Conspiracy theories and where to find them on TikTok

Authors: Francesco Corso, Francesco Pierri, Gianmarco De Francisci Morales

License: CC BY 4.0

Results of the summarizing process for the arXiv paper: 2407.12545v1

  • Comprehensive Summary
  • Key points
  • Layman's Summary
  • Blog article
, , , , TikTok has become a popular platform, especially among younger audiences, due to its viral trends and social challenges. However, concerns have been raised about the potential for harmful and dangerous content to spread on the platform. A study utilizing the TikTok Research API collected a dataset of 1.5 million videos shared in the US over three years to analyze the presence of conspiracy theories. The study found that approximately 0.1% of all videos contained conspiracy content. The research also evaluated the impact of TikTok's Creator Program on the supply of conspiratorial content. State-of-the-art Large Language Models were used to identify harmful content in audio transcriptions with high precision, although overall performance was comparable to traditional language models like RoBERTa. The findings have significant implications for researchers and social media platforms, providing insights into developing more effective moderation strategies and policies to combat malicious content. The study also sheds light on how platform policies influence content creation trends. Limitations include potential biases in data returned by the TikTok Research API and uncertainties associated with using Large Language Models. Future research aims to address these limitations by integrating multi-modal data in classification pipelines and exploring other online media platforms. Ethical considerations were taken into account throughout the study, with measures implemented to protect user privacy and ensure data security aligns with best practices. While the dataset is not publicly available, readers can contact the authors for access to code for replication purposes. In conclusion, this study highlights the importance of understanding and mitigating harmful content on social media platforms like TikTok through advanced technologies and strategic moderation approaches.
AI-generated keywords: TikTok

AI-generated Key Points

  • TikTok has gained popularity, especially among younger audiences, due to viral trends and social challenges.
  • Concerns have been raised about the potential for harmful and dangerous content on the platform.
  • A study using the TikTok Research API analyzed 1.5 million videos in the US over three years, finding that approximately 0.1% contained conspiracy content.
  • State-of-the-art Large Language Models were used to identify harmful content with high precision but had comparable performance to traditional models like RoBERTa.
  • The findings suggest the need for more effective moderation strategies and policies on social media platforms to combat malicious content.
Also access our AI generated: Comprehensive summary, Lay summary, Blog-like article; or ask questions about this paper to our AI assistant.

Abstract: TikTok has skyrocketed in popularity over recent years, especially among younger audiences, thanks to its viral trends and social challenges. However, concerns have been raised about the potential of this platform to promote and amplify online harmful and dangerous content. Leveraging the official TikTok Research API and collecting a longitudinal dataset of 1.5M videos shared in the US over a period of 3 years, our study analyzes the presence of videos promoting conspiracy theories, providing a lower-bound estimate of their prevalence (approximately 0.1% of all videos) and assessing the effects of the new Creator Program, which provides new ways for creators to monetize, on the supply of conspiratorial content. We evaluate the capabilities of state-of-the-art open Large Language Models to identify conspiracy theories after extracting audio transcriptions of videos, finding that they can detect harmful content with high precision but with overall performance comparable to fine-tuned traditional language models such as RoBERTa. Our findings are instrumental for content moderation strategies that aim to understand and mitigate the spread of harmful content on rapidly evolving social media platforms like TikTok.

Submitted to arXiv on 17 Jul. 2024

Tools for this paper

Explain it differently 3 free rewrites left today
Export BibTeX · RIS (Zotero, Mendeley, EndNote) · Markdown (Notion, Obsidian) · Premium
Citation alerts Premium
Journal club Slides to present it Premium
For journalists Press kit and hype check Premium

Ask questions about this paper to our AI assistant

You can also chat with multiple papers at once here.

Figures and plots

Ask about a figure by number or use its question button to request visual analysis. Up to two figures and four image panels per question. Values read from plots are approximate; check the original data.

AI assistant instructions?

Assess the quality of the AI-generated content by voting

Score: 1

Why do we need votes?

Votes are used to determine whether we need to re-run our summarizing tools. If the count reaches -10, our tools can be restarted.

The previous summary was created more than a year ago and can be re-run (if necessary) by clicking on the Run button below.

Similar papers summarized with our AI tools

Navigate through even more similar papers through a

tree representation

Disclaimer: The AI-based summarization tool and virtual assistant provided on this website may not always provide accurate and complete summaries or responses. We encourage you to carefully review and evaluate the generated content to ensure its quality and relevance to your needs.