Abstract
In this work, we tackle the problem of predicting entity popularity on Twitter based on the news cycle. We apply a supervised learning approach and extract four types of features: (i) signal, (ii) textual, (iii) sentiment and (iv) semantic, which we use to predict whether the popularity of a given entity will be high or low in the following hours. We run several experiments on six different entities in a dataset of over 150M tweets and 5M news and obtained F1 scores over 0.70. Error analysis indicates that news perform better on predicting entity popularity on Twitter when they are the primary information source of the event, in opposition to events such as live TV broadcasts, political debates or football matches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Dataset is available for research purposes. Access requests via e-mail.
References
Saleiro, P., Teixeira, J., Soares, C., Oliveira, E.: TimeMachine: entity-centric search and visualization of news archives. In: Ferro, N., Crestani, F., Moens, M.-F., Mothe, J., Silvestri, F., Nunzio, G.M., Hauff, C., Silvello, G. (eds.) ECIR 2016. LNCS, vol. 9626, pp. 845–848. Springer, Heidelberg (2016). doi:10.1007/978-3-319-30671-1_78
Asur, S., Bandari, R., Huberman, B.: The pulse of news in social media: forecasting popularity. In: ICWSM 2012 (2012)
Yang, J., Leskovec, J.: Patterns of temporal variation in online media. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 177–186. ACM (2011)
Weerkamp, W., Tsagkias, M., De Rijke, M.: Predicting the volume of comments on online news stories. In: CIKM 2009, pp. 1765–1768. ACM (2009)
He, X., Gao, M., Kan, M.-Y., Liu, Y., Sugiyama, K.: Predicting the popularity of web 2.0 items based on user comments. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 233–242. ACM (2014)
Gottipati, S., Jiang, J.: Finding thoughtful comments from social media. In: COLING, pp. 995–1010 (2012)
Louis, A., Nenkova, A.: What makes writing great? First experiments on article quality prediction in the science journalism domain. Trans. Assoc. Comput. Linguist. 1, 341–352 (2013)
Castillo, C., El-Haddad, M., Pfeffer, J., Stempeck, M.: Characterizing the life cycle of online news stories using social media reactions. In: Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing, pp. 211–223. ACM (2014)
Crane, R., Sornette, D.: Robust dynamic classes revealed by measuring the response function of a social system. Proc. Nat. Acad. Sci. 105(41), 15649–15653 (2008)
Lehmann, J., Gonçalves, B., Ramasco, J.J., Cattuto, C.: Dynamical classes of collective attention in Twitter. In: Proceedings of the 21st International Conference on World Wide Web, pp. 251–260. ACM (2012)
Romero, D.M., Meeder, B., Kleinberg, J.: Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on Twitter. In: Proceedings of the 20th International Conference on World Wide Web, pp. 695–704. ACM (2011)
Tsytsarau, M., Palpanas, T., Castellanos, M.: Dynamics of news events and social media reaction. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 901–910. ACM (2014)
Reis, J., Olmo, P., Benevenuto, F., Kwak, H., Prates, R., An, J.: Breaking the news: first impressions matter on online news. In: ICWSM 2015 (2015)
Boanjak, M., Oliveira, E., Martins, J., Rodrigues, E.M., Sarmento, L.: TwitterEcho: a distributed focused crawler to support open research with twitter data. In: WWW 2012, pp. 1233–1240. ACM (2012)
Saleiro, P., Rei, L., Pasquali, A., Soares, C.: Popstar at replab 2013: name ambiguity resolution on Twitter. In: CLEF 2013 Eval. Labs and Workshop Online Working Notes (2013)
Saleiro, P., Amir, S., Silva, M., Soares, C.: Popmine: tracking political opinion on the web. In: 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM), pp. 1521–1526. IEEE (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Saleiro, P., Soares, C. (2016). Learning from the News: Predicting Entity Popularity on Twitter. In: Boström, H., Knobbe, A., Soares, C., Papapetrou, P. (eds) Advances in Intelligent Data Analysis XV. IDA 2016. Lecture Notes in Computer Science(), vol 9897. Springer, Cham. https://doi.org/10.1007/978-3-319-46349-0_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-46349-0_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46348-3
Online ISBN: 978-3-319-46349-0
eBook Packages: Computer ScienceComputer Science (R0)