The power of comments: fostering social interactions in microblog networks

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 Received May 20, 2015; accepted October 29, 2015 E-mail: chenyang@fudan.edu.cn and detection of malicious users.

[1]  S. Cessie,et al.  Ridge Estimators in Logistic Regression , 1992 .

[2]  Jon M. Kleinberg,et al.  The link-prediction problem for social networks , 2007, J. Assoc. Inf. Sci. Technol..

[3]  Jun Hu,et al.  Detecting and characterizing social spam campaigns , 2010, CCS '10.

[4]  Hosung Park,et al.  What is Twitter, a social network or a news media? , 2010, WWW '10.

[5]  Ben Y. Zhao,et al.  Scaling Microblogging Services with Divergent Traffic Demands , 2011, Middleware.

[6]  Jimmy J. Lin,et al.  Information network or social network?: the structure of the twitter follow graph , 2014, WWW.

[7]  Duncan J. Watts,et al.  Everyone's an influencer: quantifying influence on twitter , 2011, WSDM '11.

[8]  Ben Y. Zhao,et al.  Uncovering social network Sybils in the wild , 2011, ACM Trans. Knowl. Discov. Data.

[9]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..

[10]  Chen Huang,et al.  Microblogging after a major disaster in China: a case study of the 2010 Yushu earthquake , 2011, CSCW.

[11]  Nick Feamster,et al.  #bias: Measuring the Tweeting Behavior of Propagandists , 2012, ICWSM.

[12]  Donald F. Towsley,et al.  Estimating and sampling graphs with multidimensional random walks , 2010, IMC '10.

[13]  Krishna P. Gummadi,et al.  On the evolution of user interaction in Facebook , 2009, WOSN '09.

[14]  Robin I. M. Dunbar Social Brain Hypothesis , 1998, Encyclopedia of Evolutionary Psychological Science.

[15]  Yiming Yang,et al.  A Comparative Study on Feature Selection in Text Categorization , 1997, ICML.

[16]  Anja Feldmann,et al.  Understanding online social network usage from a network perspective , 2009, IMC '09.

[17]  K. Fu,et al.  Reality Check for the Chinese Microblog Space: A Random Sampling Approach , 2013, PloS one.

[18]  Sofus A. Macskassy On the Study of Social Interactions in Twitter , 2012, ICWSM.

[19]  Ben Y. Zhao,et al.  Understanding latent interactions in online social networks , 2010, TWEB.

[20]  Minas Gjoka,et al.  Practical Recommendations on Crawling Online Social Networks , 2011, IEEE Journal on Selected Areas in Communications.

[21]  Sune Lehmann,et al.  Understanding the Demographics of Twitter Users , 2011, ICWSM.

[22]  Gianluca Stringhini,et al.  Detecting spammers on social networks , 2010, ACSAC '10.

[23]  Brendan T. O'Connor,et al.  Censorship and deletion practices in Chinese social media , 2012, First Monday.

[24]  Ben Y. Zhao,et al.  Brief announcement: revisiting the power-law degree distribution for social graph analysis , 2010, PODC '10.

[25]  Robin I. M. Dunbar Neocortex size as a constraint on group size in primates , 1992 .

[26]  Michael J. Pazzani,et al.  Learning Collaborative Information Filters , 1998, ICML.

[27]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[28]  Antoine Boutet,et al.  What's in Your Tweets? I Know Who You Supported in the UK 2010 General Election , 2012, ICWSM.

[29]  Dan S. Wallach,et al.  The Velocity of Censorship: High-Fidelity Detection of Microblog Post Deletions , 2013, USENIX Security Symposium.

[30]  Virgílio A. F. Almeida,et al.  Detecting Spammers on Twitter , 2010 .

[31]  Virgílio A. F. Almeida,et al.  Characterizing user behavior in online social networks , 2009, IMC '09.

[32]  Andreas Krause,et al.  Cost-effective outbreak detection in networks , 2007, KDD '07.

[33]  Fan Yang,et al.  Automatic detection of rumor on Sina Weibo , 2012, MDS '12.

[34]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[35]  Lei Shi,et al.  She gets a sports car from our donation: rumor transmission in a Chinese microblogging community , 2013, CSCW.

[36]  Krishna P. Gummadi,et al.  Measurement and analysis of online social networks , 2007, IMC '07.

[37]  Gang Wang,et al.  Northeastern University , 2021, IEEE Pulse.

[38]  Gang Wang,et al.  Social Turing Tests: Crowdsourcing Sybil Detection , 2012, NDSS.

[39]  J. E. Hirsch,et al.  An index to quantify an individual's scientific research output , 2005, Proc. Natl. Acad. Sci. USA.

[40]  Wei Chen,et al.  Efficient influence maximization in social networks , 2009, KDD.

[41]  Hawoong Jeong,et al.  Comparison of online social relations in volume vs interaction: a case study of cyworld , 2008, IMC '08.

[42]  Ben Y. Zhao,et al.  User interactions in social networks and their implications , 2009, EuroSys '09.

[43]  David D. Lewis,et al.  Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval , 1998, ECML.

[44]  Gang Wang,et al.  Serf and turf: crowdturfing for fun and profit , 2011, WWW.

[45]  Mark E. J. Newman,et al.  Power-Law Distributions in Empirical Data , 2007, SIAM Rev..

[46]  Christo Wilson,et al.  Tweeting under pressure: analyzing trending topics and evolving word choice on sina weibo , 2013, COSN '13.

[47]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[48]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[49]  Guangzhong Sun,et al.  Users sleeping time analysis based on micro-blogging data , 2012, UbiComp '12.

[50]  Juan-Zi Li,et al.  Social Influence Locality for Modeling Retweeting Behaviors , 2013, IJCAI.

[51]  Laks V. S. Lakshmanan,et al.  CELF++: optimizing the greedy algorithm for influence maximization in social networks , 2011, WWW.

[52]  Ben Y. Zhao,et al.  On the Embeddability of Random Walk Distances , 2013, Proc. VLDB Endow..

[53]  Jian Huang,et al.  Unveiling the Patterns of Video Tweeting: A Sina Weibo-Based Measurement Study , 2013, PAM.

[54]  Yong Yu,et al.  A comparative study of users' microblogging behavior on sina weibo and twitter , 2012, UMAP.

[55]  Krishna P. Gummadi,et al.  Measuring User Influence in Twitter: The Million Follower Fallacy , 2010, ICWSM.

[56]  Scott Counts,et al.  Predicting the Speed, Scale, and Range of Information Diffusion in Twitter , 2010, ICWSM.

[57]  Alex Hai Wang,et al.  Don't follow me: Spam detection in Twitter , 2010, 2010 International Conference on Security and Cryptography (SECRYPT).

[58]  Timothy W. Finin,et al.  Why we twitter: understanding microblogging usage and communities , 2007, WebKDD/SNA-KDD '07.