Abstract
Twitter plays an important role in today social network. Its key mechanism is retweet that disseminates information to broad audiences within a very short time and help increases the popularity of the social content. Therefore, an effective model for predicting the popularity of tweets is required in various domains such as news propagation, viral marketing, personalized message recommendation, and trend analysis. Although many studies have been extensively researched on predicting the popularity of tweets, they mainly focus on the content-based and the author-based features, while retweeter-based features are less concerned. This paper aims to study the impact of influential users who retweet tweets, also called retweeters, and presents simple yet effective measures for predicting the influence of retweeters on the popularity of online news tweets. By analyzing the popularity of news tweets and the impact of the retweeters, a number of useful measures are defined to evaluate influence of users in the retweeter network, and used to establish the prediction model. The experimental results show that the application of the retweeter-based features is highly effective and enhances the performance of the prediction model with high accuracy.
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References
Bao, P., Shen, H.-W., Huang, J., Cheng, X.-Q.: Popularity prediction in microblogging network: a case study on Sina Weibo. In: WWW 2013 (2013)
Bigonha, C.A., Cardoso, T.N., Moro, M.M., Goncalves, M.A., Almeida, V.: Sentiment-based influence detection on Twitter. J. Braz. Comp. Soc. 18(3), 169–183 (2012)
Cappelletti, R., Sastry, N.: IARank: ranking users on Twitter in near real-time, based on their information amplification potential. In: Social Informatics 2012, Washington, DC, USA, pp. 70–77 (2012)
Fabián, R.: Measuring user influence on Twitter: a survey (2015). arXiv:1508.07951
Gayo-Avello, D.: Nepotistic relationships in Twitter and their impact on rank prestige algorithms. Inf. Process. Manag. 49(6), 1250–1280 (2013)
Goel, S., Watts, D.J., Goldstein, D.G.: The structure of online diffusion networks. In: EC 2012, New York, USA (2012)
Hajian, B., White, T.: Modelling influence in a social network: metrics and evaluation. In: PASSAT/SocialCom 2011, Boston, MA, USA (2011)
Hong, L., Dan, O., Davison, B.D.: Predicting popular messages in twitter. In: WWW 2011 (2011)
Khrabrov, A., Cybenko, G.: Discovering influence in communication networks using dynamic graph analysis. In: PASSAT 2010, Minneapolis, Minnesota, USA (2010)
Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: WWW 2010, New York, USA (2010)
Lee, C., Kwak, H., Park, H., Moon, S.B.: Finding influentials based on the temporal order of information adoption in Twitter. In: WWW 2010, Raleigh, North Carolina, USA (2010)
Ma, Z., Sun, A., Cong, G.: On predicting the popularity of newly emerging hashtags in Twitter. J. Am. Soc. Inf. Sci. Technol. 64(7), 641399–641410 (2013)
Messias, J., Schmidt, L., Oliveira, R., Benevenuto, F.: You followed my bot! Transforming robots into influential users in Twitter. First Monday 18(7) (2013)
Nagmoti, R., Teredesai, A., Cock, M.D.: Ranking approaches for microblog search. In: WI 2010, Toronto, Canada (2010)
Neves, A., Vieira, R., Mourao, F., Rocha, L.: Quantifying complementarity among strategies for influeners’ detection on Twitter. In: ICCS 2015 (2015)
Noro, T., Ru, F., Xiao, F., Tokuda, T.: Twitter user rank using keyword search. In: 22nd European-Japanese Conference on Information Modelling, pp. 31–48. IOS Press, Prague (2012)
Petrovic, S., Osborne, M., Lavrenko, V.: RT to Win! Predicting message propagation in Twitter. In: ICWSM 2011 (2011)
Romero, D.M., Galuba, W., Asur, S., Huberman, B.A.: Influence and passivity in social media. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part III. LNCS, vol. 6913, pp. 18–33. Springer, Heidelberg (2011)
Srinivasan, M.S., Srinivasa, S., Thulasidasan, S.: Exploring celebrity dynamics on Twitter. In: I-CARE 2013, Hyderabad, India (2013)
Suh, B., Hong, L., Pirolli, P., Chi, E.H.: Want to be retweeted? Large scale analytics on factors impacting retweet in Twitter network. In: SOCIALCOM 2010 (2010)
Szabo, G., Huberman, B.A.: Predicting the popularity of online content. Commun. ACM 53(8), 80–88 (2010)
Yin, Z., Zhang, Y.: Measuring pair-wise social influence in microblog. In: ASE/IEEE International conference on Social Computing and 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust (2012)
Yuan, J., Li, L., Huang, L.L.: Topology-based algorithm for users’ influence on specific topics in micro-blog. J. Inf. Comput. Sci. 10(8), 2247–2259 (2013)
Zaman, T., Fox, E.B., Bradlow, E.T.: A Bayesian Approach For Predicting The Popularity Of Tweets. MIT, Cambridge (2014)
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Maleewong, K. (2016). An Analysis of Influential Users for Predicting the Popularity of News Tweets. In: Booth, R., Zhang, ML. (eds) PRICAI 2016: Trends in Artificial Intelligence. PRICAI 2016. Lecture Notes in Computer Science(), vol 9810. Springer, Cham. https://doi.org/10.1007/978-3-319-42911-3_26
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DOI: https://doi.org/10.1007/978-3-319-42911-3_26
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