An Empirical Assessment of the Qualitative Aspects of Misinformation in Health News

Chaoyuan Zuo, Qi Zhang, Ritwik Banerjee


Abstract
The explosion of online health news articles runs the risk of the proliferation of low-quality information. Within the existing work on fact-checking, however, relatively little attention has been paid to medical news. We present a health news classification task to determine whether medical news articles satisfy a set of review criteria deemed important by medical experts and health care journalists. We present a dataset of 1,119 health news paired with systematic reviews. The review criteria consist of six elements that are essential to the accuracy of medical news. We then present experiments comparing the classical token-based approach with the more recent transformer-based models. Our results show that detecting qualitative lapses is a challenging task with direct ramifications in misinformation, but is an important direction to pursue beyond assigning True or False labels to short claims.
Anthology ID:
2021.nlp4if-1.11
Volume:
Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda
Month:
June
Year:
2021
Address:
Online
Venue:
NLP4IF
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
76–81
Language:
URL:
https://aclanthology.org/2021.nlp4if-1.11
DOI:
10.18653/v1/2021.nlp4if-1.11
Bibkey:
Cite (ACL):
Chaoyuan Zuo, Qi Zhang, and Ritwik Banerjee. 2021. An Empirical Assessment of the Qualitative Aspects of Misinformation in Health News. In Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, pages 76–81, Online. Association for Computational Linguistics.
Cite (Informal):
An Empirical Assessment of the Qualitative Aspects of Misinformation in Health News (Zuo et al., NLP4IF 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.nlp4if-1.11.pdf