Conspiracy theories and where to find them on TikTok

• URL: http://arxiv.org/abs/2407.12545v1
• Date: Wed, 17 Jul 2024 13:28:11 GMT
• Title: Conspiracy theories and where to find them on TikTok
• Authors: Francesco Corso, Francesco Pierri, Gianmarco De Francisci Morales,
• Abstract summary: Concerns have been raised about the potential of TikTok to promote and amplify online harmful and dangerous content. Our study analyzes the presence of videos promoting conspiracy theories, providing a lower-bound estimate of their prevalence. We evaluate the capabilities of state-of-the-art open Large Language Models to identify conspiracy theories after extracting audio transcriptions of videos.
• Score: 3.424635462664968
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Advancing Content Moderation: Evaluating Large Language Models for Detecting Sensitive Content Across Text, Images, and Videos [0.1399948157377307]
  Governments, educators, and parents are often at odds with media platforms about how to regulate, control, and limit the spread of such content. Techniques from natural language processing and computer vision have been used widely to automatically identify and filter out sensitive content. More sophisticated algorithms for understanding the context of both text and image may open rooms for improvement in content censorship.
  arXiv  Detail & Related papers  (2024-11-26T05:29:18Z)
• Delving Deep into Engagement Prediction of Short Videos [34.38399476375175]
  This study delves deep into the intricacies of predicting engagement for newly published videos with limited user interactions. We introduce a substantial dataset comprising 90,000 real-world short videos from Snapchat. Our method demonstrates its ability to predict engagements of short videos purely from video content.
  arXiv  Detail & Related papers  (2024-09-30T23:57:07Z)
• HOTVCOM: Generating Buzzworthy Comments for Videos [49.39846630199698]
  This study introduces textscHotVCom, the largest Chinese video hot-comment dataset, comprising 94k diverse videos and 137 million comments. We also present the textttComHeat framework, which synergistically integrates visual, auditory, and textual data to generate influential hot-comments on the Chinese video dataset.
  arXiv  Detail & Related papers  (2024-09-23T16:45:13Z)
• PropaInsight: Toward Deeper Understanding of Propaganda in Terms of Techniques, Appeals, and Intent [71.20471076045916]
  Propaganda plays a critical role in shaping public opinion and fueling disinformation. Propainsight systematically dissects propaganda into techniques, arousal appeals, and underlying intent. Propagaze combines human-annotated data with high-quality synthetic data.
  arXiv  Detail & Related papers  (2024-09-19T06:28:18Z)
• What we can learn from TikTok through its Research API [3.424635462664968]
  The recent release of a free Research API opens the door to collecting data on posted videos, associated comments, and user activities. Our study focuses on evaluating the reliability of the results returned by the Research API, by collecting and analyzing a random sample of TikTok videos posted in a span of 6 years.
  arXiv  Detail & Related papers  (2024-02-21T14:59:49Z)
• Shifting Climates: Climate Change Communication from YouTube to TikTok [0.0]
  We studied the video content produced by 21 prominent YouTube creators who have expanded their influence to TikTok as information disseminators. We found that creators use a more emotionally resonant, self-referential, and action-oriented language compared to YouTube. We also observed a strong semantic alignment between videos and comments, with creators who excel at diversifying their TikTok content from YouTube typically receiving responses that more closely align with their produced content.
  arXiv  Detail & Related papers  (2023-12-08T11:10:10Z)
• The Conspiracy Money Machine: Uncovering Telegram's Conspiracy Channels and their Profit Model [50.80312055220701]
  We discover that conspiracy channels can be clustered into four distinct communities comprising over 17,000 channels. We find conspiracy theorists leverage e-commerce platforms to sell questionable products or lucratively promote them through affiliate links. We conclude that this business involves hundreds of thousands of donors and generates a turnover of almost $66 million.
  arXiv  Detail & Related papers  (2023-10-24T16:25:52Z)
• An Image is Worth a Thousand Toxic Words: A Metamorphic Testing Framework for Content Moderation Software [64.367830425115]
  Social media platforms are being increasingly misused to spread toxic content, including hate speech, malicious advertising, and pornography. Despite tremendous efforts in developing and deploying content moderation methods, malicious users can evade moderation by embedding texts into images. We propose a metamorphic testing framework for content moderation software.
  arXiv  Detail & Related papers  (2023-08-18T20:33:06Z)
• An Empirical Investigation of Personalization Factors on TikTok [77.34726150561087]
  Despite the importance of TikTok's algorithm to the platform's success and content distribution, little work has been done on the empirical analysis of the algorithm. Using a sock-puppet audit methodology with a custom algorithm developed by us, we tested and analysed the effect of the language and location used to access TikTok. We identify that the follow-feature has the strongest influence, followed by the like-feature and video view rate.
  arXiv  Detail & Related papers  (2022-01-28T17:40:00Z)
• Slapping Cats, Bopping Heads, and Oreo Shakes: Understanding Indicators of Virality in TikTok Short Videos [11.089339341624996]
  We study what elements of short videos posted on TikTok contribute to their virality. Our research highlights the characteristics that distinguish viral from non-viral TikTok videos.
  arXiv  Detail & Related papers  (2021-11-03T18:17:16Z)
• Content-based Analysis of the Cultural Differences between TikTok and Douyin [95.32409577885645]
  Short-form video social media shifts away from the traditional media paradigm by telling the audience a dynamic story to attract their attention. In particular, different combinations of everyday objects can be employed to represent a unique scene that is both interesting and understandable. Offered by the same company, TikTok and Douyin are popular examples of such new media that has become popular in recent years. The hypothesis that they express cultural differences together with media fashion and social idiosyncrasy is the primary target of our research.
  arXiv  Detail & Related papers  (2020-11-03T01:47:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.