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The Challenges of Studying Misinformation on Video-Sharing Platforms During Crises and Mass-Convergence Events

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arxiv 2303.14309 v1 pith:4BIGF6D6 submitted 2023-03-25 cs.HC cs.CVcs.CYcs.MMcs.SI

Sukrit Venkatagiri , Joseph S. Schafer , Stephen Prochaska This is my paper · ORCID
classification cs.HCcs.CVcs.CYcs.MMcs.SI
keywords vspschallengesmisinformationplatformsstudyingvideo-sharingabilityadapt
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Mis- and disinformation can spread rapidly on video-sharing platforms (VSPs). Despite the growing use of VSPs, there has not been a proportional increase in our ability to understand this medium and the messages conveyed through it. In this work, we draw on our prior experiences to outline three core challenges faced in studying VSPs in high-stakes and fast-paced settings: (1) navigating the unique affordances of VSPs, (2) understanding VSP content and determining its authenticity, and (3) novel user behaviors on VSPs for spreading misinformation. By highlighting these challenges, we hope that researchers can reflect on how to adapt existing research methods and tools to these new contexts, or develop entirely new ones.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Multimodal Fake News Video Explanation: Dataset, Analysis and Evaluation

    cs.CV 2025-01 conditional novelty 6.0 of 10

    A new dataset (FakeVE) and a benchmark model (MRGT) for generating natural-language explanations of why multimodal news videos are fake.

  2. Political Fact-Checking Efforts are Constrained by Deficiencies in Coverage, Speed, and Reach

    cs.SI 2024-12 conditional novelty 6.0 of 10

    During the 2022 U.S. midterms, fewer than half of prominent misinformation narratives received fact-checks, the median fact-check came four days late, and fact-check posts were about 1.2% of narrative-related conversation.

  3. FakeSV-VLM: Taming VLM for Detecting Fake Short-Video News via Progressive Mixture-Of-Experts Adapter

    cs.MM 2025-08 reject novelty 5.0 of 10

    FakeSV-VLM reaches 90.22% and 89.30% accuracy on FakeSV and FakeTT by adding a two-stage MoE adapter and contrastive alignment to InternVL2.5-8B.

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