Topic-Based Clusters in Egocentric Networks on Facebook

Homophily suggests that people tend to befriend others with shared traits, such as similar topical interests or overlapping social circles. We study how people communicate online in term of conversation topics from an egocentric viewpoint using a dataset from Facebook. We find that friends who favor similar topics form topic-based clusters; these clusters have dense connectivities, large growth rates, and little overlap.

[1]  Jon M. Kleinberg,et al.  Feedback effects between similarity and social influence in online communities , 2008, KDD.

[2]  Chenhao Tan,et al.  On the Interplay between Social and Topical Structure , 2011, ICWSM.

[3]  Filippo Menczer,et al.  Topicality and Social Impact: Diverse Messages but Focused Messengers , 2014, arXiv.org.

[4]  Tad Hogg,et al.  Friends and foes: ideological social networking , 2008, CHI.

[5]  H. B. Mann,et al.  On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other , 1947 .

[6]  Filippo Menczer,et al.  Topicality and Impact in Social Media: Diverse Messages, Focused Messengers , 2014, PloS one.

[7]  Matthew Michelson,et al.  Tweet Disambiguate Entities Retrieve Folksonomy SubTree Step 1 : Discover Categories Generate Topic Profile from SubTrees Step 2 : Discover Profile Topic Profile : “ English Football ” “ World Cup ” , 2010 .

[8]  M. Macy,et al.  Small Worlds and Cultural Polarization , 2011 .

[9]  Arun Sundararajan,et al.  Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks , 2009, Proceedings of the National Academy of Sciences.

[10]  Judith S. Donath,et al.  Homophily in online dating: when do you like someone like yourself? , 2005, CHI Extended Abstracts.

[11]  M. Macy.  Polarization in Dynamic Networks : A Hopfield Model of Emergent Structure , 2003 .

[12]  Rossano Schifanella,et al.  Folks in Folksonomies: social link prediction from shared metadata , 2010, WSDM '10.

[13]  D. Watts,et al.  Origins of Homophily in an Evolving Social Network1 , 2009, American Journal of Sociology.

[14]  Filippo Menczer,et al.  Lexical and semantic clustering by Web links , 2004, J. Assoc. Inf. Sci. Technol..

[15]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[16]  Jon M. Kleinberg,et al.  Group formation in large social networks: membership, growth, and evolution , 2006, KDD '06.

[17]  J. Fuhrmann.  Echo Chamber Rush Limbaugh And The Conservative Media Establishment , 2016 .

[18]  Rossano Schifanella,et al.  Friendship prediction and homophily in social media , 2012, TWEB.

[19]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[20]  Munmun De Choudhury,et al.  Tie Formation on Twitter: Homophily and Structure of Egocentric Networks , 2011, SocialCom/PASSAT.

[21]  Qi He,et al.  TwitterRank: finding topic-sensitive influential twitterers , 2010, WSDM '10.

[22]  Andreas Daffertshofer,et al.  Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory , 2010, PloS one.

[23]  Brian D. Davison.  Topical locality in the Web , 2000, SIGIR '00.

[24]  Sean A. Munson,et al.  Presenting diverse political opinions: how and how much , 2010, CHI.