The power of comments: fostering social interactions in microblog networks

Abstract

Today’s ubiquitous online social networks serve multiple purposes, including social communication (Facebook, Renren), and news dissemination (Twitter). But how does a social network’s design define its functionality? Answering this would need social network providers to take a proactive role in defining and guiding user behavior.

In this paper, we first take a step to answer this question with a data-driven approach, through measurement and analysis of the Sina Weibo microblogging service. Often compared to Twitter because of its format,Weibo is interesting for our analysis because it serves as a social communication tool and a platform for news dissemination, too. While similar to Twitter in functionality, Weibo provides a distinguishing feature, comments, allowing users to form threaded conversations around a single tweet. Our study focuses on this feature, and how it contributes to interactions and improves social engagement.We use analysis of comment interactions to uncover their role in social interactivity, and use comment graphs to demonstrate the structure of Weibo users interactions. Finally, we present a case study that shows the impact of comments in malicious user detection, a key application on microblogging systems. That is, using properties of comments significantly improves the accuracy in both modeling and detection of malicious users.

Keywords

microblogs / comments / social and interaction graph / user behavior

References

[1] Kwak H, Lee C, Park H, Moon S. What is Twitter, a social network or a news media? In: Proceedings of the 19th International Conference on World Wide Web. 2010, 591–600

[2] Gao Q, Abel F, Houben G J, Yu Y. A comparative study of user’s microblogging behavior on Sina Weibo and Twitter. In: Proceedings of the 20th Conference on User Modeling, Adaptation, and Personalization. 2012, 88–101

[3] Jiang J, Wilson C, Wang X, Sha W P, Huang P, Dai Y F, Zhao B Y. Understanding latent interactions in online social networks. ACM Transactions on the Web (TWEB), 2013, 7(4): 18

[4] Gjoka M, Kurant M, Butts C T, Markopoulou A. Practical recommendations on crawling online social networks. IEEE Journal on Selected Areas in Communications, 2011, 29(9): 1872–1892

[5] Ribeiro B, Towsley D. Estimating and sampling graphs with multidimensional random walks. In: Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement. 2010, 390–403

[6] Wilson C, Boe B, Sala A, Puttaswamy K P N, Zhao B Y. User interactions in social networks and their implications. In: Proceedings of the 4th ACM European Conference on Computer Systems. 2009, 205–218

[7] Gao Q, Abel F, Houben G J, Yu Y. Information propagation cultures on Sina Weibo and Twitter. In: Proceedings of the 4th Annual ACM Web Science Conference. 2012, 157–162

[8] Fu K, Chau M. Reality check for the Chinese microblog space: a random sampling approach. PloS one, 2013, 8(3): e58356

[9] Liao Q Y, Shi L. She gets a sports car from our donation: rumor transmission in a Chinese microblogging community. In: Proceedings of the 2013 Conference on Computer Supported Cooperative Work. 2013, 587–598

[10] Guo Z D, Huang J, He J, Hei X J, Wu D. Unveiling the patterns of video tweeting: a Sina Weibo-based measurement study. In: Proceedings of Passive and Active Measurement. 2013, 166–175

[11] Chen L, Zhang C, Wilson C. Tweeting under pressure: analyzing trending topics and evolving word choice on Sina Weibo. In: Proceedings of the 1st ACM Conference on Online Social Networks. 2013, 89–100

[12] Sala A, Zheng H T, Zhao B Y, Gaito S, Rossi G P. Brief announcement: revisiting the power-law degree distribution for social graph analysis. In: Proceedings of the 29th ACM SIGACT-SIGOPS Symposium on Principles of Distributed Computing. 2010, 400–401

[13] Clauset A, Shalizi C R, Newman M E J. Power-law distributions in empirical data. SIAM Review, 2009, 51(4): 661–703

[14] Java A, Song X D, Finin T, Tseng B. Why we twitter: understanding microblogging usage and communities. In: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis. 2007, 56–65

