A Survey on Natural Language Processing for Fake News Detection

Ray Oshikawa, Jing Qian, William Yang Wang


Abstract
Fake news detection is a critical yet challenging problem in Natural Language Processing (NLP). The rapid rise of social networking platforms has not only yielded a vast increase in information accessibility but has also accelerated the spread of fake news. Thus, the effect of fake news has been growing, sometimes extending to the offline world and threatening public safety. Given the massive amount of Web content, automatic fake news detection is a practical NLP problem useful to all online content providers, in order to reduce the human time and effort to detect and prevent the spread of fake news. In this paper, we describe the challenges involved in fake news detection and also describe related tasks. We systematically review and compare the task formulations, datasets and NLP solutions that have been developed for this task, and also discuss the potentials and limitations of them. Based on our insights, we outline promising research directions, including more fine-grained, detailed, fair, and practical detection models. We also highlight the difference between fake news detection and other related tasks, and the importance of NLP solutions for fake news detection.
Anthology ID:
2020.lrec-1.747
Volume:
Proceedings of the 12th Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
6086–6093
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.747
DOI:
Bibkey:
Cite (ACL):
Ray Oshikawa, Jing Qian, and William Yang Wang. 2020. A Survey on Natural Language Processing for Fake News Detection. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 6086–6093, Marseille, France. European Language Resources Association.
Cite (Informal):
A Survey on Natural Language Processing for Fake News Detection (Oshikawa et al., LREC 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.lrec-1.747.pdf
Data
FEVERFakeNewsNetLIARSome Like it Hoax