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

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.Comment: Accepted at the International Conference on Web and Social Media (ICWSM 2020); 11 pages; code and data are available at https://hpi.de/naumann/projects/repeatability/text-mining.htm

Toxicity

In research on online comments on social media platforms, different terms are widely used to describe comments that are hateful or disrespectful and thereby poison a discussion. This chapter takes a theoretical perspective on the term toxicity and related research in the field of computer science. More specifically, it explains the usage of the term and why its exact interpretation depends on the platform in question. Further, the article discusses the advantages of toxicity over other terms and provides an overview of the available toxic comment datasets. Finally, it introduces the concept of engaging comments as the counterpart of toxic comments, leading to a task that is complementary to the prevention and removal of toxic comments: the fostering and highlighting of engaging comments

Gender Effects in Online Low-Threshold Evaluations: Evidence from a Large-Scale Online Discussion-based Community

Online communities thrive on the basis of interactions between like-minded individuals, and usually involve some form of feedback or evaluations by peers. In these contexts, there is systematic evidence of gender-based biases in evaluations. How can such biases be attenuated? We study the efficacy of one approach—anonymization of gender information on the community. We use data from a large-scale digital discussion platform, Political Science Rumors, to examine the presence of gender bias. When users on the community post a discussion message, they are randomly assigned a pseudonym in the form of a given (or first name), such as “Daniel” or “Haylee,” and each post subsequently garners positive and negative votes from readers. We analyze the up votes, down votes, and net votes garnered by 1.4 million posts where names are randomly assigned to posters. We find that posts from randomly assigned “female” names receive 2.5% lower evaluation scores, all else equal. Further, when “female” users post emotive content with a negative tone, the posts receive disproportionately more negative evaluations

Challenges and perspectives of hate speech research

This book is the result of a conference that could not take place. It is a collection of 26 texts that address and discuss the latest developments in international hate speech research from a wide range of disciplinary perspectives. This includes case studies from Brazil, Lebanon, Poland, Nigeria, and India, theoretical introductions to the concepts of hate speech, dangerous speech, incivility, toxicity, extreme speech, and dark participation, as well as reflections on methodological challenges such as scraping, annotation, datafication, implicity, explainability, and machine learning. As such, it provides a much-needed forum for cross-national and cross-disciplinary conversations in what is currently a very vibrant field of research