Understanding Popularity, Reputation, and Social Influence in the Twitter Society

The pervasive presence of online media in our society has transferred a significant part of political deliberation to online forums and social networking sites. This article examines popularity, reputation, and social influence on Twitter using large-scale digital traces from 2009 to 2016. We process network information on more than 40 million users, calculating new global measures of reputation that build on the D-core decomposition and the bow-tie structure of the Twitter follower network. We integrate our measurements of popularity, reputation, and social influence to evaluate what keeps users active, what makes them more popular, and what determines their influence. We find that there is a range of values in which the risk of a user becoming inactive grows with popularity and reputation. Popularity in Twitter resembles a proportional growth process that is faster in its strongly connected component, and that can be accelerated by reputation when users are already popular. We find that social influence on Twitter is mainly related to popularity rather than reputation, but that this growth of influence with popularity is sublinear. The explanatory and predictive power of our method shows that global network metrics are better predictors of inactivity and social influence, calling for analyses that go beyond local metrics like the number of followers.

[1]  Stefano Battiston,et al.  The Network of Global Corporate Control , 2011, PloS one.

[2]  P. Attewell.  The First and Second Digital Divides , 2001 .

[3]  Michael Mitzenmacher,et al.  A Brief History of Generative Models for Power Law and Lognormal Distributions , 2004, Internet Math..

[4]  Philip Ball.  Social science goes virtual , 2007, Nature.

[5]  Antonio Lima,et al.  Personalized routing for multitudes in smart cities , 2015, EPJ Data Science.

[6]  Jon M Kleinberg,et al.  Hubs, authorities, and communities , 1999, CSUR.

[7]  Andrei Z. Broder,et al.  Graph structure in the Web , 2000, Comput. Networks.

[8]  Krishna P. Gummadi,et al.  Quantifying Information Overload in Social Media and Its Impact on Social Contagions , 2014, ICWSM.

[9]  Filippo Menczer,et al.  Partisan asymmetries in online political activity , 2012, EPJ Data Science.

[10]  D. Holman,et al.  Becoming popular: interpersonal emotion regulation predicts relationship formation in real life social networks , 2015, Front. Psychol..

[11]  Q. Vuong.  Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses , 1989 .

[12]  Yamir Moreno,et al.  Sentiment cascades in the 15M movement , 2015, EPJ Data Science.

[13]  A. Pentland,et al.  Life in the network: The coming age of computational social science: Science , 2009 .

[14]  Yamir Moreno,et al.  The Dynamics of Protest Recruitment through an Online Network , 2011, Scientific reports.

[15]  Pavlin Mavrodiev,et al.  Social resilience in online communities: the autopsy of friendster , 2013, COSN '13.

[16]  Lev Muchnik,et al.  Identifying influential spreaders in complex networks , 2010, 1001.5285.

[17]  S. Krishna,et al.  A model for the emergence of cooperation, interdependence, and structure in evolving networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[18]  Fabián Riquelme,et al.  Measuring user influence on Twitter: A survey , 2015, Inf. Process. Manag..

[19]  Kristina Lerman,et al.  Information is not a Virus, and Other Consequences of Human Cognitive Limits , 2016, Future Internet.

[20]  Zhiming Zheng,et al.  Searching for superspreaders of information in real-world social media , 2014, Scientific Reports.

[21]  Cécile Favre,et al.  Information diffusion in online social networks: a survey , 2013, SGMD.

[22]  W. Bennett,et al.  THE LOGIC OF CONNECTIVE ACTION , 2012 .

[23]  Stephen B. Seidman,et al.  Network structure and minimum degree , 1983 .

[24]  E. B. Wilson.  Probable Inference, the Law of Succession, and Statistical Inference , 1927 .

[25]  Yi Yu,et al.  System crash as dynamics of complex networks , 2016, Proceedings of the National Academy of Sciences.

[26]  D. Sim.  The Rise of the Network Society (The Information Age: Economy, Society and Culture, Volume 1) , 1998 .

[27]  A. Pentland,et al.  Computational Social Science , 2009, Science.

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

[29]  B. Latané.  The psychology of social impact. , 1981 .

[30]  Yamir Moreno,et al.  Absence of influential spreaders in rumor dynamics , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[31]  Fang Wu,et al.  The economics of attention: maximizing user value in information-rich environments , 2007, ADKDD '07.

[32]  Sandra González-Bailón.  Social Science in the Era of Big Data , 2013 .

[33]  Zeynep Tufekci,et al.  Big Questions for Social Media Big Data: Representativeness, Validity and Other Methodological Pitfalls , 2014, ICWSM.

[34]  Arnim Bleier,et al.  When Politicians Talk: Assessing Online Conversational Practices of Political Parties on Twitter , 2014, ICWSM.

[35]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .

[36]  R. M. Alvarez.  Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data , 2014 .

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

[38]  Nemanja Spasojevic,et al.  Klout score: Measuring influence across multiple social networks , 2015, 2015 IEEE International Conference on Big Data (Big Data).

[39]  M. G. Pittau,et al.  A weakly informative default prior distribution for logistic and other regression models , 2008, 0901.4011.

[40]  Taha Yasseri,et al.  A Biased Review of Biases in Twitter Studies on Political Collective Action , 2016, Front. Phys..

[41]  M. Porter,et al.  Critical Truths About Power Laws , 2012, Science.

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

[43]  D. Boyd,et al.  CRITICAL QUESTIONS FOR BIG DATA , 2012 .

[44]  Noah E. Friedkin,et al.  Theoretical Foundations for Centrality Measures , 1991, American Journal of Sociology.

[45]  Venkata Rama Kiran Garimella,et al.  Who watches (and shares) what on youtube? and when?: using twitter to understand youtube viewership , 2013, WSDM.

[46]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[47]  Olivier Toubia,et al.  Intrinsic vs. Image-Related Utility in Social Media: Why Do People Contribute Content to Twitter? , 2013, Mark. Sci..

[48]  D. Plenz,et al.  powerlaw: A Python Package for Analysis of Heavy-Tailed Distributions , 2013, PloS one.

[49]  B. P. Emmett,et al.  Formal Theories of Mass Behaviour , 1967 .

[50]  Karolin Kappler,et al.  Communication dynamics in twitter during political campaigns: The case of the 2011 Spanish national election , 2013 .

[51]  Starr Roxanne Hiltz,et al.  Structuring computer-mediated communication systems to avoid information overload , 1985, CACM.