mlsanityData to 7 Oct 2026

An Interactive Framework for Profiling News Media Sources

arXiv 2309.07384 ↗First posted 14 Sep 2023cs.CLcited by 4

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Abstract (arXiv)

The recent rise of social media has led to the spread of large amounts of fake and biased news, content published with the intent to sway beliefs. While detecting and profiling the sources that spread this news is important to maintain a healthy society, it is challenging for automated systems. In this paper, we propose an interactive framework for news media profiling. It combines the strengths of graph based news media profiling models, Pre-trained Large Language Models, and human insight to characterize the social context on social media. Experimental results show that with as little as 5 human interactions, our framework can rapidly detect fake and biased news media, even in the most challenging settings of emerging news events, where test data is unseen.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 32 of its 55 references that are in the map. First posted on arXiv 14 Sep 2023.

Builds on

32 of 55 references.

References · 32

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20 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 1.

4 papers.

  1. 2025Profiling News Media for Factuality and Bias Using LLMs and the Fact-Checking Methodology of Human Expertscited by 3
  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
  4. 2026A Multi-View Media Profiling Suite: Resources, Evaluation, and Analysiscited by 0