Case Study on Detecting COVID-19 Health-Related Misinformation in Social Media
This addresses the spread of misinformation during the COVID-19 pandemic, which is a critical issue for public health officials and social media users, though it is incremental as it builds on existing frameworks and methods.
The paper tackled the problem of detecting COVID-19 health-related misinformation on social media by developing an interdisciplinary mechanism using social psychology and machine learning, achieving up to 78% accuracy with a Decision Tree classifier on Twitter data.
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.