# Topological data mapping of online hate speech, misinformation, and general mental health: A large language model based study

## TL;DR

This study uses AI to analyze Reddit posts and finds links between online hate speech, misinformation, and mental health conditions like personality disorders.

## Contribution

A novel approach combining large language models and topological data analysis to map relationships between online speech and mental health.

## Key findings

- Hate speech communities showed speech patterns similar to personality disorder communities.
- Misinformation communities were most similar to control groups but had some overlap with anxiety disorder communities.
- Topological data analysis revealed visual connections between hate speech, misinformation, and mental health.

## Abstract

The advent of social media has led to an increased concern over its potential to propagate hate speech and misinformation, which, in addition to contributing to prejudice and discrimination, has been suspected of playing a role in increasing social violence and crimes in the United States. While literature has shown the existence of an association between posting hate speech and misinformation online and certain personality traits of posters, the general relationship and relevance of online hate speech/misinformation in the context of overall psychological wellbeing of posters remain elusive. One difficulty lies in finding data analytics tools capable of adequately analyzing the massive amount of social media posts to uncover the underlying hidden links. Machine learning and large language models such as ChatGPT make such an analysis possible. In this study, we collected thousands of posts from carefully selected communities on the social media site Reddit. We then utilized OpenAI’s GPT3 to derive embeddings of these posts, which are high-dimensional real-numbered vectors that presumably represent the hidden semantics of posts. We then performed various machine-learning classifications based on these embeddings in order to identify potential similarities between hate speech/misinformation speech patterns and those of various communities. Finally, a topological data analysis (TDA) was applied to the embeddings to obtain a visual map connecting online hate speech, misinformation, various psychiatric disorders, and general mental health.

The advent of social media has led to an increased concern over its potential to propagate hate speech and misinformation, which, in addition to contributing to prejudice and discrimination, has been suspecting of playing a role in increasing social violence and crimes in the United States. While literature has shown the existence of an association between posting hate speech and misinformation online and certain personality traits of posters, the general relationship between online hate speech, misinformation, and general mental health remains unclear. In this study, we collected thousands of posts from misinformation, hate speech, and psychiatric disorder communities on the social media site Reddit. We then used machine-learning methods to analyze the speech patterns of posts from each community and then identify which psychiatric disorder communities’ speech patterns are most similar to those of hate speech or misinformation. We found that speech patterns in hate speech communities were most similar to those seen in communities for Antisocial Personality Disorder, Borderline Personality Disorder, Narcissistic Personality Disorder, Schizoid Personality Disorder, and Complex Post-Traumatic Stress Disorder. For misinformation communities, their most similar communities were our control communities, though there was a degree of similarity with anxiety disorder communities.