Context In the *Age of the Internet* we humans as a species have become increasingly connected with each other. Unfortunately, that's not always a good thing. Sometimes we end up inadvertently connecting with people we'd really rather not talk to at all and it ruins our day. In fact, trolls abound on the internet and its become increasingly difficult to find quality online discussions. Many online publishers simply do not allow commenting because of how easy it is for a few trolls to derail an otherwise illuminating discussion. But maybe we can fix all that with the power of data science. Content The dataset is a csv of about 30k reddit comments made in /r/science between Jan 2017 and June 2018. 10k of the comments were removed by moderators; the original text for these comments was recovered using the pushshift.io API. Each comment is a top-level reply to the parent post and has a comment score of 14 or higher. Acknowledgements The dataset comes from Google BigQuery, Reddit, and Pushshift.io. Thanks to Jesper Wrang of [removeddit](https://removeddit.com/) for advising on how to construct the dataset. Thanks to Jigsaw for hosting the Toxic Comment Classification Kaggle Challenge -- from which I learned a lot about NLP Thanks to the participants of said challenge -- I borrow heavily from your results. Inspiration Can we help reduce moderator burnout by automating comment removal? What features are most predictive of popular comments getting removed?