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The advent of social media has led to an increased concern over its potentialto propagate hate speech and misinformation, which, in addition to contributingto prejudice and discrimination, has been suspected of playing a role inincreasing social violence and crimes in the United States. While literaturehas shown the existence of an association between posting hate speech andmisinformation online and certain personality traits of posters, the generalrelationship and relevance of online hate speech/misinformation in the contextof overall psychological wellbeing of posters remain elusive. One difficultylies in the lack of adequate data analytics tools capable of adequatelyanalyzing the massive amount of social media posts to uncover the underlyinghidden links. Recent progresses in machine learning and large language modelssuch as ChatGPT have made such an analysis possible. In this study, wecollected thousands of posts from carefully selected communities on the socialmedia site Reddit. We then utilized OpenAI's GPT3 to derive embeddings of theseposts, which are high-dimensional real-numbered vectors that presumablyrepresent the hidden semantics of posts. We then performed variousmachine-learning classifications based on these embeddings in order tounderstand the role of hate speech/misinformation in various communities.Finally, a topological data analysis (TDA) was applied to the embeddings toobtain a visual map connecting online hate speech, misinformation, variouspsychiatric disorders, and general mental health.

Topological Data Mapping of Online Hate Speech, Misinformation, and General Mental Health: A Large Language Model Based Study