Towards Detecting Harmful Agendas in News Articles

Melanie Subbiah, Amrita Bhattacharjee, Yilun Hua, Tharindu Kumarage, Huan Liu +1 more
1/31/2023
cs.CLcs.LG

No official code found. Best implementation: melaniesubbiah/harmfulagendasnews (5 stars, MIT, last commit 2023-10-20).

Abstract

Manipulated news online is a growing problem which necessitates the use of automated systems to curtail its spread. We argue that while misinformation and disinformation detection have been studied, there has been a lack of investment in the important open challenge of detecting harmful agendas in news articles; identifying harmful agendas is critical to flag news campaigns with the greatest potential for real world harm. Moreover, due to real concerns around censorship, harmful agenda detectors must be interpretable to be effective. In this work, we propose this new task and release a dataset, NewsAgendas, of annotated news articles for agenda identification. We show how interpretable systems can be effective on this task and demonstrate that they can perform comparably to black-box models.

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Code Implementations(1)

melaniesubbiah/harmfulagendasnewsCommunity100%
MIT

Code for the Detecting Harmful Agendas in News Articles paper (https://arxiv.org/abs/2302.00102).

51Shell, PythonApr 26, 20233 years agoMIT

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Cite this paper

@article{subbiah2023towards,
  title  = {Towards Detecting Harmful Agendas in News Articles},
  author = {Melanie Subbiah and Amrita Bhattacharjee and Yilun Hua and Tharindu Kumarage and Huan Liu and Kathleen McKeown},
  year   = {2023},
  eprint = {2302.00102},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2302.00102v3}
}

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