of research. • How diﬀerent topics can lead to generation and spread of hate speech in a user network is underexplored. • We aim to determine how a hateful tweet diﬀuses via retweet and which users are likely to generate hate in the ﬁrst place. Motivation
user activity history, and follower networks. (spanning 20+ topics) • Manually annotated a total of 17k tweets and trained a Hate Detection model for our dataset. • Additionally crawled online news articles published during the timeframe of Twitter dataset. • We design 2 models one for determining generation of hate and other for diﬀusion of hate in the follow network.
hate generation and spread on twitter. • We developed multiple supervised models powered by rich feature representation to predict the probability of any given user tweeting something hateful. • We proposed RETINA, a neural framework exploiting extra-Twitter information (in terms of news) with attention mechanism for predicting potential retweeters for any given tweet. • Reference Code: https://github.com/LCS2-IIITD/RETINA • Paper: https://arxiv.org/abs/2010.04377