skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: The Presentation of Self in Virtual Life: Disinformation Warnings and the Spread of Misinformation Regarding COVID-19
In our analysis, we examine whether the labelling of social media posts as misinformation affects the subsequent sharing of those posts by social media users. Conventional understandings of the presentation-of-self and work in cognitive psychology provide different understandings of whether labelling misinformation in social media posts will reduce sharing behavior. Part of the problem with understanding whether interventions will work hinges on how closely social media interactions mirror other interpersonal interactions with friends and associates in the off-line world. Our analysis looks at rates of misinformation labelling during the height of the COVID-19 pandemic on Facebook and Twitter, and then assesses whether sharing behavior is deterred misinformation labels applied to social media posts. Our results suggest that labelling is relatively successful at lowering sharing behavior, and we discuss how our results contribute to a larger understanding of the role of existing inequalities and government responses to crises like the COVID-19 pandemic.  more » « less
Award ID(s):
2031768
PAR ID:
10478242
Author(s) / Creator(s):
; ; ; ;
Editor(s):
Redbird, Beth; Harbridge-Yong, Laurel; Mersey, Rachel Davis
Publisher / Repository:
Russell Sage Foundation
Date Published:
Journal Name:
RSF: The Russell Sage Journal of the Social Sciences
Edition / Version:
1
Volume:
8
Issue:
8
ISSN:
23378253
Page Range / eLocation ID:
52-68
Subject(s) / Keyword(s):
Covid-19 Misinformation Social Media
Format(s):
Medium: X Size: 1585 KB Other: PDF
Size(s):
1585 KB
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; ; ; (, PLOS ONE)
    Guidi, Barbara (Ed.)
    The COVID-19 pandemic brought widespread attention to an “infodemic” of potential health misinformation. This claim has not been assessed based on evidence. We evaluated if health misinformation became more common during the pandemic. We gathered about 325 million posts sharing URLs from Twitter and Facebook during the beginning of the pandemic (March 8-May 1, 2020) compared to the same period in 2019. We relied on source credibility as an accepted proxy for misinformation across this database. Human annotators also coded a subsample of 3000 posts with URLs for misinformation. Posts about COVID-19 were 0.37 times as likely to link to “not credible” sources and 1.13 times more likely to link to “more credible” sources than prior to the pandemic. Posts linking to “not credible” sources were 3.67 times more likely to include misinformation compared to posts from “more credible” sources. Thus, during the earliest stages of the pandemic, when claims of an infodemic emerged, social media contained proportionally less misinformation than expected based on the prior year. Our results suggest that widespread health misinformation is not unique to COVID-19. Rather, it is a systemic feature of online health communication that can adversely impact public health behaviors and must therefore be addressed. 
    more » « less
  2. ; ; ; ; (, Scientific Reports)
    Abstract Misinformation about the COVID-19 pandemic proliferated widely on social media platforms during the course of the health crisis. Experts have speculated that consuming misinformation online can potentially worsen the mental health of individuals, by causing heightened anxiety, stress, and even suicidal ideation. The present study aims to quantify the causal relationship between sharing misinformation, a strong indicator of consuming misinformation, and experiencing exacerbated anxiety. We conduct a large-scale observational study spanning over 80 million Twitter posts made by 76,985 Twitter users during an 18.5 month period. The results from this study demonstrate that users who shared COVID-19 misinformation experienced approximately two times additional increase in anxiety when compared to similar users who did not share misinformation. Socio-demographic analysis reveals that women, racial minorities, and individuals with lower levels of education in the United States experienced a disproportionately higher increase in anxiety when compared to the other users. These findings shed light on the mental health costs of consuming online misinformation. The work bears practical implications for social media platforms in curbing the adverse psychological impacts of misinformation, while also upholding the ethos of an online public sphere. 
    more » « less
  3. ; ; (, Proceedings of the ACM on Human-Computer Interaction)
    While COVID-19 text misinformation has already been investigated by various scholars, fewer research efforts have been devoted to characterizing and understanding COVID-19 misinformation that is carried out through visuals like photographs and memes. In this paper, we present a mixed-method analysis of image-based COVID-19 misinformation in 2020 on Twitter. We deploy a computational pipeline to identify COVID-19 related tweets, download the images contained in them, and group together visually similar images. We then develop a codebook to characterize COVID-19 misinformation and manually label images as misinformation or not. Finally, we perform a quantitative analysis of tweets containing COVID-19 misinformation images. We identify five types of COVID-19 misinformation, from a wrong understanding of the threat severity of COVID-19 to the promotion of fake cures and conspiracy theories. We also find that tweets containing COVID-19 misinformation images do not receive more interactions than baseline tweets with random images posted by the same set of users. As for temporal properties, COVID-19 misinformation images are shared for longer periods of time than non-misinformation ones, as well as have longer burst times. %\ywi added "have'' %\ywFor RQ2, we compare non-misinformation images instead of random images, and so it is not a direct comparison. When looking at the users sharing COVID-19 misinformation images on Twitter from the perspective of their political leanings, we find that pro-Democrat and pro-Republican users share a similar amount of tweets containing misleading or false COVID-19 images. However, the types of images that they share are different: while pro-Democrat users focus on misleading claims about the Trump administration's response to the pandemic, as well as often sharing manipulated images intended as satire, pro-Republican users often promote hydroxychloroquine, an ineffective medicine against COVID-19, as well as conspiracy theories about the origin of the virus. Our analysis sets a basis for better understanding COVID-19 misinformation images on social media and the nuances in effectively moderate them. 
    more » « less
  4. ; ; ; ; ; (, Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020)
    null (Ed.)
    The ongoing pandemic has heightened the need for developing tools to flag COVID-19-related misinformation on the internet, specifically on social media such as Twitter. However, due to novel language and the rapid change of information, existing misinformation detection datasets are not effective for evaluating systems designed to detect misinformation on this topic. Misinformation detection can be divided into two sub-tasks: (i) retrieval of misconceptions relevant to posts being checked for veracity, and (ii) stance detection to identify whether the posts Agree, Disagree, or express No Stance towards the retrieved misconceptions. To facilitate research on this task, we release COVIDLies (https://ucinlp.github.io/covid19 ), a dataset of 6761 expert-annotated tweets to evaluate the performance of misinformation detection systems on 86 different pieces of COVID-19 related misinformation. We evaluate existing NLP systems on this dataset, providing initial benchmarks and identifying key challenges for future models to improve upon. 
    more » « less
  5. ; ; (, ACM)
    Attempting to make sense of a phenomenon or crisis, social media users often share data visualizations and interpretations that can be erroneous or misleading. Prior work has studied how data visualizations can mislead, but do misleading visualizations reach a broad social media audience? And if so, do users amplify or challenge misleading interpretations? To answer these questions, we conducted a mixed-methods analysis of the public's engagement with data visualization posts about COVID-19 on Twitter. Compared to posts with accurate visual insights, our results show that posts with misleading visualizations garner more replies in which the audiences point out nuanced fallacies and caveats in data interpretations. Based on the results of our thematic analysis of engagement, we identify and discuss important opportunities and limitations to effectively leveraging crowdsourced assessments to address data-driven misinformation. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email