Predicting the Factuality of Reporting of News Media Using Observations About User Attention in Their YouTube Channels
Not on the map. Why?
Abstract (arXiv)
We propose a novel framework for predicting the factuality of reporting of news media outlets by studying the user attention cycles in their YouTube channels. In particular, we design a rich set of features derived from the temporal evolution of the number of views, likes, dislikes, and comments for a video, which we then aggregate to the channel level. We develop and release a dataset for the task, containing observations of user attention on YouTube channels for 489 news media. Our experiments demonstrate both complementarity and sizable improvements over state-of-the-art textual representations.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 9 of its 21 references that are in the map. First posted on arXiv 27 Aug 2021.
Builds on
9 of 21 references.
References · 9
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
- 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,242
- 2017CSI: A Hybrid Deep Model for Fake News Detectioncited by 101
- 2020FANG: Leveraging Social Context for Fake News Detection Using Graph Representationcited by 98
- 2018Predicting Factuality of Reporting and Bias of News Media Sourcescited by 71
- 2017This Just In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real Newscited by 59
- 2019Auditing Radicalization Pathways on YouTubecited by 52
- 2020What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Contextcited by 42
- 2019Predicting the Leading Political Ideology of YouTube Channels Using Acoustic, Textual, and Metadata Informationcited by 9
Built on by
Citing papers per year: 2023: 1; 2024: 1.
2 papers.