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

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