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Misinformation Detection on YouTube Using Video Captions
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Detailed description Millions of people use platforms such as YouTube, Facebook, Twitter, and other mass media. Due to the accessibility of these platforms, they are often used to establish a narrative, conduct propaganda, and disseminate misinformation. This work proposes an approach that uses state-of-the-art NLP techniques to extract features from video captions (subtitles). To evaluate our approach, we utilize a publicly accessible and labeled dataset for classifying videos as misinformation or not. The motivation behind exploring video captions stems from our analysis of videos metadata. Attributes such as the number of views, likes, dislikes, and comments are ineffective as videos are hard to differentiate using this information. Using caption dataset, the proposed models can classify videos among three classes (Misinformation, Debunking Misinformation, and Neutral) with 0.85 to 0.90 F1-score. To emphasize the relevance of the misinformation class, we re-formulate our classification problem as a two-class classification - Misinformation vs. others (Debunking Misinformation and Neutral). In our experiments, the proposed models can classify videos with 0.92 to 0.95 F1-score and 0.78 to 0.90 AUC ROC.
Group Societal Debates and Misinformation
Involved Institutions University of Tartu
Involved People Sharma, Rajesh, [email protected], orcid.org/0000-0003-3581-1332
State Complete
Thematic Cluster Text and Social Media Mining [TSMM]
Thematic Cluster Social Data [SD]
system:type Experiment
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Author Sharma Rajesh
Maintainer Sharma Rajesh
Version 1
Last Updated 16 September 2023, 10:07 (CEST)
Created 14 October 2022, 10:37 (CEST)