Online social media has become increasingly popular in recent years due to its ease of access and ability to connect with others. One of social media's main draws is its anonymity, allowing users to share their thoughts and opinions without fear of judgment or retribution. This anonymity has also made social media prone to harmful content, which requires moderation to ensure responsible and productive use. Several methods using artificial intelligence have been employed to detect harmful content. However, conversation and contextual analysis of hate speech are still understudied. Most promising works only analyze a single text at a time rather than the conversation supporting it. In this work, we employ a tree-based approach to understand how users behave concerning toxicity in public conversation settings. To this end, we collect both the posts and the comment sections of the top 100 posts from 8 Reddit communities that allow profanity, totaling over 1 million responses. We find that toxic comments increase the likelihood of subsequent toxic comments being produced in online conversations. Our analysis also shows that immediate context plays a vital role in shaping a response rather than the original post. We also study the effect of consensual profanity and observe overlapping similarities with non-consensual profanity in terms of user behavior and patterns.

翻译：近年来，在线社交媒体因其易于访问和人际连接能力而日益普及。社交媒体的主要吸引力之一在于其匿名性，允许用户分享想法和观点而无需担心评判或报复。这种匿名性也使得社交媒体易受有害内容影响，需要采取监管措施以确保负责任且高效的平台使用。目前已有多种基于人工智能的方法被用于检测有害内容，然而，针对仇恨言论的对话及语境分析仍研究不足。大多数先进研究仅能逐条分析单一文本，而非支持该文本的完整对话。在本研究中，我们采用基于树结构的方法来理解用户在公共对话场景中针对毒性的行为模式。为此，我们收集了8个允许脏话的Reddit社区中排名前100的帖子及其评论区域，共计超过100万条回复。研究发现，毒性评论会增加后续毒性评论在在线对话中产生的概率。分析同时表明，即时语境对塑造回复内容的作用远大于原始帖子。此外，我们还研究了共识性脏话的影响，并观察到其在用户行为与模式上与非常规脏话存在重叠相似性。