Who creates trends in online social media: The crowd or opinion leaders?

Internet slang words can very quickly become ubiquitous because of social memes and viral online content. Weibo, a Twitter-like service in China, demonstrates that the adoption of popular Internet slang undergoes 2 distinct peaks in its temporal evolution, in which the former is relatively much lower than the latter. An in-depth comparison of the diffusion of these different peaks suggests that popular attention in the early stage of propagation results in large-scale coverage, while the participation of opinion leaders at the early stage only leads to minor popularity. Our empirical results question the conventional influentials hypothesis and provide some insights for marketing practice and influence maximization in social networks.

[1]  Filippo Menczer,et al.  The Digital Evolution of Occupy Wall Street , 2013, PloS one.

[2]  Kevin Fiedler,et al.  Grooming Gossip And The Evolution Of Language , 2016 .

[3]  E. Rogers,et al.  Diffusion of Innovations , 1964 .

[4]  Michal B. Paradowski,et al.  Understanding the Social Cascading of Geekspeak and the Upshots for Social Cognitive Systems , 2011, 1111.6843.

[5]  E. Katz.  The Two-Step Flow of Communication: An Up-To-Date Report on an Hypothesis , 1957 .

[6]  A. Vespignani,et al.  Competition among memes in a world with limited attention , 2012, Scientific Reports.

[7]  Didier Sornette,et al.  Robust dynamic classes revealed by measuring the response function of a social system , 2008, Proceedings of the National Academy of Sciences.

[8]  Kazuyuki Aihara,et al.  Quantifying Collective Attention from Tweet Stream , 2013, PloS one.

[9]  Sushant S. Khopkar,et al.  Efficient algorithms for incremental all pairs shortest paths, closeness and betweenness in social network analysis , 2014, Social Network Analysis and Mining.

[10]  Philip Garnett,et al.  Word Diffusion and Climate Science , 2012, PloS one.

[11]  Joshua M. Scacco,et al.  Changing Deliberative Norms on News Organizations' Facebook Sites , 2015, J. Comput. Mediat. Commun..

[12]  Jure Leskovec,et al.  Inferring networks of diffusion and influence , 2010, KDD.

[13]  Albert-László Barabási,et al.  Collective Response of Human Populations to Large-Scale Emergencies , 2011, PloS one.

[14]  Andrea Tagarelli,et al.  Online popularity and topical interests through the lens of instagram , 2014, HT.

[15]  Pin Luarn,et al.  The network effect on information dissemination on social network sites , 2014, Comput. Hum. Behav..

[16]  Yamir Moreno,et al.  Broadcasters and Hidden Influentials in Online Protest Diffusion , 2012, ArXiv.

[17]  Filippo Menczer,et al.  Virality Prediction and Community Structure in Social Networks , 2013, Scientific Reports.

[18]  Brendan T. O'Connor,et al.  Diffusion of Lexical Change in Social Media , 2012, PloS one.

[19]  Ramakrishnan Srikant,et al.  Kdd-2001: Proceedings of the Seventh Acm Sigkdd International Conference on Knowledge Discovery and Data Mining : August 26-29, 2001 San Francisco, Ca, USA , 2002 .

[20]  D. Meadows-Klue.  The Tipping Point: How Little Things Can Make a Big Difference , 2004 .

[21]  Samarth Swarup,et al.  A model of norm emergence and innovation in language change , 2011, AAMAS.

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

[23]  Yong Hu,et al.  Impact of user influence on information multi-step communication in a micro-blog , 2014 .

[24]  Xueqi Cheng,et al.  Popularity prediction in microblogging network: a case study on sina weibo , 2013, WWW.

[25]  Daniel M. Romero,et al.  Influence and passivity in social media , 2010, ECML/PKDD.

[26]  Bernardo A. Huberman,et al.  Trends in Social Media: Persistence and Decay , 2011, ICWSM.

[27]  何小海,et al.  Impact of user influence on information multi-step communication in a micro-blog , 2014 .

[28]  Filippo Menczer,et al.  Clustering memes in social media streams , 2014, Social Network Analysis and Mining.

[29]  Maureen Tanner,et al.  Social capital: the benefit of Facebook ‘friends’ , 2013, Behav. Inf. Technol..

[30]  Jure Leskovec,et al.  The dynamics of viral marketing , 2005, EC '06.

[31]  D. Watts,et al.  Influentials, Networks, and Public Opinion Formation , 2007 .

[32]  L. Flynn,et al.  Opinion leaders and opinion seekers: Two new measurement scales , 1996 .

[33]  Fang Wu,et al.  Novelty and collective attention , 2007, Proceedings of the National Academy of Sciences.

[34]  Jiawei Han,et al.  Inferring the Diffusion and Evolution of Topics in Social Communities , 2011 .

[35]  Yifei Yuan,et al.  Scalable Influence Maximization in Social Networks under the Linear Threshold Model , 2010, 2010 IEEE International Conference on Data Mining.

[36]  Ciro Cattuto,et al.  Dynamical classes of collective attention in twitter , 2011, WWW.

[37]  Hsia-Ching Chang,et al.  A new perspective on Twitter hashtag use: Diffusion of innovation theory , 2010, ASIST.

[38]  J. Walther.  Computer-Mediated Communication , 1996 .

[39]  Łukasz Jonak,et al.  Diffusion of Linguistic Innovation as Social Coordination , 2012 .

[40]  Christian Bauckhage,et al.  Collective attention to social media evolves according to diffusion models , 2014, WWW '14 Companion.

[41]  Thomas W. Valente,et al.  Opinion Leadership and Social Contagion in New Product Diffusion , 2011, Mark. Sci..

[42]  Jon Kleinberg,et al.  Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter , 2011, WWW.

[43]  Matthew Richardson,et al.  Mining the network value of customers , 2001, KDD '01.

[44]  Ee-Peng Lim,et al.  Influentials, Novelty, and Social Contagion: The Viral Power of Average Friends, Close Communities, and Old News , 2012, Soc. Networks.

[45]  Peng Bao,et al.  Cumulative Effect in Information Diffusion: Empirical Study on a Microblogging Network , 2013, PloS one.

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

[47]  Guannan Liu,et al.  Being Rational or Aggressive? A Revisit to Dunbar's Number in Online Social Networks , 2010, Neurocomputing.

[48]  R. Bagozzi,et al.  A Social Influence Model of Consumer Participation in Network- and Small-Group-Based Virtual Communities , 2004 .

[49]  Filippo Menczer,et al.  Evolution of online user behavior during a social upheaval , 2014, WebSci '14.

[50]  Éva Tardos,et al.  Influential Nodes in a Diffusion Model for Social Networks , 2005, ICALP.

[51]  Robert L. Goldstone,et al.  The simultaneous evolution of author and paper networks , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[52]  Michael Trusov,et al.  Determining Influential Users in Internet Social Networks , 2010 .

[53]  Filippo Menczer,et al.  Traveling trends: social butterflies or frequent fliers? , 2013, COSN '13.