An Interactive Framework for Profiling News Media Sources

Nikhil Mehta, Dan Goldwasser


Abstract
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.
Anthology ID:
2024.naacl-long.3
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
40–58
Language:
URL:
https://aclanthology.org/2024.naacl-long.3
DOI:
Bibkey:
Cite (ACL):
Nikhil Mehta and Dan Goldwasser. 2024. An Interactive Framework for Profiling News Media Sources. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 40–58, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
An Interactive Framework for Profiling News Media Sources (Mehta & Goldwasser, NAACL 2024)
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PDF:
https://aclanthology.org/2024.naacl-long.3.pdf
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