = Connectivity (of the web graph) Questions: • What is X like? • What causes X? bow-tie architecture of the web [Broder et al 2000] Markus Strohmaier 2010 8

Knowledge Management Institute Example: X = Connectivity (of the web graph) Questions: • What is X like? • What causes X? • How can we bow-tie architecture Social mechanisms, such as improve X? of the web preferential attachment an open issue p [Broder et al 2000] [Barabasi 1999] Markus Strohmaier 2010 9

Knowledge Management Institute Social Computational Systems: What type of questions are we asking? e.g. X = Connectivity of the web graph C ti it f th b h • Description and Classification: • Causality: • What is X like? • Does X cause Y? • What are its properties? • Does X prevent Y? • How can it be categorized? • What causes X? • How can we measure it? • What effect does X have on Y? • Descriptive Process: • Causality - Comparative: • How does X work? • Does X cause more Y than does Z? • What is the process by which X • Is X better at preventing Y than is Z? pp happens? • Does X cause more Y than does Z • How does X evolve? under one condition but not others? • Descriptive Comparative: • Design • How does X differ from Y? • What is an effective way to achieve X? y • Relationship: • How can we improve X? • Are X and Y related? • Do occurences of X correlate with occurences of Y? cf. [Easterbrook 2007 et al.] Markus Strohmaier 2010 Selecting Empirical Methods for Software Engineering Research, Steve Easterbrook, Janice Singer, Margaret-Anne Storey, Daniela Damian, "Selecting Empirical Methods for Software 10 Engineering Research", Guide to Advanced Empirical Software Engineering, 2007

Knowledge Management Institute Attempting a Definition: Social-Computational Systems …refer to systems in which essential system properties and functions (“X”) are influenced by the behavior of users. Thus, certain system properties and functions are not engineered by a single person, but they are emergent, i.e. the result of aggregating information from a large group of usersusers. In this sense, certain system properties and functions of social- computational systems are b i l beyond the direct control of system d h di l f designers. New approaches for designing and shaping social-computational systems are needed. Markus Strohmaier 2010 11

Knowledge Management Institute The Dual Nature of Web-Science Science Engineering What is X like? Improve X? Prevent Y? typically beyond control social computation = social behavior + algorithmic computation emergent social-computational system properties and f functions through aggregation Markus Strohmaier 2010 12

Knowledge Management Institute Social Computational Systems: What type of questions are we asking? • Description and Classification: • Causality: • What is X like? • Does X cause Y? • What are its properties? • Does X prevent Y? • How can it be categorized? • What causes X? • How can we measure it? • What effect does X have on Y? • Descriptive Process: • Causality - Comparative: • How does X work? • Today‘s talk: Y than does Z? Does X cause more • What is the process by which X • X1=Navigability Is X better at preventing Y than is Z? pp happens? • X2=Semantics Y than does Z Semantics Does X cause more • How does X evolve? of User-Generated not others? under one condition but Content • Descriptive Comparative: • How does X differ from Y? • Design • Relationship: • What is an effective way to achieve X? • Are X and Y related? • How can we improve X? • Do occurences of X correlate with occurences of Y? cf. [Easterbrook 2007 et al.] Markus Strohmaier 2010 Selecting Empirical Methods for Software Engineering Research, Steve Easterbrook, Janice Singer, Margaret-Anne Storey, Daniela Damian, "Selecting Empirical Methods for Software 14 Engineering Research", Guide to Advanced Empirical Software Engineering, 2007

Knowledge Management Institute X1=Navigability g y Question: How can we Measure and Improve Navigability in Social Tagging S t N i bilit i S i l T i Systems? ? Tag clouds as an instrument for g navigation Markus Strohmaier 2010 16

Knowledge Management Institute Tag Clouds are Supposed to be Efficient Tools for Navigating Tagging Systems The Navigability Assumption: • An implicit assumption among designers of social tagging systems that tag clouds are specifically useful to support navigation. • This has hardly been tested or critically reflected in the past past. Navigating tagging systems via tag clouds: 1) The system presents a tag cloud to the user. ) y p g 2) The user selects a tag from the tag cloud. 3) The system presents a list of resources tagged with the selected tag tag. 4) The user selects a resource from the list of resources. 5) The system transfers the user to the selected resource, and th process potentially starts anew. d the t ti ll t t Markus Strohmaier 2010 17

Knowledge Management Institute Navigability of Social Tagging Systems Question: How does (i) th size of t clouds and the i f tag l d d (ii) number of resources / tag influence the navigability (X1) of social tagging systems? established systems, many users New system, few users Markus Strohmaier 2010 18

Knowledge Management Institute Defining Navigability A network is navigable iff: There is a path between all or almost all pairs of nodes in the t i th network. k Formally: 1. There exists a giant component 2. 2 The effective diameter is low (bounded by log n) J. Kleinberg. The small-world phenomenon: An algorithmic perspective. Proc. 32nd ACM Symposium on Theory of Computing, 2000. Also appears as Cornell Computer Science Technical Report 99-1776 (October 1999) Markus Strohmaier 2010 19

