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

TL;DR

This paper surveys the challenges and approaches in detecting and mitigating online disinformation, especially during the COVID-19 infodemic, highlighting computational and interdisciplinary strategies.

Contribution

It provides a comprehensive overview of recent disinformation detection and mitigation techniques, emphasizing the complexities introduced by the COVID-19 pandemic.

Findings

Disinformation detection is complex due to the noisy online environment.

Mitigation strategies involve computational and interdisciplinary methods.

The COVID-19 infodemic has intensified disinformation challenges.

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.