Disinformation in the Online Information Ecosystem: Detection, Mitigation and Challenges

• URL: http://arxiv.org/abs/2010.09113v1
• Date: Sun, 18 Oct 2020 21:44:23 GMT
• Title: Disinformation in the Online Information Ecosystem: Detection, Mitigation and Challenges
• Authors: Amrita Bhattacharjee, Kai Shu, Min Gao, Huan Liu
• Abstract summary: A large fraction of the common public turn to social media platforms for news and even information regarding highly concerning issues such as COVID-19 symptoms. There is a significant amount of ongoing research in the directions of disinformation detection and mitigation. We discuss the online disinformation problem, focusing on the recent 'infodemic' in the wake of the coronavirus pandemic.
• Score: 35.0667998623823
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid increase in access to internet and the subsequent growth in the population of online social media users, the quality of information posted, disseminated and consumed via these platforms is an issue of growing concern. A large fraction of the common public turn to social media platforms and in general the internet for news and even information regarding highly concerning issues such as COVID-19 symptoms. Given that the online information ecosystem is extremely noisy, fraught with misinformation and disinformation, and often contaminated by malicious agents spreading propaganda, identifying genuine and good quality information from disinformation is a challenging task for humans. In this regard, there is a significant amount of ongoing research in the directions of disinformation detection and mitigation. In this survey, we discuss the online disinformation problem, focusing on the recent 'infodemic' in the wake of the coronavirus pandemic. We then proceed to discuss the inherent challenges in disinformation research, and then elaborate on the computational and interdisciplinary approaches towards mitigation of disinformation, after a short overview of the various directions explored in detection efforts.

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