TY - GEN
T1 - An Empirical Assessment of the Qualitative Aspects of Misinformation in Health News
AU - Zuo, Chaoyuan
AU - Zhang, Qi
AU - Banerjee, Ritwik
N1 - Publisher Copyright:
© 2021 Association for Computational Linguistics.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85121660006
U2 - 10.18653/v1/2021.nlp4if-1.11
DO - 10.18653/v1/2021.nlp4if-1.11
M3 - Conference contribution
AN - SCOPUS:85121660006
T3 - NLP4IF 2021 - NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, Proceedings of the 4th Workshop
SP - 76
EP - 81
BT - NLP4IF 2021 - NLP for Internet Freedom
A2 - Feldman, Anna
A2 - Da San Martino, Giovanni
A2 - Leberknight, Chris
A2 - Nakov, Preslav
PB - Association for Computational Linguistics (ACL)
T2 - 4th Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, NLP4IF 2021
Y2 - 6 June 2021
ER -