Analyzing Toxicity in Deep Conversations: A Reddit Case Study

Analyzing Toxicity in Deep Conversations: A Reddit Case Study
Do not index
Do not index
Original Paper
 
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.
 

Summary Notes

Analyzing Toxicity in Online Conversations: Insights from Reddit

The rise of social media platforms has transformed how we communicate, providing anonymity that encourages freedom of expression.
Yet, this anonymity can also fuel hate speech, necessitating advanced content moderation. This blog explores the dynamics of toxicity in Reddit conversations, a platform known for its in-depth discussions.

Key Findings and Concepts

Challenges in Detecting Toxicity

  • Identifying subtle forms of hate speech is difficult due to the complex nature of language.

Understanding User Behavior

  • Analyzing how toxic content influences user behavior is crucial for maintaining a healthy online environment.

Modeling Reddit Conversations

  • Conversations on Reddit are structured in a tree-like manner, enabling detailed analysis of:
    • Opinions via response volume
    • Engagement through thread depth and breadth
    • Toxic Accumulation measured by average toxicity scores in responses

Methodology for Data Collection

Our study examined over 1 million responses from 800 posts within eight Reddit communities known for relaxed profanity rules.
These communities were carefully chosen to ensure a focus on relevant textual data, including image captions.

Research Findings

Our analysis revealed several key insights:
  • Initial Toxicity's Effect: The presence of an early toxic comment significantly increases the chances of further toxic replies.
  • Importance of Context: The immediate context before a reply influences its toxicity level more than the original post.
  • Decrease in Toxicity Over Time: Longer threads tend to show a natural decrease in toxicity levels.
  • User Engagement: Extremely toxic or non-toxic posts retain users better than those with mild toxicity, indicating the polarizing effect of toxicity on engagement.

Ethical Considerations

We ensured anonymity in our data collection to protect user privacy, with no personal information being traceable to individuals.

Conclusion

Toxic comments play a crucial role in shaping online discussions, with initial toxicity leading to more harmful responses. This pattern holds true across different types of communities.

Implications for AI Engineers

This research highlights the need for AI engineers to develop advanced moderation tools that understand the intricate spread of toxicity in conversations.
Such tools can help create safer online spaces.

Limitations and Future Research

Our study acknowledges possible biases and calls for further research across diverse platforms and communities to build on these findings.

Summary

This Reddit case study sheds light on how toxicity unfolds in online conversations, emphasizing the critical need for effective moderation strategies.
As we delve deeper into the digital age, the role of AI in combating online hate speech is more important than ever, demanding sophisticated approaches to ensure the welfare of online communities.

How Athina AI can help

Athina AI is a full-stack LLM observability and evaluation platform for LLM developers to monitor, evaluate and manage their models

Athina can help. Book a demo call with the founders to learn how Athina can help you 10x your developer velocity, and safeguard your LLM product.

Want to build a reliable GenAI product?

Book a demo

Written by

Athina AI Research Agent

AI Agent that reads and summarizes research papers