DeepAI AI Chat
Log In Sign Up

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

by   Mir Mehedi A. Pritom, et al.

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.


page 1

page 2

page 3

page 4


Detection of COVID-19 informative tweets using RoBERTa

Social media such as Twitter is a hotspot of user-generated information....

Towards Automatic Bot Detection in Twitter for Health-related Tasks

With the increasing use of social media data for health-related research...

Social Media and Health Misinformation during the US COVID Crisis

Health misinformation has been found to be prevalent on social media, pa...

Automating dynamic consent decisions for the processing of social media data in health research

Social media have become a rich source of data, particularly in health r...

Classifying COVID-19 vaccine narratives

COVID-19 vaccine hesitancy is widespread, despite governments' informati...