Case Study on Detecting COVID-19 Health-Related Misinformation in Social Media

TL;DR

This study develops a machine learning-based method to detect COVID-19 health misinformation on social media, achieving up to 78% accuracy, and offers insights on countermeasures and ethical issues.

Contribution

It introduces an interdisciplinary approach combining social psychology and machine learning to identify COVID-19 misinformation on social media.

Findings

Achieved up to 78% accuracy in misinformation detection

Utilized uni-gram NLP features with Decision Tree classifier

Provided ethical considerations and countermeasure suggestions

Abstract

COVID-19 pandemic has generated what public health officials called an infodemic of misinformation. As social distancing and stay-at-home orders came into effect, many turned to social media for socializing. This increase in social media usage has made it a prime vehicle for the spreading of misinformation. This paper presents a mechanism to detect COVID-19 health-related misinformation in social media following an interdisciplinary approach. Leveraging social psychology as a foundation and existing misinformation frameworks, we defined misinformation themes and associated keywords incorporated into the misinformation detection mechanism using applied machine learning techniques. Next, using the Twitter dataset, we explored the performance of the proposed methodology using multiple state-of-the-art machine learning classifiers. Our method shows promising results with at most 78% accuracy in classifying health-related misinformation versus true information using uni-gram-based NLP feature generations from tweets and the Decision Tree classifier. We also provide suggestions on alternatives for countering misinformation and ethical consideration for the study.