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Title: Cannot Predict Comment Volume of a News Article before (a few) Users Read It
Many news outlets allow users to contribute comments on topics about daily world events. News articles are the seeds that spring users' interest to contribute content, i.e., comments. An article may attract an apathetic user engagement (several tens of comments) or a spontaneous fervent user engagement (thousands of comments). In this paper, we study the problem of predicting the total number of user comments a news article will receive. Our main insight is that the early dynamics of user comments contribute the most to an accurate prediction, while news article specific factors have surprisingly little influence. This appears to be an interesting and understudied phenomenon: collective social behavior at a news outlet shapes user response and may even downplay the content of an article. We compile and analyze a large number of features, both old and novel from literature. The features span a broad spectrum of facets including news article and comment contents, temporal dynamics, sentiment/linguistic features, and user behaviors. We show that the early arrival rate of comments is the best indicator of the eventual number of comments. We conduct an in-depth analysis of this feature across several dimensions, such as news outlets and news article categories. We show that the relationship between the early rate and the final number of comments as well as the prediction accuracy vary considerably across news outlets and news article categories (e.g., politics, sports, or health).  more » « less
Award ID(s):
1838145
NSF-PAR ID:
10292520
Author(s) / Creator(s):
Date Published:
Journal Name:
Proceedings of the International AAAI Conference on Weblogs and Social Media
ISSN:
2162-3449
Page Range / eLocation ID:
173-184
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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