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Digg it Up! Analyzing Popularity Evolution in a Web 2.0 Setting

MSoDa Workshop 21 July 2008, Patras, Greece. Digg it Up! Analyzing Popularity Evolution in a Web 2.0 Setting. Symeon Papadopoulos Athena Vakali Ioannis Kompatsiaris. Aristotle University, Thessaloniki, Greece Informatics & Telematics Institute, Thessaloniki, Greece. Introduction.

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Digg it Up! Analyzing Popularity Evolution in a Web 2.0 Setting

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  1. MSoDa Workshop 21 July 2008, Patras, Greece Digg it Up! Analyzing Popularity Evolution in a Web 2.0 Setting Symeon Papadopoulos Athena Vakali Ioannis Kompatsiaris Aristotle University, Thessaloniki, Greece Informatics & Telematics Institute, Thessaloniki, Greece

  2. Introduction • Social Media  An emerging publishing paradigm for digital media • Popular Social Media applications: diggTM, newsvine, del.icio.us • Complex phenomena: • Story popularity evolution • Influence of user social network on story popularity Symeon Papadopoulos et al.

  3. Related Work • Social Tagging Systems [Golder 2006], [Halpin 2007], [Giannakidou 2008]. • Folksonomies [Hotho 2006a], [Hotho 2006b], [Mika 2005]. • Temporal activity patterns [Cha 2007], [Kaltenbrunner 2007a], [Kaltenbrunner 2007b]. • Communities in time [Falkowski 2007]. • Novelty diffusion in Digg [Wu 2007]. Symeon Papadopoulos et al.

  4. diggTM: Social Media Platform Media type Topics Popular section # Diggs Frequently updated (e.g. once in 20 min) Symeon Papadopoulos et al.

  5. Diggsonomy • Social Bookmarking System (SBS) Diggsonomy: B= {U, R, T, S, D} U: User set, R: Resources, T: Vote timestamps, S⊆UU: User social network, D⊆URT: User voting set Personomy: Pu = {Ru, Su, Du} Du={(r,t)RT | (u,r,t)D}, Su=πU(S), Ru=πR(Du) Digg History: Hr = πUT(D|r) ⊆UT • Based on the concept of Folksonomy. See: [Hotho 2006a], [Hotho 2006b] and [Mika 2005] Symeon Papadopoulos et al.

  6. Power Laws • Very frequently appearing in social, natural and computer Systems [Newman 2005] • Described by: • For smoother plots: Symeon Papadopoulos et al.

  7. Temporal Evolution • Popularity evolution follows S-shaped curve. • Two-phase growth for stories that are promoted to the popular section. • Aggregate histograms summarize the phenomenon. Symeon Papadopoulos et al.

  8. Social Network Influence • User Social Susceptibility Du΄={(ru,tu)Du |  fSU, (ru,tf)Df, tf<tu} • Story Social Influence Gain Ηr΄={(u,tk)Hr | (u0,t0)Hr, u0Su, t0<tk} Symeon Papadopoulos et al.

  9. Experimental Study - Dataset Diggs / Topic Avg. story life / Topic Topic Composition Symeon Papadopoulos et al.

  10. Experimental Study – Power Law • Approximate power law with exponent a = 1.85 (since the exponent of the CDF was found to be a΄=0.85). • Rich-get-richer: Few stories receive loads of Diggs. Partly due to restricted webpage real estate available to show stories [cha2007]. Symeon Papadopoulos et al.

  11. Experimental Study – Popularity • Stories that are meant to be popular maintain some popularity momentum through their phase-A of popularity growth. • During phase-B of popularity growth, the biggest majority of Diggs is collected in the first 10% of the phase: The stories gain extremely high exposure since they are placed in the main diggTM page. Symeon Papadopoulos et al.

  12. Experimental Study - SSIG • A surprising finding: • Stories that have high social information gain (>0.3) are almost bound to be non-popular. • Potential reason: • diggTM may block such stories from becoming popular in order to prevent coordinated user communities from artificially boosting story popularity. Symeon Papadopoulos et al.

  13. Conclusions • Digg ~ Popularity in Social Media follows a power law  Rich get richer! • Popularity is greatly boosted when placed in the first page of the site.  Exposure results into popularity. • Social aspects influence popularity.  Stories digg-ed by many “friends” are blocked from being popular. Symeon Papadopoulos et al.

