Research & Studies

Identifying Misinformation on YouTube through Transcript Contextual Analysis with Transformer Models

September 5, 2023 Cyprus University of TechnologyChristos Christodoulou, Nikos Salamanos, Pantelitsa Leonidou, Michail Papadakis, Michael Sirivianos  https://arxiv.org/abs/2307.12155  YouTube, as a dominant platform for video content, has become an essential source of information for many. However, its user-generated nature makes it susceptible to the spread of misinformation. The challenge intensifies when considering the platform’s recommendation algorithms, […]

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Detecting Disinformation Cascades on Twitter (CUT)

March 7, 2023 Recent events have demonstrated that disinformation spread through social networks can have dire political, economic, and social consequences. Detecting disinformation must inevitably rely on the structure of the network, on users’ particularities, and on event occurrence patterns. In our study with the title “A Unified Graph-Based Approach to Disinformation Detection using Contextual

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Analysis of YouTube’s Pseudoscientific Video Recommendations (CUT)

March 7, 2023 YouTube has revolutionized how people discover and consume videos, becoming one of the primary news sources for Internet users. Since content on YouTube is generated by its users, the platform is particularly vulnerable to misinformative and conspiratorial videos. Even worse, the role played by YouTube’s recommendation algorithm in unwittingly promoting questionable content

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