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Ralf Krestel

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ICWSM 20

Top Comment or Flop Comment? Predicting and Explaining User Engagement in Online News Discussions

Abstract

Comment sections below online news articles enjoy growing popularity among readers. However, the overwhelming number of comments makes it infeasible for the average news consumer to read all of them and hinders engaging discussions. Most platforms display comments in chronological order, which neglects that some of them are more relevant to users and are better conversation starters. In this paper, we systematically analyze user engagement in the form of the upvotes and replies that a comment receives. Based on comment texts, we train a model to distinguish comments that have either a high or low chance of receiving many upvotes and replies. Our evaluation on user comments from TheGuardian.com compares recurrent and convolutional neural network models, and a traditional feature-based classifier. Further, we investigate what makes some comments more engaging than others. To this end, we identify engagement triggers and arrange them in a taxonomy. Explanation methods for neural networks reveal which input words have the strongest influence on our model's predictions. In addition, we evaluate on a dataset of product reviews, which exhibit similar properties as user comments, such as featuring upvotes for helpfulness.

Full Paper

ICWSM20.pdf

Conference Homepage

ICWSM 2020

BibTex Entry

News

Watch our new MOOC in German about hate and fake in the Internet ("Trolle, Hass und Fake-News: Wie können wir das Internet retten?") on openHPI (link).

New Publication

Our work on Predicting and Explaining User Engagement in Online News Discussions was accepted as full paper at ICWSM 2020.

New Photos

I added some photos from my trip to Hildesheim - UNESCO World Heritage Site.

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