Given a tweet, predicting the discussions that unfold around it is convoluted, to say the least. Most if not all of the discernibly benign tweets which seem innocuous may very well attract inflammatory posts (hate speech) from people who find them non-congenial. Therefore, building upon the aforementioned task and predicting if a tweet will incite hate speech is of critical importance. To stifle the dissemination of online hate speech is the need of the hour. Thus, there have been a handful of models for the detection of hate speech. Classical models work retrospectively by leveraging a reactive strategy – detection after the postage of hate speech, i.e., a backward trace after detection. Therefore, a benign post that may act as a surrogate to invoke toxicity in the near future, may not be flagged by the existing hate speech detection models. In this paper, we address this problem through a proactive strategy initiated to avert hate crime. We propose DRAGNET, a deep stratified learning framework which predicts the intensity of hatred that a root tweet can fetch through its subsequent replies. We extend the collection of social media discourse from our earlier work [1], comprising the entire reply chains up to $\sim$5k root tweets catalogued into four controversial topics Similar to [1], we notice a handful of cases where despite the root tweets being non-hateful, the succeeding replies inject an enormous amount of toxicity into the discussions. DRAGNET turns out to be highly effective, significantly outperforming six state-of-the-art baselines. It beats the best baseline with an increase of 9.4% in the Pearson correlation coefficient and a decrease of 19% in Root Mean Square Error. Further, DRAGNET’S deployment in Logically’s advanced AI platform designed to monitor real-world problematic and hateful narratives has improved the aggregated insights extracted for understanding their spread, influence and thereby offering actionable intelligence to counter them

Curbing hate speech is undoubtedly a major challenge for online microblogging platforms like Twitter. While there have been studies around hate speech detection, it is not clear how hate speech finds its way into an online discussion. It is important for a content moderator to not only identify which tweet is hateful but also to predict which tweet will be responsible for accumulating hate speech. This would help in prioritizing tweets that need constant monitoring. Our analysis reveals that for hate speech to manifest in an ongoing discussion, the source tweet may not necessarily be hateful; rather, there are plenty of such non-hateful tweets which gradually invoke hateful replies, resulting in the entire reply threads becoming provocative.