[15] Chun H, Kwak H, Eom Y H, Ahn Y Y, Moon S, Jeong H. Comparison of online social relations in volume vs interaction: a case study of cyworld. In: Proceedings of the 8th ACM SIGCOMM Conference on Internet Measurement. 2008, 57–70

[16] Viswanath B, Mislove A, Cha M, Gummadi K P. On the evolution of user interaction in Facebook. In: Proceedings of the 2nd ACM Workshop on Online Social Networks. 2009, 37–42

[17] Benevenuto F, Rodrigues T, Cha M, Almeida V. Characterizing user behavior in online social networks. In: Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement Conference. 2009, 49–62

[18] Schneider F, Feldmann A, Krishnamurthy B, Willinger W. Understanding online social network usage from a network perspective. In: Proceedings of the 9th ACMSIGCOMMConference on Internet Measurement Conference. 2009, 35–48

[19] Xu T Y, Chen Y, Jiao L, Zhao B Y, Hui P, Fu X M. Scaling microblogging services with divergent traffic demands. In: Proceedings of the 12th International Middleware Conference. 2011, 20–39

[20] Mislove A, Marcon M, Gummadi K P, Druschel P, Bhattacharjee B. Measurement and analysis of online social networks. In: Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement. 2007, 29–42

[21] Dunbar R I M. Neocortex size as a constraint on group size in primates. Journal of Human Evolution, 1992, 22(6): 469–493

[22] Dunbar R I M. The social brain hypothesis. Foundations in Social Neuroscience, 2002, 5(71): 69

[23] Watts D J, Strogatz S H. Collective dynamics of “small-world” networks. Nature, 1998, 393(6684): 440–442

[24] Zhao X H, Chang A, Sarma A D, Zheng H T, Zhao B Y. On the embeddability of random walk distances. VLDB Endowment, 2013, 6(14): 1690–1701

[25] Liben-Nowell D, Kleinberg J. The link-prediction problem for social networks. Journal of the American Society for Information Science and Technology, 2007, 58(7): 1019–1031

[26] Hirsch J E. An index to quantify an individual’s scientific research output. National Academy of Sciences of the United States of America, 2005, 102(46): 16569–16572

[27] Benevenuto F, Magno G, Rodrigues T, Almeida V. Detecting spammers on Twitter. In: Proceedings of Collaboration, Electronic Messaging, Anti-Abuse and Spam Conference. 2010, 12

[28] Wang A H. Don’t follow me: spam detection in Twitter. In: Proceedings of the 2010 International Conference on Security and Cryptography. 2010, 1–10

[29] Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten I H. The WEKA data mining software: an update. ACM SIGKDD Explorations Newsletter, 2009, 11(1): 10–18

[30] Lewis D D. Naive (Bayes) at forty: the independence assumption in information retrieval. In: Proceedings of the 10th European Conference on Machine Learning. 1998, 4–15

[31] Chang C C, Lin C J. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3):27

[32] Breiman L. Random forests. Machine Learning, 2001, 45(1): 5–32

[33] Le Cessie S, Van Houwelingen J C. Ridge estimators in logistic regression. Applied Statistics, 1992, 191–201

[34] Billsus D, Pazzani M J. Learning collaborative information filters. In: Proceedings of International Conference on Machine Learning. 1998, 46–54

[35] Yang Y M, Pedersen J O. A comparative study on feature selection in text categorization. In: Proceedings of International Conference on Machine Learning. 1997, 412–420

[36] Wang G, Wilson C, Zhao X H, Zhu Y B, Mohanlal M, Zheng H T, Zhao B Y. Serf and turf: crowdturfing for fun and profit. In: Proceedings of the 21st International Conference on World Wide Web. 2012, 679–688

[37] Kempe D, Kleinberg J, Tardos É. Maximizing the spread of influence through a social network. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2003, 137–146

[38] Leskovec J, Krause A, Guestrin C, Faloutsos C, VanBriesen J, Glance N. Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2007, 420–429

[39] Chen W, Wang Y J, Yang S Y. Efficient influence maximization in social networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and DataMining. 2009, 199–208