  14. Future Work • Analysis of diggTM social network: centrality, k-cores, degree distribution, community structure. • Study of correlation between popularity and vocabulary  Discovery of “catchy” words. • Prediction of popularity. What kind of features could be of use? Symeon Papadopoulos et al.

  15. References (1/2) • A. L. Barabasi and R. Albert, “Emergence of scaling in random networks”, Science, 286(5439), 509-512, October 1999. • George Edward Pehlam Box and Gwilym M. Jenkins, “Time Series Analysis: Forecasting and Control”, Prentice-Hall PTR, Upper Saddle River, NJ, USA, 1994. • Meeyoung Cha, Haewoon Kwak, Pablo Rodriguez, Yong-Yeol Ahn and Sue Moon, “I Tube, You Tube, Everybody Tubes: Analyzing the World’s Largest User Generated Content Video System”, in ACM International Measurement Conference, October 2007. • E. Giannakidou, V. Koutsonikola, A. Vakali and I. Kompatsiaris, “Co-clustering tags and social data sources”, in 9th International Conference on Web-Age Information Management, WAIM 2008. • Tanja Falkowski and Myra Spiliopoulou, “Users in volatile communities: Studying active participation and community evolution”, User Modeling 2007, 47-56. • Scott A. Golder and Bernardo A. Huberman, “Usage patterns of collaborative tagging systems”, Journal of Info. Sciences, 32(2), 198-208, 2006. • Harry Halpin, Valentin Robu and Hana Shepherd, “The complex dynamics of collaborative tagging”, in WWW ’07: Proceedings of the 16th International Conference on World Wide Web, pp. 211-220, New York, NY, USA, 2007, ACM Press. • Andreas Hotho, Robert Jaeschke, Christoph Schmitz, and Gerd Stumme, “Information retrieval in folksonomies: Search and ranking”, in Proceedings of the 3rd European Semantic Web Conference, volume 4011 of LNCS, pp. 411-426, Budva, Montenegro, June 2006, Springer. • Andreas Hotho, Robert Jaeschke, Christoph Schmitz, and Gerd Stumme, “Trend detection in folksonomies”, in Proceedings of the First International Conference on Semantic and Digital Media Technologies (SAMT 2006), pp. 56-70, Springer, December 2006. Symeon Papadopoulos et al.

  16. References (2/2) • Andreas Kaltenbrunner, Vicenc Gomez, and Vicente Lopez, “Description and prediction of slashdot activity”, in Proceedings of the 5th Latin American Web Congress (LA-WEB 2007), Santiago de Chile, 2007, IEEE Computer Society. • Andreas Kaltenbrunner, Vicenc Gomez, Ayman Moghnieh, Rodrigo Meza, Josep Blat, and Vincente Lopez, “Homogeneous temporal activity patterns in a large online communication space”, in Proceedings of the BIS 2007, Workshop on Social Aspects of the Web (SAW 2007), Poznan, Poland, 2007, CEUR-WS. • Jon M. Kleinberg, “Authoritative sources in a hyperlinked environment”, Journal of the ACM, 46(5), 604-632, 1999. • Peter Mika, “Ontologies are us: A unified model of social netoworks and semantics”, International Semantic Web Conference, pp. 522-536, 2005. • M. Newman, “Power laws, Pareto distribution and Zipf’s law”, Contemporary Physics, 46, 323-351, September 2005. • Lawrence Page, Sergey Brin, Rajeev Motwani and Terry Winograd, “The pagerank citation ranking: Bringing order to the web”, Technical report, Stanford Digital Library Technologies Project, 1998. • Matthew Richardson, Ewa Dominowska, and Robert Ragno, “Predicting clicks: estimating the click-through rate for new ads”, in WWW’07: Proceedings of the 16th International Conference on the World Wide Web, pp. 521-530, New York, NY, USA, 2007, ACM. • Fang Wu, and Bernardo A. Huberman, “ Novelty and collective attention”, Proceedings of the National Academy of Sciences, 104(45), 17599-17601, November 2007. Symeon Papadopoulos et al.

  17. Questions Symeon Papadopoulos et al.

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