Knowledge Management Institute Navigability: Examples Example 1: Not navigable: No giant component Example 2: Not navigable: giant component BUT component, avg. shortest path > log2(9) Markus Strohmaier 2010 20

Knowledge Management Institute Navigability: Examples Example 3: Navigable: Giant component AND avg. avg shortest path ≤ 2 < log2(9) Is this efficiently navigable? There are short paths between all nodes, but can an agent or algorithm find them with local knowledge only? Markus Strohmaier 2010 21

Knowledge Management Institute Efficiently navigable A network is efficiently navigable iff: If there is an algorithm that can find a short path with only l l local k l knowledge ( ith b l d (with branching f t k) and hi factor k), d the delivery time of the algorithm is bounded polynomially by logk(n). B Example 4: p A C Efficiently navigable, if the algorithm knows it needs to go through A B C Markus Strohmaier 2010 J. Kleinberg. The small-world phenomenon: An algorithmic perspective. Proc. 32nd ACM Symposium on Theory of Computing, 2000. Also appears as Cornell Computer Science 22 Technical Report 99-1776 (October 1999)

Knowledge Management Institute User Interface constraints Tag Cloud Size n n: number of tags shown per tag cloud (topN most common algorithm) Pagination of resources / tag k: number of resources shown per page (reverse chronological ordering) Markus Strohmaier 2010 23

Knowledge Management Institute How UI constraints effect Navigability Tag Cloud Size Pagination Limiting the tag cloud size n to practically feasible sizes (e.g. 5, 10, or more) does not influence navigability (this is not very surprising). BUT: Limiting the out-degree of high frequency tags k (e.g. through pagination with resources sorted in reverse-chronological order) leaves the network vulnerable to fragmentation. This destroys navigability of prevalent approaches to tag clouds. Markus Strohmaier 2010 24

Knowledge Management Institute Findings 1. For 1 F certain specific, b t popular, t cloud scenarios, th t i ifi but l tag l d i the so-called Navigability Assumption does not hold. 2. While we could confirm that tag-resource networks have g efficient navigational properties in theory, we found that popular user interface decisions significantly impair navigability. navigability These results make a theoretical and an empirical argument against existing approaches to tag cloud construction. How can we improve the navigability of social tagging systems? Markus Strohmaier 2010 25

Knowledge Management Institute Recovering Navigability in Social Tagging Systems Instead of reverse-chronological ordering of resources, we apply a random ordering. Markus Strohmaier 2010 26

Knowledge Management Institute Efficient Navigability in Social Tagging Systems Instead of random ordering, we use hierarchical background knowledge for ranking paginated resources [Kleinberg 2001]. Markus Strohmaier 2010 J. M. Kleinberg, “Small-world phenomena and the dynamics of information,” in Advances in Neural Information Processing Systems (NIPS), 14. MIT Press, 27 2001, p. 2001.

Knowledge Management Institute Social Computational Systems Implications • Navigability in social tagging systems is an emergent system property • S Some of our initial intuitions about navigability (t f i iti l i t iti b t i bilit (tag clouds) are wrong • The UI represents an opportunity to influence emergent system properties Markus Strohmaier 2010 28

Knowledge Management Institute X1=Semantics Question: How can we Measure and Influence Emergent Semantics in Social Tagging Systems? S t ? Markus Strohmaier 2010 30

Knowledge Management Institute Emergent Semantic Structures Markus Strohmaier 2010 Lerman et al 2010 31

Knowledge Management Institute Pragmatics influence emergent properties Motivations for Tagging M ti ti f T i Kinds f T Ki d of Tags • Future Retrieval • Content-based • Contribution and Sharing • Context-based • Attracting Attention (Flickr) • Attribute Tags • Play and Competition (ESP This suggests that … • Ownership Tags Game) emergent semantics are influenced by the Tags • Subjective • underlying motivation for tagging Self Presentation (cf. f ( f for example, [Heckner 2009]) l [H k • Organizational Tags • Opinion Expression • Purpose Tags • Task Organization (“toread”) • Factual Tags • ( for:scott ) Social Signalling (“for:scott”) • P Personal T l Tags • Money (Amazon Mechanical • Self-referential tags Turk) • Tag Bundles g • Categorization / Description Markus Strohmaier 2010 Gupta et al. 2010 32

Knowledge Management Institute Why Do Users Tag? One ( f O (of many) answers: ) To categorize or to describe resources Categorizer (C) Describer (D) Goal later browsing later search Change of vocabulary costly cheap Size f Si of vocabulary b l limited li it d Open O Tags subjective objective Example tag clouds Semantic Assumption: Categorizers produce more precise emergent semantics than Describers. Markus Strohmaier 2010 M. Strohmaier, C. Koerner, R. Kern, Why do Users Tag? Detecting Users' Motivation for Tagging in Social Tagging Systems, 4th International AAAI Conference on Weblogs and Social Media 33 (ICWSM2010), Washington, DC, USA, May 23-26, 2010.

