Topological Data Mapping of Online Hate Speech, Misinformation, and
  General Mental Health: A Large Language Model Based Study
Andrew Alexander Hongbin Wang
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 the lack of adequate data analytics tools capable of adequately
analyzing the massive amount of social media posts to uncover the underlying
hidden links. Recent progresses in machine learning and large language models
such as ChatGPT have made 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
understand the role of hate speech/misinformation in 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.