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

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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].

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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]

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)1(

0 1'')(

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1

1 min

ln1

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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

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r

rr H

HI

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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.

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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.

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Questions