Tweets are the most concise form of communication in online social media, wherein a single tweet has the potential to make or break the discourse of the conversation. Online hate speech is more accessible than ever, and stifling its propagation is of utmost importance for social media companies and users for congenial communication. Most of the research barring a recent few has focused on classifying an individual tweet regardless of the tweet thread/context leading up to that point. One of the classical approaches to curb hate speech is to adopt a reactive strategy after the hate speech postage. The ex-post facto strategy results in neglecting subtle posts that do not show the potential to instigate hate speech on their own but may portend in the subsequent discussion ensuing in the post's replies. In this paper, we propose DRAGNET++, which aims to predict the intensity of hatred that a tweet can bring in through its reply chain in the future. It uses the semantic and propagating structure of the tweet threads to maximize the contextual information leading up to and the fall of hate intensity at each subsequent tweet. We explore three publicly available Twitter datasets -- Anti-Racism contains the reply tweets of a collection of social media discourse on racist remarks during US political and Covid-19 background; Anti-Social presents a dataset of 40 million tweets amidst the COVID-19 pandemic on anti-social behaviours; and Anti-Asian presents Twitter datasets collated based on anti-Asian behaviours during COVID-19 pandemic. All the curated datasets consist of structural graph information of the Tweet threads. We show that DRAGNET++ outperforms all the state-of-the-art baselines significantly. It beats the best baseline by an 11% margin on the Person correlation coefficient and a decrease of 25% on RMSE for the Anti-Racism dataset with a similar performance on the other two datasets.

翻译：推文是在线社交媒体中最简洁的交流形式，单条推文可能决定对话走向的成败。仇恨言论比以往任何时候都更容易获取，遏制其传播对于社交媒体公司和用户营造和谐交流环境至关重要。除近期少数研究外，大多数研究都聚焦于对单个推文进行分类，而忽略了该推文所在线程/上下文。遏制仇恨言论的经典方法之一是在仇恨言论发布后采取应对策略。这种事后策略会导致忽视那些自身不具煽动仇恨潜力、但可能在帖子回复的后续讨论中预示危险的微妙帖子。本文提出DRAGNET++，旨在预测推文通过其未来回复链可能引发的仇恨强度。该方法利用推文线程的语义和传播结构，最大化每条后续推文上下文信息中仇恨强度的上升与下降趋势。我们探索了三个公开的Twitter数据集：Anti-Racism包含美国政治及新冠疫情背景下种族主义言论社交媒体讨论的回复推文；Anti-Social提供了新冠疫情中反社会行为的4000万条推文数据集；Anti-Asian整理了新冠疫情期间基于反亚裔行为的Twitter数据集。所有整理的数据集均包含推文线程的结构化图信息。研究表明，DRAGNET++显著优于所有最先进的基线方法。它在Anti-Racism数据集上的皮尔逊相关系数比最佳基线高出11%，均方根误差降低25%，在其他两个数据集上表现相似。