mlsanityData to 7 Oct 2026

Interactively Learning Social Media Representations Improves News Source Factuality Detection

arXiv 2309.14966 ↗First posted 26 Sep 2023cs.CL cs.AI cs.SIcited by 3

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Abstract (arXiv)

The rise of social media has enabled the widespread propagation of fake news, text that is published with an intent to spread misinformation and sway beliefs. Rapidly detecting fake news, especially as new events arise, is important to prevent misinformation. While prior works have tackled this problem using supervised learning systems, automatedly modeling the complexities of the social media landscape that enables the spread of fake news is challenging. On the contrary, having humans fact check all news is not scalable. Thus, in this paper, we propose to approach this problem interactively, where humans can interact to help an automated system learn a better social media representation quality. On real world events, our experiments show performance improvements in detecting factuality of news sources, even after few human interactions.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 22 of its 51 references that are in the map. First posted on arXiv 26 Sep 2023.

Builds on

22 of 51 references.

References · 22

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10 more
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Cited by year

Citing papers per year: 2024: 3.

3 papers.

  1. 2024Challenges in Pre-Training Graph Neural Networks for Context-Based Fake News Detection: An Evaluation of Current Strategies and Resource Limitationscited by 2
  2. 2024MGM: Global Understanding of Audience Overlap Graphs for Predicting the Factuality and the Bias of News Mediacited by 2
  3. 2024Using RL to Identify Divisive Perspectives Improves LLMs Abilities to Identify Communities on Social Mediacited by 1