Authors: Arnaud Stiegler, Redouane Dziri
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.
(find the data here: https://www.kaggle.com/areeves87/rscience-popular-comment-removal)
This project aims at accurately classifying removed comments, leveraging some NLP tools (using scikit-learn and nltk) , with the following questions in mind:
Can we help reduce moderator burnout by automating comment removal? What features are most predictive of popular comments getting removed?