Published July 22, 2023 | Version v1
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Identifying Misinformation on YouTube through Transcript Contextual Analysis with Transformer Models

  • 1. ROR icon Cyprus University of Technology


Misinformation on YouTube is a significant concern, necessitating robust detection strategies. In this paper, we introduce a novel methodology for video classification, focusing on the veracity of the content. We convert the conventional video classification task into a text classification task by leveraging the textual content derived from the video transcripts. We employ advanced machine learning techniques like transfer learning to solve the classification challenge. Our approach incorporates two forms of transfer learning: (a) fine-tuning base transformer models such as BERT, RoBERTa, and ELECTRA, and (b) few-shot learning using sentence-transformers MPNet and RoBERTa-large. We apply the trained models to three datasets: (a) YouTube Vaccine–misinformation related videos, (b) YouTube Pseudoscience videos, and (c) Fake-News dataset (a collection of articles). Including the Fake-News dataset extended the evaluation of our approach beyond YouTube videos. Using these datasets, we evaluated the models distinguishing valid information from misinformation. The fine-tuned models yielded Matthews Correlation Coefficient>0.81, accuracy>0.90, and F1 score>0.90 in two of three datasets. Interestingly, the fewshot models outperformed the fine-tuned ones by 20% in both accuracy and F1 score for the YouTube Pseudoscience dataset, highlighting the potential utility of this approach– especially in the context of limited training data.


Identifying Misinformation on YouTube through Transcript Contextual Analysis with Transformer Models.pdf

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INCOGNITO – IdeNtity verifiCatiOn with privacy-preservinG credeNtIals for anonymous access To Online services 824015
European Commission
Mediterranean Digital Media Observatory 101083756
Digital Europe