Knowledge Management Institute Measures for Tagging Pragmatics vs. Tag Semantics Categorizer/Describer: C t i /D ib Semantics: [Cattuto et al 2008] S ti • Size of tag vocabulary • Co-occurrence count • Tags per resource • Cosine similarity (TagCont) • Tags per post • FolkRank [Hotho et al 2006] • Orphaned tags Markus Strohmaier 2010 C. Körner, D. Benz, A. Hotho, M. Strohmaier, G. Stumme, Stop Thinking, Start Tagging: Tag Semantics Emerge From Collaborative Verbosity, 19th International World Wide Web Conference (WWW2010), Raleigh, NC, USA, April 26-30, ACM, 2010. 34

Knowledge Management Institute Experimental Setup As dataset, we used A ad t t d • a crawl from Delicious (University of Kassel) • from November 2006 (containing 667,128 users) • 10.000 most common tags, minimum of 100 resources / user For semantic grounding, we used • WordNet as a knowledge base (cf. [Cattuto et al. 2008]) • Jiang-Conrath as a measure of similarity • combines the taxonomic path length between to nodes in WordNet with an information- theoretic similarity measure [Jiang and Conrath 1997] • A WordNet library as an implementation • by [Pedersen et al 2004] Markus Strohmaier 2010 C. Körner, D. Benz, A. Hotho, M. Strohmaier, G. Stumme, Stop Thinking, Start Tagging: Tag Semantics Emerge From Collaborative Verbosity, 19th International World Wide Web Conference (WWW2010), Raleigh, NC, USA, April 26-30, ACM, 2010. 35

Knowledge Management Institute Results Describers outperform categorizers on precision of emergent tag semantics Categorizers perform Describers perform worse than random better than random worse Random Random users users better Categorizers Describers C. Körner, D. Benz, A. Hotho, M. Strohmaier, G. Stumme, Stop Thinking, Start Tagging: Tag Semantics Emerge From Collaborative Verbosity, 19th International World Wide Web Conference Markus Strohmaier (WWW2010), Raleigh, NC, USA, April 26-30, ACM, 2010. 2010 36

Knowledge Management Institute Social Computational Systems Implications • Semantics in social tagging systems is an emergent system property • S Some of our initial i t iti f i iti l intuitions about semantics are b t ti wrong • describers outperform categorizers on a particular task • User behavior influences emergent system properties Markus Strohmaier 2010 37

Knowledge Management Institute Social-Computational Systems: Conclusions 1. Certain properties of social computational systems (such as navigability or semantics) are emergent p p g y ) g properties, they are , y beyond the direct influence of system designers 2. The user interface is an opportunity to influence these emergent properties 3. If user motivation or behavior changes over time, system properties may change. It is through the process of social computation, i.e. the combination of social behavior and algorithmic computation, that system properties and functions emerge. Markus Strohmaier 2010 39

Knowledge Management Institute Web-Science: A Call to Action As web scientists, we need to • study and map the complex relationships between user behavior behavior, user interfaces and emergent properties • understand the potentials and limits of influencing emergent system properties t ti As web engineers, we need to • shift perspective away from designing towards shaping social- computational systems • reconcile user behaviors with desired system properties Markus Strohmaier 2010 40

Knowledge Management Institute End of Presentation Thank you! Markus Strohmaier Graz University of Technology, Austria y gy, in collaboration with: H.P. Grahsl, D. Helic, C. Körner, R. Kern, C. Trattner, D. Benz, A. Hotho, G. Stumme Markus Strohmaier 2010 42

Knowledge Management Institute Related Publications • Intent and motivation in social media M. Strohmaier, C. Koerner, R. Kern, Why do Users Tag? Detecting Users' Motivation for Tagging in Social Users Tagging Systems, 4th International AAAI Conference on Weblogs and Social Media (ICWSM2010), Washington, DC, USA, May 23-26, 2010. • Social computation and emergent structures C. Körner, D. Benz, A. Hotho, M. Strohmaier, G. Stumme, Stop Thinking, Start Tagging: Tag Semantics Arise From Collaborative Verbosity, 19th International World Wide Web Conference (WWW2010), Raleigh, NC, USA, April 26-30, ACM, 2010. 26 30, D. Helic, C. Trattner, M. Strohmaier and K. Andrews, On the Navigability of Social Tagging Systems, The 2nd IEEE International Conference on Social Computing (SocialCom 2010), Minneapolis, Minnesota, USA, 2010. • Knowledge acquisition from social media C. Wagner, M. Strohmaier, The Wisdom in Tweetonomies: Acquiring Latent Conceptual Structures from Social Awareness Streams, Semantic Search 2010 Workshop (SemSearch2010), in conjunction with the 19th International World Wide Web Conference (WWW2010), Raleigh, NC, USA, April 26-30, ACM, 2010. Markus Strohmaier 2010 43

Social computation of emergent networks on user generated content

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