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

Authors

Qi Su^{1, *,} , Mingyu Wan^{1, 2}, Xiaoqian Liu¹, Chu-Ren Huang^2,

¹Peking University, Beijing, China

²The Hong Kong Polytechnic University, Hong Kong, China

^*Corresponding author. Email: sukia@pku.edu.cn

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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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  -