Skip to main content

Advertisement

SpringerLink
Log in

Predicting the popularity of topics based on user sentiment in microblogging websites

  • Published:
Journal of Intelligent Information Systems Aims and scope Submit manuscript

Abstract

Behavioral economics show us that emotions play an important role in individual behavior and decision-making. Does this also affect collective decision making in a community? Here we investigate whether the community sentiment energy of a topic is related to the spreading popularity of the topic. To compute the community sentiment energy of a topic, we first analyze the sentiment of a user on the key phrases of the topic based on the recent tweets of the user. Then we compute the total sentiment energy of all users in the community on the topic based on the Markov Random Field (MRF) model and graph entropy model. Experiments on two communities find the linear correlation between the community sentiment energy and the real spreading popularity of topics. Based on the finding, we proposed two models to predict the popularity of topics. Experimental results show the effectiveness of the two models and the helpful of sentiment in predicting the popularity of topics. Experiments also show that community sentiment affects collective decision making of spreading a topic or not in the community.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. SINA Weibo. Wikipedia. https://en.wikipedia.org/wiki/Sina_Weibo

  2. Homepage of Jackie Chan on SINA Weibo. http://weibo.com/jackiechan.

  3. Jackie Chan. Wikipedia. http://en.wikipedia.org/wiki/Jackie_Chan.

  4. Homepage of Zhi-Hua Zhou on SINA Weibo. http://weibo.com/zhouzh2012.

  5. Zhi-Hua Zhou’s Homepage. https://cs.nju.edu.cn/zhouzh/.

  6. ANSJ: https://github.com/NLPchina/ansj_seg

  7. Pearson correlation coefficient: https://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient

  8. NLPIR: http://ictclas.nlpir.org/

  9. Student’s t-test. Wikipedia. https://en.wikipedia.org/wiki/Student’s_t-test

  10. Coefficient of determination. Wikipedia. https://en.wikipedia.org/wiki/Coefficient_of_determination

References

  • Anand, K., & Bianconi, G. (2009). Entropy measures for networks: toward an information theory of complex topologies. Physical Review E, 80(4), 045,102.

    Article  Google Scholar 

  • Bao, P., Shen, H.W., Huang, J., & Cheng, X.Q. (2013). Popularity prediction in microblogging network: a case study on sina weibo. In Proceedings of the 22nd international conference on world wide web companion, international world wide web conferences steering committee (pp. 177–178).

  • Barbosa, L., & Feng, J. (2010). Robust sentiment detection on twitter from biased and noisy data. In Proceedings of the 23rd international conference on computational linguistics: posters (pp. 36–44). Association for Computational Linguistics.

  • Bifet, A., & Frank, E. (2010). Sentiment knowledge discovery in twitter streaming data. In Discovery science (pp. 1–15): Springer.

  • Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1–8.

    Article  Google Scholar 

  • Bollen, J., Pepe, A., & Mao, H. (2009). Modeling public mood and emotion: twitter sentiment and socio-economic phenomena. arXiv:0911.1583.

  • Buckley, C. (2004). Topic prediction based on comparative retrieval rankings. In Proceedings of the 27th annual international ACM SIGIR conference on research and development in information retrieval, SIGIR ’04 (pp. 506–507). New York, NY, USA: ACM. https://doi.org/10.1145/1008992.1009093.

  • Crane, R., & Sornette, D. (2008). Robust dynamic classes revealed by measuring the response function of a social system. Proceedings of the National Academy of Sciences, 105(41), 15,649–15,653.

    Article  Google Scholar 

  • Cruz, J.D., Bothorel, C., & Poulet, F. (2011). Entropy based community detection in augmented social networks. In International conference on computational aspects of social networks (cason), 2011 (pp. 163–168). IEEE.

  • Damasio, A. (1995). DESCARTES’ERROR: emotion, reason, and the human brain. Optometry & Vision Science, 72(11), 847–848.

    Article  Google Scholar 

  • Dancey, C.P., & Reidy, J. (2007). Statistics without maths for psychology. London: Pearson Education.

    Google Scholar 

  • Davidov, D., Tsur, O., & Rappoport, A. (2010). Enhanced sentiment learning using twitter hashtags and smileys. In Proceedings of the 23rd international conference on computational linguistics: posters (pp. 241–249). Association for Computational Linguistics.

  • Diakopoulos, N.A., & Shamma, D.A. (2010). Characterizing debate performance via aggregated twitter sentiment. In Proceedings of the SIGCHI conference on human factors in computing systems, CHI ’10 (pp. 1195–1198). New York, NY, USA: ACM. https://doi.org/10.1145/1753326.1753504.

  • Dolan, R.J. (2002). Emotion, cognition, and behavior. Science, 298(5596), 1191–1194.

    Article  Google Scholar 

  • Figueiredo, F., Benevenuto, F., & Almeida, J.M. (2011). The tube over time: characterizing popularity growth of youtube videos. In Proceedings of the fourth ACM international conference on web search and data mining, WSDM ’11 (pp. 745–754). New York, NY, USA: ACM. https://doi.org/10.1145/1935826.1935925.

