Abstract
At present, news channels are using social media to disseminate news to a large audience. These news channels try to convey the news in such a way that attracts more user interaction in the form of views, likes, comments, and shares. In online news, one of the important factors in getting higher user interaction is to recommend the top news articles that would attract more number of users to give opinion, especially in the form of comments. When a news article starts getting many comments, it automatically attracts other readers to participate in the discussion. We say that a news article has “high-arousal” content if it can attract more number of comments from users. When a new news article is written it has no user interaction information, such as number of views, likes, shares or comments. In this paper, our aim is to predict news articles which have higher potential to generate high-arousal. In other words, they would attract a large number of users to give opinion in the form of comments. Unlike previous studies, we predict the arousal of news articles prior to their release, which brings the possibility of appropriate decision making to modify the article content or its ranking in audience newsfeed. We generate multiple features from the content of news articles and show that our best set of features can predict the arousal with an accuracy of 81%. We perform our experiments on social media page of CNN news channel, containing four years of data with 33,324 news articles and 226.83 million reactions.
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
References
Aggarwal, C.C.: Recommender Systems. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-29659-3
Ahmed, M., Spagna, S., Huici, F., Niccolini, S.: A peek into the future: predicting the evolution of popularity in user generated content. In: WSDM, pp. 607–616. ACM (2013)
Bandari, R., Asur, S., Huberman, B.A.: The pulse of news in social media: forecasting popularity. In: ICWSM, vol. 12, pp. 26–33 (2012)
Bucher, T.: Want to be on the top? Algorithmic power and the threat of invisibility on Facebook. New Media Soc. 14(7), 1164–1180 (2012)
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)
Das, A.S., Datar, M., Garg, A., Rajaram, S.: Google news personalization: scalable online collaborative filtering. In: WWW, pp. 271–280. ACM (2007)
Dietterich, T.G.: Ensemble learning. In: The Handbook of Brain Theory and Neural Networks, vol. 2, pp. 110–125 (2002)
Esiyok, C., Kille, B., Jain, B.J., Hopfgartner, F., Albayrak, S.: Users’ reading habits in online news portals. In: Proceedings of the 5th Information Interaction in Context Symposium, pp. 263–266. ACM (2014)
Figueiredo, F., Pinto, H., BeléM, F., Almeida, J., GonçAlves, M., Fernandes, D., Moura, E.: Assessing the quality of textual features in social media. Inf. Process. Manage. 49(1), 222–247 (2013)
Kim, C., Yang, S.U.: Like, comment, and share on facebook: how each behavior differs from the other. Public Relat. Rev. 43(2), 441–449 (2017)
Lee, J.: The double-edged sword: the effects of journalists’ social media activities on audience perceptions of journalists and their news products. J. Comput.-Mediated Commun. 20(3), 312–329 (2015)
Lee, J.G., Moon, S., Salamatian, K.: An approach to model and predict the popularity of online contents with explanatory factors. In: 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 1, pp. 623–630. IEEE (2010)
Lin, C., Xie, R., Guan, X., Li, L., Li, T.: Personalized news recommendation via implicit social experts. Inf. Sci. 254, 1–18 (2014)
Liu, J., Dolan, P., Pedersen, E.R.: Personalized news recommendation based on click behavior. In: Proceedings of the 15th International Conference on Intelligent User Interfaces, pp. 31–40. ACM (2010)
Lusa, L., et al.: Smote for high-dimensional class-imbalanced data. BMC Bioinformatics 14(1), 106 (2013)
Manning, C.: Information extraction and named entity recognition (2012)
Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J.R., Bethard, S., McClosky, D.: The Stanford CoreNLP natural language processing toolkit. In: ACL (System Demonstrations), pp. 55–60 (2014)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
Naveed, N., Gottron, T., Kunegis, J., Alhadi, A.C.: Bad news travel fast: a content-based analysis of interestingness on twitter. In: Proceedings of the 3rd International Web Science Conference, p. 8. ACM (2011)
Petrovic, S., Osborne, M., Lavrenko, V.: Rt to win! Predicting message propagation in twitter. ICWSM 11, 586–589 (2011)
Rokach, L.: Ensemble-based classifiers. Artif. Intell. Rev. 33(1), 1–39 (2010)
Suh, B., Hong, L., Pirolli, P., Chi, E.H.: Want to be retweeted? Large scale analytics on factors impacting retweet in Twitter network. In: 2010 IEEE Second International Conference on Social computing (socialcom), pp. 177–184. IEEE (2010)
Tatar, A., Antoniadis, P., De Amorim, M.D., Fdida, S.: From popularity prediction to ranking online news. Soc. Network Anal. Min. 4(1), 1–12 (2014)
Tatar, A., Leguay, J., Antoniadis, P., Limbourg, A., de Amorim, M.D., Fdida, S.: Predicting the popularity of online articles based on user comments. In: Proceedings of the International Conference on Web Intelligence, Mining and Semantics, p. 67. ACM (2011)
Tsagkias, M., De Rijke, M., Weerkamp, W.: Linking online news and social media. In: WSDM, pp. 565–574. ACM (2011)
Weaver, J., Tarjan, P.: Facebook linked data via the graph API. Semant. Web 4(3), 245–250 (2013)
Weng, L., Menczer, F., Ahn, Y.Y.: Predicting successful memes using network and community structure. In: ICWSM (2014)
Wu, S., Tan, C., Kleinberg, J.M., Macy, M.W.: Does bad news go away faster? In: ICWSM. Citeseer (2011)
Yano, T., Smith, N.A.: What’s worthy of comment? Content and comment volume in political blogs. In: ICWSM (2010)
Zaman, T.R., Herbrich, R., Van Gael, J., Stern, D.: Predicting information spreading in Twitter. In: Workshop on Computational Social Science and the Wisdom of Crowds, Nips, vol. 104, pp. 17599–601. Citeseer (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Kumar, N., Yadandla, A., Suryamukhi, K., Ranabothu, N., Boya, S., Singh, M. (2017). Arousal Prediction of News Articles in Social Media. In: Ghosh, A., Pal, R., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2017. Lecture Notes in Computer Science(), vol 10682. Springer, Cham. https://doi.org/10.1007/978-3-319-71928-3_30
Download citation
DOI: https://doi.org/10.1007/978-3-319-71928-3_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-71927-6
Online ISBN: 978-3-319-71928-3
eBook Packages: Computer ScienceComputer Science (R0)