@inproceedings{subbiah-etal-2023-towards,
title = "Towards Detecting Harmful Agendas in News Articles",
author = "Subbiah, Melanie and
Bhattacharjee, Amrita and
Hua, Yilun and
Kumarage, Tharindu and
Liu, Huan and
McKeown, Kathleen",
editor = "Barnes, Jeremy and
De Clercq, Orph{\'e}e and
Klinger, Roman",
booktitle = "Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, {\&} Social Media Analysis",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.wassa-1.11",
doi = "10.18653/v1/2023.wassa-1.11",
pages = "110--128",
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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<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.</abstract>
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%0 Conference Proceedings
%T Towards Detecting Harmful Agendas in News Articles
%A Subbiah, Melanie
%A Bhattacharjee, Amrita
%A Hua, Yilun
%A Kumarage, Tharindu
%A Liu, Huan
%A McKeown, Kathleen
%Y Barnes, Jeremy
%Y De Clercq, Orphée
%Y Klinger, Roman
%S Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F subbiah-etal-2023-towards
%X 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.
%R 10.18653/v1/2023.wassa-1.11
%U https://aclanthology.org/2023.wassa-1.11
%U https://doi.org/10.18653/v1/2023.wassa-1.11
%P 110-128
Markdown (Informal)
[Towards Detecting Harmful Agendas in News Articles](https://aclanthology.org/2023.wassa-1.11) (Subbiah et al., WASSA 2023)
ACL
- Melanie Subbiah, Amrita Bhattacharjee, Yilun Hua, Tharindu Kumarage, Huan Liu, and Kathleen McKeown. 2023. Towards Detecting Harmful Agendas in News Articles. In Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, pages 110–128, Toronto, Canada. Association for Computational Linguistics.