Kyumin Lee
Michelle Zhou
Jeffrey Nichols
ABSTRACT
There has been much effort on studying how social media sites, such as Twitter, help propagate information in different situations, including spreading alerts and SOS messages in an emergency. However, existing work has not addressed how to actively identify and engage the right strangers at the right time on social media to help effectively propagate intended information within a desired time frame. To ad-dress this problem, we have developed two models: (i) a feature-based model that leverages peoplesfi exhibited social behavior, including the content of their tweets and social interactions, to characterize their willingness and readiness to propagate information on Twitter via the act of retweeting; and (ii) a wait-time model based on a user's previous retweeting wait times to predict her next retweeting time when asked. Based on these two models, we build a recommender system that predicts the likelihood of a stranger to retweet information when asked, within a specific time window, and recommends the top-N qualified strangers to engage with. Our experiments, including live studies in the real world, demonstrate the effectiveness of our work.
- Agarwal, N., Liu, H., Tang, L., and Yu, P. S. Identifying the influential bloggers in a community. In.WSDM, 2008. Google Scholar
Digital Library
- Bakshy, E., Hofman, J. M., Mason, W. A., and Watts, D. J. Everyone's an influencer: quantifying influence on twitter, In WSDM, 2011. Google ScholarDigital Library
- Bakshy, E., Rosenn, I., Marlow, C., and Adamic, L. The role of social network in information diffusion. In WWW, 2012. Google ScholarDigital Library
- Boyd, D., Golder, S., and Lotan, G. Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter. In HICSS, 2010. Google ScholarDigital Library
- Budak, C., Agrawal, D., and El Abbadi, A. Limiting the spread of misinformation in social networks. In WWW, 2011. Google ScholarDigital Library
- Cha, M., Haddadi, H., Benevenuto, F., and Gummadi, K.P. Measuring user influence in twitter: The million follower fallacy. In ICWSM, 2010.Google ScholarCross Ref
- Chaoji, V., Ranu, S., Rastogi, R., and Bhatt, R. Recommendations to boost content spread in social networks., In WWW, 2012. Google ScholarDigital Library
- Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P. SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16: 321--357, 2002. Google ScholarDigital Library
- Chen, J. Cypher, A., Drews, C. and Nichols, J. CrowdE: Filtering Tweets for Direct Customer Engagements. In ICWSM 2013.Google Scholar
- Costa, P.T., and McCrae, R.R. Revised NEO Personality Inventory (NEO-PI-R) and NEO Five-Factor Inventory (NEOFFI) manual. Psychological Assessment Resources, 1992.Google Scholar
- Fast, L. A., and Funder, D. C. Personality as manifest in word use: Correlations with self-report, acquaintance report, and behavior. Journal of Personality and Social Psychology, Vol 94(2), 2008.Google ScholarCross Ref
- Fawcett, T. An introduction to ROC analysis. Pattern Recogn. Lett., Vol 27( 8), 2006. Google ScholarDigital Library
- Gill, A. J., Nowson, S., and Oberlander, J. What Are They Blogging Aboutfi Personality, Topic and Motivation in Blogs, In ICWSM, 2009.Google Scholar
- Goyal, A., Bonchi, F., and Lakshmanan, L. V.S. Learning influence probabilities in social networks. In WSDM, 2010. Google ScholarDigital Library
- Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. The WEKA data mining software: an update. SIGKDD Explorations Newsletter, 11(1): 10--18, 2009. Google ScholarDigital Library
- Hoang, T.-A., and Lim, E.-P. Virality and Susceptibility in Information Diffiusions, In ICWSM 2012.Google Scholar
- Huang, J., Cheng, X.-Q., Shen, H.-W, Zhou, T., and Jin, X. Exploring social influence via posterior effect of word-ofmouth recommendations. In WSDM, 2012. Google ScholarDigital Library
- Lee, K., Caverlee, J., and Webb, S. Uncovering social spammers: social honeypots + machine learning. In SIGIR 2010. Google ScholarDigital Library
- Lee, K., Eoff, B. D., and Caverlee, J. Seven Months with the Devils: A Long-Term Study of Content Polluters on Twitter. In ICWSM, 2011.Google Scholar
- Liu, X.Y., and Zhou, Z.H. The influence of class imbalance on cost-sensitive learning: an empirical study. In ICDM, 2006. Google ScholarDigital Library
- Macskassy, S. A., and Michelson, M. Why Do People Retweetfi Anti-Homophily Wins the Day!. In ICWSM, 2011Google Scholar
- Mahmud, J., Zhou, M., Megiddo, N., Nichols, J., and Drews, C. Recommending Targeted Strangers from Whom to Solicit Information in Twitter. In IUI, 2013. Google ScholarDigital Library
- Nichols, J., and Kang, J-H. Asking Questions of Targeted Strangers on Social Networks. In CSCW, 2012. Google ScholarDigital Library
- Pennebaker, J.W., Francis, M.E., and Booth, R.J. Linguistic Inquiry and Word Count. Erlbaum Publishers, 2001.Google Scholar
- Romero. D. M., Galuba. W., Asur. S, and Huberman, B. A. Influence and passivity in social media. In ECML/PKDD, 2011. Google ScholarDigital Library
- Shannon, C. E., A mathematical theory of communication. Bell system technical journal, Vol 27, 1948.Google Scholar
- Singer, Y. How to win friends and influence people, truthfully: influence maximization mechanisms for social networks, In WSDM, 2012. Google ScholarDigital Library
- Starbird, K. and Palen, L. Pass It Onfi: Retweeting in Mass Emergency, In ISCRAM, 2010.Google Scholar
- Ver Steeg, G. and Galstyan, A. Information transfer in social media. In WWW, 2012. Google ScholarDigital Library
- Weng, J. Lim. E.-P., Jiang. J, and He. Q. Twitterrank: Finding topic-sensitive influential twitterers. In WSDM, 2010. Google ScholarDigital Library
- Yang, Y., and Pedersen, O.J. A Comparative Study on Feature Selection in Text Categorization. In ICML, 1997. Google ScholarDigital Library
- Yarkoni, Tal. Personality in 100,000 words: A large-scale analysis of personality and word usage among bloggers. Journal of Research in Personality, 2010.Google ScholarCross Ref
Index Terms
- Who will retweet this?: Automatically Identifying and Engaging Strangers on Twitter to Spread Information
Recommendations
Who Will Retweet This? Detecting Strangers from Twitter to Retweet Information
Survey Paper, Regular Papers and Special Section on Participatory Sensing and Crowd IntelligenceThere has been much effort on studying how social media sites, such as Twitter, help propagate information in different situations, including spreading alerts and SOS messages in an emergency. However, existing work has not addressed how to actively ...
Who will retweet me?: finding retweeters in twitter
SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrievalAn important aspect of communication in Twitter (and other Social Network is message propagation -- people creating posts for others to share. Although there has been work on modelling how tweets in Twitter are propagated (retweeted), an untackled ...
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 ...
Comments