  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.
