The Pulse of News in Social Media: Forecasting Popularity

By Roja Bandari, Sitaram Asur, Bernardo A. Huberman. International Conference on Web and Social Media, 2012

453 citations recorded by the source corpus.

News articles are extremely time sensitive by nature. There is also intense competition among news items to propagate as widely as possible. Hence, the task of predicting the popularity of news items on the social web is both interesting and challenging. Prior research has dealt with predicting eventual online popularity based on early popularity. It is most desirable, however, to predict the popularity of items prior to their release, fostering the possibility of appropriate decision making to modify an article and the manner of its publication. In this paper, we construct a multi-dimensional feature space derived from properties of an article and evaluate the efficacy of these features to serve as predictors of online popularity. We examine both regression and classification algorithms and demonstrate that despite randomness in human behavior, it is possible to predict ranges of popularity on twitter with an overall 84% accuracy. Our study also serves to illustrate the differences between traditionally prominent sources and those immensely popular on the social web.

Subfields: Information Systems, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/984d1f4522443f0ad6571d342c1b3088420fb4ec

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Citations in the corpus

Modeling Interestingness with Deep Neural Networks (2014, unknown): “There are many systems that identify popular content in the Web or recommend content (e.g., Bandari et al. 2012; Lerman and Hogg 2010; Szabo and Huberman 2010), which is closely re-lated to the highlighting task.”
A Proactive Intelligent Decision Support System for Predicting the Popularity of Online News (2015, methodology): “Similarly to what is executed in [12,3,7], we assume a binary classification task, where an article is considered “popular” if the number of shares is higher than a fixed decision threshold (D1), else it is considered “unpopular”.”
News Sharing in Social Media: A Review of Current Research on News Sharing Users, Content, and Networks (2015, background): “Research on content characteristics also supports this ideal type by identifying factors like source credibility and a high informational value as drivers of news sharing (e.g., Bandari et al., 2012; Rudat et al., 2014).”
The emergence of network media logic in political communication: A theoretical approach (2015, background): “Bandari et al. (2012) have developed criteria to forecast the popularity of news on Twitter, and found that traditionally prominent news sources differ from news sources that are popular on social media platforms. Distribution on social media platforms is built on the logic of virality, which can be”