[40] Goyal A, Lu W, Lakshmanan L V S. CELF++: optimizing the greedy algorithm for influence maximization in social networks. In: Proceedings of the 20th International Conference Companion on World Wide Web. 2011, 47–48

[41] Myers S A, Sharma A, Gupta P, Lin J. Information network or social network?: the structure of the Twitter follow graph. In: Proceedings of the Companion Publication of the 23rd International Conference on World Wide Web Companion. 2014, 493–498

[42] Yang J, Counts S. Predicting the speed, scale, and range of information diffusion in Twitter. In: Proceedings of the International AAAI Conference on Web and Social Media. 2010, 355–358

[43] Lumezanu C, Feamster N, Klein H. #bias: Measuring the tweeting behavior of propagandists. In: Proceedings of the International AAAI Conference on Web and Social Media. 2012

[44] Cha M, Haddadi H, Benevenuto F, Gummadi P K. Measuring user influence in Twitter: The million follower fallacy. In: Proceedings of the International AAAI Conference on Web and Social Media. 2010, 10–17

[45] Bakshy E, Hofman J M, Mason W A, Watts D J. Everyone’s an influencer: quantifying influence on Twitter. In: Proceedings of the 4th ACMInternational Conference onWeb Search and DataMining. 2011, 65–74

[46] Boutet A, Kim H, Yoneki E. What’s in your tweets? I know who you supported in the UK 2010 general election. In: Proceedings of the International AAAI Conference on Web and Social Media. 2012

[47] Mislove A, Lehmann S, Ahn Y Y, Onnela J P, Rosenquist J N. Understanding the demographics of Twitter users. In: Proceedings of the International AAAI Conference on Web and Social Media. 2011

[48] Bamman D, O’Connor B, Smith N. Censorship and deletion practices in Chinese social media. First Monday, 2012, 17(3)

[49] Zhu T, Phipps D, Pridgen A, Crandall J R, Wallach D S. The velocity of censorship: high-fidelity detection of microblog post deletions. In: Proceedings of the 22nd USENIX Conference on Security. 2013, 227–240

[50] Zhang J, Liu B, Tang J, Chen T, Li J. Social influence locality for modeling retweeting behaviors. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence. 2013, 2761–2767

[51] Qu Y, Huang C, Zhang P Y, Zhang J. Microblogging after a major disaster in China: a case study of the 2010 Yushu earthquake. In: Proceedings of the ACM 2011 Conference on Computer Supported Cooperative Work. 2011, 25–34

[52] Yu H R, Sun G Z, Lv M. Users sleeping time analysis based on microblogging data. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing. 2012, 964–968

[53] Yang F, Liu Y, Yu X H, Yang M. Automatic detection of rumor on Sina Weibo. In: Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics. 2012, 13

[54] Macskassy S A. On the study of social interactions in Twitter. In: Proceedings of the International AAAI Conference onWeb and Social Media. 2012

[55] Gao H Y, Hu J, Wilson C, Li Z C, Chen Y, Zhao B Y. Detecting and characterizing social spam campaigns. In: Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement. 2010, 35–47

[56] Wang G, Konolige T, Wilson C, Wang X, Zheng H, Zhao B Y. You are how you click: Clickstream analysis for sybil detection. In: Proceedings of the 22nd USENIX Conference on Security. 2013, 1–15

[57] Wang G, Mohanlal M, Wilson C, Wang X, Metzger M, Zheng H T, Zhao B Y. Social turing tests: crowdsourcing sybil detection. In: Proceedings of the 20th Annual Network and Distributed System Security Symposium. 2013

[58] Yang Z, Wilson C, Wang X, Gao T, Zhao B Y, Dai Y. Uncovering social network sybils in the wild. ACM Transactions on Knowledge Discovery from Data, 2014, 8(1): 2

[59] Stringhini G, Kruegel C, Vigna G. Detecting spammers on social networks. In: Proceedings of the 26th Annual Computer Security Applications Conference. 2010, 1–9

RIGHTS & PERMISSIONS

Higher Education Press and Springer-Verlag Berlin Heidelberg