"Thought I'd Share First" and Other Conspiracy Theory Tweets from the COVID-19 Infodemic: Exploratory Study

TL;DR

This study analyzes COVID-19 misinformation on social media, showing how conspiracy theories evolve and demonstrating the effectiveness of machine learning in identifying misinformation for better public health messaging.

Contribution

It introduces an approach combining model-labeled data and machine learning to detect and analyze evolving health-related misinformation on social media.

Findings

Random forest classifiers achieved F1 scores up to 0.857 for certain conspiracy theories.

Misinformation tweets tend to have more negative sentiment than non-misinformation tweets.

Conspiracy theories evolve over time, incorporating unrelated details and real-world events.

Abstract

Background: The COVID-19 outbreak has left many people isolated within their homes; these people are turning to social media for news and social connection, which leaves them vulnerable to believing and sharing misinformation. Health-related misinformation threatens adherence to public health messaging, and monitoring its spread on social media is critical to understanding the evolution of ideas that have potentially negative public health impacts. Results: Analysis using model-labeled data was beneficial for increasing the proportion of data matching misinformation indicators. Random forest classifier metrics varied across the four conspiracy theories considered (F1 scores between 0.347 and 0.857); this performance increased as the given conspiracy theory was more narrowly defined. We showed that misinformation tweets demonstrate more negative sentiment when compared to nonmisinformation tweets and that theories evolve over time, incorporating details from unrelated conspiracy theories as well as real-world events. Conclusions: Although we focus here on health-related misinformation, this combination of approaches is not specific to public health and is valuable for characterizing misinformation in general, which is an important first step in creating targeted messaging to counteract its spread. Initial messaging should aim to preempt generalized misinformation before it becomes widespread, while later messaging will need to target evolving conspiracy theories and the new facets of each as they become incorporated.