  • Gao, S., Ma, J., & Chen, Z. (2014). Effective and effortless features for popularity prediction in microblogging network. In Proceedings of the companion publication of the 23rd international conference on world wide web companion, WWW companion ’14 (pp. 269–270). Switzerland: International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva. https://doi.org/10.1145/2567948.2577312.

  • Hong, L., Dan, O., & Davison, B.D. (2011). Predicting popular messages in twitter. In Proceedings of the 20th international conference companion on world wide web (pp. 57–58). ACM.

  • Kindermann, R., Snell, J.L., et al. (1980). Markov random fields and their applications Vol. 1. Providence: American Mathematical Society.

    Book  MATH  Google Scholar 

  • Körner, J. (1973). Coding of an information source having ambiguous alphabet and the entropy of graphs. In 6th prague conference on information theory (pp. 411–425).

  • Lee, J.G., Moon, S., & Salamatian, K. (2010). An approach to model and predict the popularity of online contents with explanatory factors. In IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology (WI-IAT), 2010 (Vol. 1, pp. 623–630). IEEE.

  • Lerman, K., & Hogg, T. (2010). Using a model of social dynamics to predict popularity of news. In Proceedings of the 19th international conference on world wide web (pp. 621–630). ACM.

  • Li, S.Z. (2009). Markov random field modeling in image analysis. New York: Springer Science & Business Media.

    MATH  Google Scholar 

  • Metzler, D., & Croft, W.B. (2005). A markov random field model for term dependencies. In Proceedings of the 28th annual international ACM SIGIR conference on research and development in information retrieval (pp. 472–479). ACM.

  • Pinto, H., Almeida, J.M., & Gonçalves, M.A. (2013). Using early view patterns to predict the popularity of youtube videos. In Proceedings of the sixth ACM international conference on web search and data mining, WSDM ’13 (pp. 365–374). New York, NY, USA: ACM. https://doi.org/10.1145/2433396.2433443.

  • Read, J. (2005). Using emoticons to reduce dependency in machine learning techniques for sentiment classification. In Proceedings of the ACL student research workshop (pp. 43–48). Association for Computational Linguistics.

  • Salganik, M.J., Dodds, P.S., & Watts, D.J. (2006). Experimental study of inequality and unpredictability in an artificial cultural market. Science, 311(5762), 854–856.

    Article  Google Scholar 

  • Shannon, C.E. (2001). A mathematical theory of communication. ACM SIGMOBILE Mobile Computing and Communications Review, 5(1), 3–55.

    Article  MathSciNet  Google Scholar 

  • Simonyi, G. (1995). Graph entropy: a survey. Combinatorial Optimization, 20, 399–441.

    Article  MathSciNet  MATH  Google Scholar 

  • Szabo, G., & Huberman, B.A. (2010). Predicting the popularity of online content. Communications of the ACM, 53(8), 80–88.

    Article  Google Scholar 

  • Thelwall, M., Buckley, K., & Paltoglou, G. (2011). Sentiment in twitter events. Journal of the American Society for Information Science and Technology, 62(2), 406–418.

    Article  Google Scholar 

  • Tong, H., Liu, Y., Peng, H., & Tang, J. (2008). Internet users’ psychosocial attention prediction: web hot topic prediction based on adaptive ar model. In International conference on computer science and information technology, 2008. ICCSIT’08 (pp. 458–462). IEEE.

  • Tumasjan, A., Sprenger, T.O., Sandner, P.G., & Welpe, I.M. (2010). Predicting elections with twitter: what 140 characters reveal about political sentiment. ICWSM, 10, 178–185.

    Google Scholar 

  • Wikipedia (2015). Pearson product-moment correlation coefficient. https://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient.

  • Wu, T., Timmers, M., Vleeschauwer, D.D., & Leekwijck, W.V. (2010). On the use of reservoir computing in popularity prediction. In Second international conference on evolving internet (INTERNET), 2010 (pp. 19–24). IEEE.

  • Yin, P., Luo, P., Wang, M., & Lee, W.C. (2012). A straw shows which way the wind blows: ranking potentially popular items from early votes. In Proceedings of the fifth ACM international conference on web search and data mining (pp. 623–632). ACM.

Download references

Acknowledgements

This work is supported by National Defense Science and Technology Project Funds (Grant No. 3101283) National Natural Science Foundation of China (Grant No. 61502517).

Author information

Authors and Affiliations

  1. Academy of Ocean Science and Engineering, National University of Defense Technology, Changsha, 410073, China

    Xiang Wang & Min Zhu

  2. College of Computer, National University of Defense Technology, Changsha, 410073, China

    Xiang Wang, Chen Wang & Jiumin Huang

  3. College of Information System and Management, National University of Defense Technology, Changsha, 410073, China

    Zhaoyun Ding

Authors
  1. Xiang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chen Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhaoyun Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Min Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jiumin Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiang Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, X., Wang, C., Ding, Z. et al. Predicting the popularity of topics based on user sentiment in microblogging websites. J Intell Inf Syst 51, 97–114 (2018). https://doi.org/10.1007/s10844-017-0486-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10844-017-0486-z

Keywords

Search

Navigation