Shuai Gao
ABSTRACT
Popularity prediction on microblogging platforms aims to predict the future popularity of a message based on its retweeting dynamics in the early stages. Existing works mainly focus on exploring effective features for prediction, while ignoring the underlying arrival process of retweets. Also, the effect of user activity variation on the retweeting dynamics in the early stages has been neglected. In this paper, we propose an extended reinforced Poisson process model with time mapping process to model the retweeting dynamics and predict the future popularity. The proposed model explicitly characterizes the process through which a message gain its retweets, by capturing a power-law temporal relaxation function corresponding to the aging in the ability of the message to attract new retweets and an exponential reinforcement mechanism characterizing the "richer-get-richer" phenomenon. Further, we introduce the notation of weibo time and integrate a time mapping process into the proposed model to eliminate the effect of user activity variation. Extensive experiments on two Weibo datasets, with 10K and 18K messages respectively, well demonstrate the effectiveness of our proposed model in popularity prediction.
- M. Ahmed, S. Spagna, F. Huici, and S. Niccolini. A peek into the future: predicting the evolution of popularity in user generated content. In WSDM, pages 607--616, 2013. Google Scholar
Digital Library
- S. Asur and B. A. Huberman. Predicting the future with social media. In WI-IAT, volume 1, pages 492--499, 2010. Google ScholarDigital Library
- R. Bandari, S. Asur, and B. A. Huberman. The pulse of news in social media: Forecasting popularity. In ICWSM, 2012.Google Scholar
- P. Bao, H.-W. Shen, J. Huang, and X.-Q. Cheng. Popularity prediction in microblogging network: a case study on sina weibo. In WWW, pages 177--178, 2013. Google ScholarDigital Library
- J. Cheng, L. Adamic, P. A. Dow, J. M. Kleinberg, and J. Leskovec. Can cascades be predicted? In WWW, pages 925--936, 2014. Google ScholarDigital Library
- R. Crane and D. Sornette. Robust dynamic classes revealed by measuring the response function of a social system. PNAS, 105(41):15649--15653, 2008.Google ScholarCross Ref
- F. Figueiredo. On the prediction of popularity of trends and hits for user generated videos. In WSDM, pages 741--746, 2013. Google ScholarDigital Library
- S. Gao, J. Ma, and Z. Chen. Effective and effortless features for popularity prediction in microblogging network. In WWW, pages 269--270, 2014. Google ScholarDigital Library
- M. Gomez-Rodriguez, J. Leskovec, et al. Modeling information propagation with survival theory. In ICML, pages 666--674, 2013.Google Scholar
- M. Gupta, J. Gao, C. Zhai, and J. Han. Predicting future popularity trend of events in microblogging platforms. ASIST, 49(1):1--10, 2012.Google ScholarCross Ref
- L. Hong, O. Dan, and B. D. Davison. Predicting popular messages in twitter. In WWW, pages 57--58, 2011. Google ScholarDigital Library
- A. Kupavskii, L. Ostroumova, A. Umnov, S. Usachev, P. Serdyukov, G. Gusev, and A. Kustarev. Prediction of retweet cascade size over time. In CIKM, pages 2335--2338, 2012. Google ScholarDigital Library
- H. Kwak, C. Lee, H. Park, and S. Moon. What is twitter, a social network or a news media? In WWW, pages 591--600, 2010. Google ScholarDigital Library
- K. Lerman and T. Hogg. Using a model of social dynamics to predict popularity of news. In WWW, pages 621--630, 2010. Google ScholarDigital Library
- H. Li, X. Ma, F. Wang, J. Liu, and K. Xu. On popularity prediction of videos shared in online social networks. In CIKM, pages 169--178, 2013. Google ScholarDigital Library
- F. M. Lilian Weng and Y.-Y. Ahn. Virality prediction and community structure in social networks. Scientific reports, 3, 2013.Google Scholar
- S. Lin, X. Kong, and P. S. Yu. Predicting trends in social networks via dynamic activeness model. In CIKM, pages 1661--1666, 2013. Google ScholarDigital Library
