digg it up! analyzing popularity evolution in a web 2.0 setting
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Digg it Up! Analyzing Popularity Evolution in a Web 2.0 Setting
Symeon PapadopoulosAthena Vakali
Ioannis Kompatsiaris
Aristotle University, Thessaloniki, GreeceInformatics & Telematics Institute, Thessaloniki, Greece
MSoDa Workshop21 July 2008, Patras, Greece
MSoDa 2008 Symeon Papadopoulos et al. 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
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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].
MSoDa 2008 Symeon Papadopoulos et al. 4
diggTM: Social Media Platform
Popular section
# Diggs
TopicsMedia type
Frequently updated(e.g. once in 20 min)
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Diggsonomy• Social Bookmarking System (SBS)
Diggsonomy: B = {U, R, T, S, D}U: User set, R: Resources, T: Vote timestamps, S⊆UU: User social network, D⊆URT: User voting set
Personomy: Pu = {Ru, Su, Du}Du={(r,t)RT | (u,r,t)D}, Su=πU(S), Ru=πR(Du)
Digg History: Hr = πUT(D|r) ⊆UT• Based on the concept of Folksonomy. See:
[Hotho 2006a], [Hotho 2006b] and [Mika 2005]
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Power Laws
• Very frequentlyappearing in social,natural and computerSystems [Newman 2005]
aCxxp )(
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1 min
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• Described by:• For smoother plots:
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Temporal Evolution
• Popularity evolutionfollows S-shaped curve.• Two-phase growth for stories that are promoted to the popular section.• Aggregate histograms
summarize the phenomenon.
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Social Network Influence
• User Social Susceptibility
Du΄={(ru,tu)Du | fSU, (ru,tf)Df, tf<tu}
• Story Social Influence Gain
Ηr΄={(u,tk)Hr | (u0,t0)Hr, u0Su, t0<tk}
|||| '
u
uu D
DI
|||| '
r
rr H
HI
MSoDa 2008 Symeon Papadopoulos et al. 9
Experimental Study - DatasetGeneral Topics
Stats Tech World & Biz.
Science Games Lifestyle Entertain. Sports Offbeat Total
# stories 16 257 25 894 4 504 6 182 18 070 15 088 5 604 13 509 105 108
# domains 6 999 8 048 2 094 2 071 7 811 4 775 2 052 5 260 30 944
# users 7 604 9 786 2 573 2 712 7 966 6 073 2 382 7 251 34 593
avg. # Diggs 17.31 14.19 28.69 18.43 8.47 9.28 9.11 22.12 14.60
max # Diggs 6 886 4 964 2 991 5 442 4 419 7 842 3 236 8 517 8 517
avg. story life 29.93 29.05 37.96 26.99 22.79 21.31 26.29 27.53 26.92
max story life 397.84 406.60 390.35 395.93 406.73 395.91 394.01 398.90 358.33
Technology15%
World & Business
26%
Science4%
Gaming6%
Lifestyle17%
Entertainment14%
Sports5%
Offbeat13%
TopicComposition
05
101520253035
Diggs / Topic
0
5
1015
20
25
30
35
40
Techn
ology
Worl
d & B
usine
ss
Scienc
e
Gaming
Lifes
tyle
Enterta
inmen
t
Sports
Offbea
t
Avg. story life / Topic
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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].
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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.
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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.
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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.
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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?
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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.
MSoDa 2008 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.
MSoDa 2008 Symeon Papadopoulos et al. 17
Questions
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