Topicality and Social Impact: Diverse Messages but Focused Messengers

Are users who comment on a variety of matters more likely to achieve high influence than those who delve into one focused field? Do general Twitter hashtags, such as #lol, tend to be more popular than novel ones, such as #instantlyinlove? Questions like these demand a way to detect topics hidden behind messages associated with an individual or a hashtag, and a gauge of similarity among these topics. Here we develop such an approach to identify clusters of similar hashtags by detecting communities in the hashtag co-occurrence network. Then the topical diversity of a user’s interests is quantified by the entropy of her hashtags across different topic clusters. A similar measure is applied to hashtags, based on co-occurring tags. We find that high topical diversity of early adopters or co-occurring tags implies high future popularity of hashtags. In contrast, low diversity helps an individual accumulate social influence. In short, diverse messages and focused messengers are more likely to gain impact.

[1]  Filippo Menczer,et al.  Clustering memes in social media , 2013, 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013).

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

[3]  Krithi Ramamritham,et al.  Real Time Discovery of Dense Clusters in Highly Dynamic Graphs: Identifying Real World Events in Highly Dynamic Environments , 2012, Proc. VLDB Endow..

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

[5]  Lei Yang,et al.  We know what @you #tag: does the dual role affect hashtag adoption? , 2012, WWW.

[6]  Katherine L. Milkman,et al.  What Makes Online Content Viral? , 2012 .

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

[8]  M. Newman Communities, modules and large-scale structure in networks , 2011, Nature Physics.

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

[10]  John R. Kender,et al.  Visual memes in social media: tracking real-world news in YouTube videos , 2011, ACM Multimedia.

[11]  Qing Yang,et al.  Discovering User Interest on Twitter with a Modified Author-Topic Model , 2011, 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.

[12]  Krishna P. Gummadi,et al.  Media Landscape in Twitter: A World of New Conventions and Political Diversity , 2011, ICWSM.

[13]  Lada A. Adamic,et al.  Memes Online: Extracted, Subtracted, Injected, and Recollected , 2011, ICWSM.

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

[15]  Francesco Bonchi,et al.  The Meme Ranking Problem: Maximizing Microblogging Virality , 2010, 2010 IEEE International Conference on Data Mining Workshops.

[16]  Dylan Walker,et al.  Creating Social Contagion Through Viral Product Design: A Randomized Trial of Peer Influence in Networks , 2010, ICIS.

[17]  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 .

[18]  Ed H. Chi,et al.  Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network , 2010, 2010 IEEE Second International Conference on Social Computing.

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

[20]  N. Eagle,et al.  Network Diversity and Economic Development , 2010, Science.

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

[22]  Michael S. Bernstein,et al.  Short and tweet: experiments on recommending content from information streams , 2010, CHI.

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

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

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

[26]  Barry Smyth,et al.  Using twitter to recommend real-time topical news , 2009, RecSys '09.

[27]  A. Vespignani Predicting the Behavior of Techno-Social Systems , 2009, Science.

[28]  Jimeng Sun,et al.  Social influence analysis in large-scale networks , 2009, KDD.

[29]  Jure Leskovec,et al.  Meme-tracking and the dynamics of the news cycle , 2009, KDD.

[30]  B. Huberman,et al.  Predicting the popularity of online content , 2008, Commun. ACM.

[31]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[32]  Marián Boguñá,et al.  Self-similarity of complex networks and hidden metric spaces , 2007, Physical review letters.

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

[34]  Kristina Lerman,et al.  Social Information Processing in News Aggregation , 2007, IEEE Internet Computing.

[35]  A-L Barabási,et al.  Structure and tie strengths in mobile communication networks , 2006, Proceedings of the National Academy of Sciences.

[36]  Matthew J. Salganik,et al.  Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market , 2006, Science.

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

[38]  Taher H. Haveliwala Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Search , 2003, IEEE Trans. Knowl. Data Eng..

[39]  Kenneth Y. Goldberg,et al.  Eigentaste: A Constant Time Collaborative Filtering Algorithm , 2001, Information Retrieval.

[40]  Alessandro Vespignani,et al.  Epidemic spreading in scale-free networks. , 2000, Physical review letters.

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

[42]  John M. Quigley,et al.  Urban Diversity and Economic Growth , 2012 .

[43]  Filippo Menczer,et al.  Adaptive information agents in distributed textual environments , 1998, AGENTS '98.

[44]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[45]  Mark S. Granovetter The Strength of Weak Ties , 1973, American Journal of Sociology.

[46]  D. Kendall,et al.  Epidemics and Rumours , 1964, Nature.

[47]  WILLIAM GOFFMAN,et al.  Generalization of Epidemic Theory: An Application to the Transmission of Ideas , 1964, Nature.

[48]  Charu C. Aggarwal,et al.  Event Detection in Social Streams , 2012, SDM.

[49]  Steve Cayzer,et al.  Learning User Profiles from Tagging Data and Leveraging them for Personal(ized) Information Access , 2007, WWW 2007.