🤖 AI Summary
This study investigates historical inaccuracies in AI-generated first-person historical narratives on TikTok and the resulting harmful user responses, with a particular focus on the proliferation of hate speech and misinformation surrounding sensitive topics such as the Holocaust. Employing a two-phase empirical approach—combining an exploratory pilot study with large-scale data analysis via the TikTok Research API—the research integrates manual annotation and a DistilBERT-based comment classification model for mixed-methods analysis. The findings reveal, for the first time in a systematic manner, that contemporary historical themes dominate and frequently contain factual errors; moreover, content related to the Holocaust elicits significantly more hate speech and misinformation compared to other topics like the Black Death, underscoring the risks posed by AI-generated historical content on short-video platforms and the urgent need for effective moderation and oversight.
📝 Abstract
This paper examines the history POV trend on TikTok, in which AI-generated first-person scenes depict historical events. We use a two-stage empirical approach: an exploratory pilot study and a larger-scale study building up on a dataset obtained through the TikTok Research API. In both studies we analyze the themes of the trend and how the audience responds in the comments. Findings show a dominance of emotionally charged contemporary history topics, with historical inaccuracies visible at the caption level. A comparative comment analysis of Black Death and Holocaust videos, combining manual annotation with DistilBERT-based classification, reveals that topic choice shapes audience response, with Holocaust content attracting disproportionately higher rates of hate speech and disinformation. The paper also reflects on the strengths and limitations of API-based research for studying fast-moving platform trends.