Analyzing Toxicity in Deep Conversations: A Reddit Case Study

• URL: http://arxiv.org/abs/2404.07879v1
• Date: Thu, 11 Apr 2024 16:10:44 GMT
• Title: Analyzing Toxicity in Deep Conversations: A Reddit Case Study
• Authors: Vigneshwaran Shankaran, Rajesh Sharma,
• Abstract summary: This work employs a tree-based approach to understand how users behave concerning toxicity in public conversation settings. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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