Naveen Kumar Sharma
Fabricio Benevenuto
Abstract
In this paper, we design and evaluate a novel who-is-who service for inferring attributes that characterize individual Twitter users. Our methodology exploits the Lists feature, which allows a user to group other users who tend to tweet on a topic that is of interest to her, and follow their collective tweets. Our key insight is that the List meta-data (names and descriptions) provides valuable semantic cues about who the users included in the Lists are, including their topics of expertise and how they are perceived by the public. Thus, we can infer a user's expertise by analyzing the meta-data of crowdsourced Lists that contain the user. We show that our methodology can accurately and comprehensively infer attributes of millions of Twitter users, including a vast majority of Twitter's influential users (based on ranking metrics like number of followers). Our work provides a foundation for building better search and recommendation services on Twitter.
- Americans for Democratic Action. www.adaction.org.Google Scholar
- M. Bernstein, D. Tan, G. Smith, M. Czerwinski, and E. Horvitz. Collabio: a game for annotating people within social networks. In ACM symposium on User interface software and technology (UIST), 2009. Google ScholarDigital Library
- M. S. Bernstein, B. Suh, L. Hong, J. Chen, S. Kairam, and E. H. Chi. Eddi: interactive topic-based browsing of social status streams. In ACM symposium on User interface software and technology (UIST), 2010. Google ScholarDigital Library
- M. Cha, H. Haddadi, F. Benevenuto, and K. P. Gummadi. Measuring User Influence in Twitter: The Million Follower Fallacy. In AAAI Conference on Weblogs and Social Media (ICWSM), May 2010.Google Scholar
- S. Dill et al. Semtag and seeker: bootstrapping the semantic web via automated semantic annotation. In ACM Conference on World Wide Web (WWW), 2003. Google ScholarDigital Library
- A. Java, X. Song, T. Finin, and B. Tseng. Why we twitter: understanding microblogging usage and communities. In WebKDD and SNA-KDD workshop on Web mining and Social Network Analysis, 2007. Google ScholarDigital Library
- N. Kallen. Twitter blog: Soon to Launch: Lists. http://tinyurl.com/lists-launch, Sep 2009.Google Scholar
- D. Kim, Y. Jo, I.-C. Moon, and A. Oh. Analysis of Twitter Lists as a Potential Source for Discovering Latent Characteristics of Users. In ACM CHI Workshop on Microblogging, 2010.Google Scholar
- Lada Adamic -- University of Michigan. www.ladamic.com.Google Scholar
- M. Michelson and S. A. Macskassy. Discovering users' topics of interest on Twitter: a first look. In Workshop on Analytics for Noisy unstructured text Data (AND), 2010. Google ScholarDigital Library
- A. Pal and S. Counts. Identifying topical authorities in microblogs. In ACM Conference on Web Search and Data Mining (WSDM), 2011. Google ScholarDigital Library
- R. Pochampally and V. Varma. User context as a source of topic retrieval in Twitter. In Workshop on Enriching Information Retrieval (with ACM SIGIR), Jul 2011.Google Scholar
- D. Ramage, S. Dumais, and D. Liebling. Characterizing Microblogs with Topic Models. In AAAI Conference on Weblogs and Social Media (ICWSM), 2010.Google Scholar
- Twitter: Who to Follow. twitter.com/who_to_follow.Google Scholar
- Twitter Improves "Who To Follow" Results & Gains Advanced Search Page. http://selnd.com/wtfdesc.Google Scholar
- M. J. Welch, U. Schonfeld, D. He, and J. Cho. Topical semantics of Twitter links. In ACM Conference on Web Search and Data Mining (WSDM), 2011. Google ScholarDigital Library
- J. Weng, E.-P. Lim, J. Jiang, and Q. He. Twitterrank: finding topic-sensitive influential twitterers. In ACM Conference on Web Search and Data Mining (WSDM), 2010. Google ScholarDigital Library
- S. Wu, J. M. Hofman, W. A. Mason, and D. J. Watts. Who says what to whom on Twitter. In ACM Conference on World Wide Web (WWW), 2011. Google ScholarDigital Library
- X. Wu, L. Zhang, and Y. Yu. Exploring social annotations for the semantic web. In ACM Conference on World Wide Web (WWW), 2006. Google ScholarDigital Library
Index Terms
- Inferring who-is-who in the Twitter social network
Recommendations
What is Twitter, a social network or a news media?
WWW '10: Proceedings of the 19th international conference on World wide webTwitter, a microblogging service less than three years old, commands more than 41 million users as of July 2009 and is growing fast. Twitter users tweet about any topic within the 140-character limit and follow others to receive their tweets. The goal ...
Inferring user interests in the Twitter social network
RecSys '14: Proceedings of the 8th ACM Conference on Recommender systemsWe propose a novel mechanism to infer topics of interest of individual users in the Twitter social network. We observe that in Twitter, a user generally follows experts on various topics of her interest in order to acquire information on those topics. ...
Inferring who-is-who in the Twitter social network
WOSN '12: Proceedings of the 2012 ACM workshop on Workshop on online social networksIn this paper, we design and evaluate a novel who-is-who service for inferring attributes that characterize individual Twitter users. Our methodology exploits the Lists feature, which allows a user to group other users who tend to tweet on a topic that ...
Comments