COVID-19 on Social Media: Analyzing Misinformation in Twitter Conversations

Abstract

The ongoing Coronavirus (COVID-19) pandemic highlights the inter-connectedness of our present-day globalized world. With social distancing policies in place, virtual communication has become an important source of (mis)information. As increasing number of people rely on social media platforms for news, identifying misinformation and uncovering the nature of online discourse around COVID-19 has emerged as a critical task. To this end, we collected streaming data related to COVID-19 using the Twitter API, starting March 1, 2020. We identified unreliable and misleading contents based on fact-checking sources, and examined the narratives promoted in misinformation tweets, along with the distribution of engagements with these tweets. In addition, we provide examples of the spreading patterns of prominent misinformation tweets. The analysis is presented and updated on a publically accessible dashboard (https://usc-melady.github.io/COVID-19-Tweet-Analysis) to track the nature of online discourse and misinformation about COVID-19 on Twitter from March 1 - June 5, 2020. The dashboard provides a daily list of identified misinformation tweets, along with topics, sentiments, and emerging trends in the COVID-19 Twitter discourse. The dashboard is provided to improve visibility into the nature and quality of information shared online, and provide real-time access to insights and information extracted from the dataset.

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Cited by in corpus (13)

• Classification Aware Neural Topic Model and its Application on a New COVID-19 Disinformation Corpus
• A First Instagram Dataset on COVID-19
• Characterizing COVID-19 Misinformation Communities Using a Novel Twitter Dataset
• Large-scale, Language-agnostic Discourse Classification of Tweets During COVID-19
• Detecting Topic and Sentiment Dynamics Due to COVID-19 Pandemic Using Social Media
• Exploratory Analysis of COVID-19 Related Tweets in North America to Inform Public Health Institutes
• VigDet: Knowledge Informed Neural Temporal Point Process for Coordination Detection on Social Media
• CO.ME.T.A. -- covid-19 media textual analysis. A dashboard for media monitoring
• People Still Care About Facts: Twitter Users Engage More with Factual Discourse than Misinformation--A Comparison Between COVID and General Narratives on Twitter
• An Exploratory Study of COVID-19 Information on Twitter in the Greater Region
• Corrective Information Does Not Necessarily Curb Social Disruption
• An Exploration of Geo-temporal Characteristics of Users' Reactions on Social Media During the Pandemic
• Online geolocalized emotion across US cities during the COVID crisis: Universality, policy response, and connection with local mobility