Natural Language Processing Research

Volume 1, Issue 1-2, July 2020, Pages 1 - 13

Motivations, Methods and Metrics of Misinformation Detection: An NLP Perspective

Authors
Qi Su1, *, ORCID, Mingyu Wan1, 2, Xiaoqian Liu1, Chu-Ren Huang2, ORCID
1Peking University, Beijing, China
2The Hong Kong Polytechnic University, Hong Kong, China
*Corresponding author. Email: sukia@pku.edu.cn
Corresponding Author
Qi Su
Received 20 October 2019, Accepted 14 May 2020, Available Online 11 June 2020.
DOI
10.2991/nlpr.d.200522.001How to use a DOI?
Keywords
Misinformation detection; Information credibility; Feature representations; Modeling and predicting
Abstract

The rise of misinformation online and offline reveals the erosion of long-standing institutional bulwarks against its propagation in the digitized era. Concerns over the problem are global and the impact is long-lasting. The past few decades have witnessed the critical role of misinformation detection in enhancing public trust and social stability. However, it remains a challenging problem for the Natural Language Processing community. This paper discusses the main issues of misinformation and its detection with a comprehensive review on representative works in terms of detection methods, feature representations, evaluation metrics and reference datasets. Advantages and disadvantages of the key techniques are also addressed with focuses on content-based analysis and predicative modeling. Alternative solutions to anti-misinformation imply a trend of hybrid multi-modal representation, multi-source data and multi-facet inference, e.g., leveraging the language complexity. In spite of decades' efforts, the dynamic and evolving nature of misrepresented information across different domains, languages, cultures and time spans determines the openness and uncertainty of this restless adventure in the future.

Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
Natural Language Processing Research
Volume-Issue
1 - 1-2
Pages
1 - 13
Publication Date
2020/06/11
ISSN (Online)
2666-0512
DOI
10.2991/nlpr.d.200522.001How to use a DOI?
Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Qi Su
AU  - Mingyu Wan
AU  - Xiaoqian Liu
AU  - Chu-Ren Huang
PY  - 2020
DA  - 2020/06/11
TI  - Motivations, Methods and Metrics of Misinformation Detection: An NLP Perspective
JO  - Natural Language Processing Research
SP  - 1
EP  - 13
VL  - 1
IS  - 1-2
SN  - 2666-0512
UR  - https://doi.org/10.2991/nlpr.d.200522.001
DO  - 10.2991/nlpr.d.200522.001
ID  - Su2020
ER  -