Mapping the interaction between science and misinformation in COVID-19 tweets

2025-07-02

AI Summary

This study investigates the co-occurrence and co-dissemination mechanisms of scientific information and misinformation on Twitter (X) during the COVID-19 pandemic. Leveraging a large-scale corpus of 407 million tweets, we integrated URL credibility classifications from Media Bias/Fact Check and identified scholarly article citations using Altmetric to systematically analyze users’ cross-category sharing behaviors. Results reveal that approximately 45% of users who shared scientific content also disseminated unreliable information; their cited papers were disproportionately preprints, published in low-impact journals, sparsely cited, and subject to elevated retraction rates. Crucially, the findings indicate that scientific communication failure is not the primary driver of misinformation spread; rather, open science resources—particularly preprints—are susceptible to misuse or misinterpretation. This work provides novel empirical evidence and a risk-aware framework for understanding the “science–misinformation co-propagation” phenomenon.

Technology Category

• Application Domains: Misinformation & Fake News
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Responsible Web: Mitigating misinformation and disinformation
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks

Abstract

During the COVID-19 pandemic, scientific understanding related to the topic evolved rapidly. Along with scientific information being discussed widely, a large circulation of false information, labelled an infodemic by the WHO, emerged. Here, we study the interaction between misinformation and science on Twitter (now X) during the COVID-19 pandemic. We built a comprehensive database of $sim$407M COVID-19 related tweets and classified the reliability of URLs in the tweets based on Media Bias/Fact Check. In addition, we use Altmetric data to see whether a tweet refers to a scientific publication. We find that many users find that many users share both scientific and unreliable content; out of the $sim$1.2M users who share science, $45%$ also share unreliable content. Publications that are more frequently shared by users who also share unreliable content are more likely to be preprints, slightly more often retracted, have fewer citations, and are published in lower-impact journals on average. Our findings suggest that misinformation is not related to a ``deficit'' of science. In addition, our findings raise some critical questions about certain open science practices and their potential for misuse. Given the fundamental opposition between science and misinformation, our findings highlight the necessity for proactive scientific engagement on social media platforms to counter false narratives during global crises.

Problem

• Analyzes interaction between COVID-19 science and misinformation on Twitter
• Examines user behavior sharing both reliable and unreliable pandemic content
• Assesses impact of preprint sharing and open science misuse risks

Innovation

• Built database of 407M COVID-19 tweets
• Classified URL reliability using Media Bias
• Linked tweets to science via Altmetric