- Z. Ma, A. Sun, and G. Cong. On predicting the popularity of newly emerging hashtags in twitter. JASIST, 2013.Google ScholarCross Ref
- Y. Matsubara, Y. Sakurai, B. A. Prakash, L. Li, and C. Faloutsos. Rise and fall patterns of information diffusion: Model and implications. In KDD, pages 6--14, 2012. Google ScholarDigital Library
- M. McGlohon, J. Leskovec, C. Faloutsos, M. Hurst, and N. Glance. Finding patterns in blog shapes and blog evolution. In ICWSM, 2007.Google Scholar
- H. Pinto, J. M. Almeida, and M. A. Gonçalves. Using early view patterns to predict the popularity of youtube videos. In WSDM, pages 365--374, 2013. Google ScholarDigital Library
- S. Redner. Citation statistics from 110 years of physical review. PHYSICS TODAY, 58(6):49--54, 2005.Google ScholarCross Ref
- H.-W. Shen, D. Wang, C. Song, and A.-L. Barabási. Modeling and predicting popularity dynamics via reinforced poisson processes. In AAAI, 2014.Google ScholarDigital Library
- B. Suh, L. Hong, P. Pirolli, and E. H. Chi. Want to be retweeted? large scale analytics on factors impacting retweet in twitter network. In Socialcom, pages 177--184, 2010. Google ScholarDigital Library
- G. Szabo and B. A. Huberman. Predicting the popularity of online content. Commun. ACM, 53:80--88, 2010. Google ScholarDigital Library
- M. Tsagkias, W. Weerkamp, and M. De Rijke. Predicting the volume of comments on online news stories. In CIKM, pages 1765--1768, 2009. Google ScholarDigital Library
- O. Tsur and A. Rappoport. What's in a hashtag?: content based prediction of the spread of ideas in microblogging communities. In WSDM, pages 643--652, 2012. Google ScholarDigital Library
- A. Tumasjan, T. O. Sprenger, P. G. Sandner, and I. M. Welpe. Predicting elections with twitter: What 140 characters reveal about political sentiment. ICWSM, 10:178--185, 2010.Google ScholarCross Ref
- D. Wang, C. Song, and A.-L. Barabási. Quantifying long-term scientific impact. Science, 342(6154):127--132, 2013.Google ScholarCross Ref
- C. Williams and G. Gulati. What is a social network worth? facebook and vote share in the 2008 presidential primaries. American Political Science Association, 2008.Google Scholar
- J. Yang and J. Leskovec. Patterns of temporal variation in online media. In WSDM, pages 177--186, 2011. Google ScholarDigital Library
- P. Zhang, X. Wang, and B. Li. On predicting twitter trend: Factors and models. In ASONAM, 2013. Google ScholarDigital Library
Index Terms
- Modeling and Predicting Retweeting Dynamics on Microblogging Platforms
Recommendations
Modeling and Predicting Retweeting Dynamics via a Mixture Process
WWW '16 Companion: Proceedings of the 25th International Conference Companion on World Wide WebModeling and predicting retweeting dynamics in social media has important implications to an array of applications. Existing models either fail to model the triggering effect of retweeting dynamics, e.g., the model based on reinforced Poisson process, ...
An extended spreading activation technique for hashtag recommendation in microblogging platforms
WIMS '17: Proceedings of the 7th International Conference on Web Intelligence, Mining and SemanticsMicroblogging platforms such as Twitter have become immensely popular. Users of these platforms use hashtags to gather similar posts under one umbrella. However, the percentage of tweets containing hashtags is still small, and hashtag use is very ...
Modeling and Predicting Popularity Dynamics of Microblogs using Self-Excited Hawkes Processes
WWW '15 Companion: Proceedings of the 24th International Conference on World Wide WebThe ability to model and predict the popularity dynamics of individual user generated items on online media has important implications in a wide range of areas. In this paper, we propose a probabilistic model using a Self-Excited Hawkes Process (SEHP) ...
Comments