Misinformation poses a threat to democracy and to people’s health. Reliability criteria for news websites can help people identify misinformation. But despite their importance, there has been no empirically substantiated list of criteria for distinguishing reliable from unreliable news websites. We identify reliability criteria, describe how they are applied in practice, and compare them to prior work. Based on our analysis, we distinguish between manipulable and less manipulable criteria and compare politically diverse laypeople as end-users and journalists as expert users. We discuss 11 widely recognized criteria, including the following 6 criteria that are difficult to manipulate: content, political alignment, authors, professional standards, what sources are used, and a website’s reputation. Finally, we describe how technology may be able to support people in applying these criteria in practice to assess the reliability of websites.
- Award ID(s):
- 2017655
- PAR ID:
- 10479216
- Publisher / Repository:
- Sage
- Date Published:
- Journal Name:
- New Media & Society
- ISSN:
- 1461-4448
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
-
Online misinformation is believed to have contributed to vaccine hesitancy during the Covid-19 pandemic, highlighting concerns about social media’s destabilizing role in public life. Previous research identified a link between political conservatism and sharing misinformation; however, it is not clear how partisanship affects how much misinformation people see online. As a result, we do not know whether partisanship drives exposure to misinformation or people selectively share misinformation despite being exposed to factual content. To address this question, we study Twitter discussions about the Covid-19 pandemic, classifying users along the political and factual spectrum based on the information sources they share. In addition, we quantify exposure through retweet interactions. We uncover partisan asymmetries in the exposure to misinformation: conservatives are more likely to see and share misinformation, and while users’ connections expose them to ideologically congruent content, the interactions between political and factual dimensions create conditions for the highly polarized users—hardline conservatives and liberals—to amplify misinformation. Overall, however, misinformation receives less attention than factual content and political moderates, the bulk of users in our sample, help filter out misinformation. Identifying the extent of polarization and how political ideology exacerbates misinformation can help public health experts and policy makers improve their messaging.more » « less
-
more » « less
Abstract We review several topics of philosophical interest connected to misleading online content. First we consider proposed definitions of different types of misleading content. Then we consider the epistemology of misinformation, focusing on approaches from virtue epistemology and social epistemology. Finally we discuss how misinformation is related to belief polarization, and argue that models of rational polarization present special challenges for conceptualizing fake news and misinformation.
-
Though significant efforts such as removing false claims and promoting reliable sources have been increased to combat COVID-19 misinfodemic, it remains an unsolved societal challenge if lacking a proper understanding of susceptible online users, i.e., those who are likely to be attracted by, believe and spread misinformation. This study attempts to answer who constitutes the population vulnerable to the online misinformation in the pandemic, and what are the robust features and short-term behavior signals that distinguish susceptible users from others. Using a 6-month longitudinal user panel on Twitter collected from a geopolitically diverse network-stratified samples in the US, we distinguish different types of users, ranging from social bots to humans with various level of engagement with COVID-related misinformation. We then identify users' online features and situational predictors that correlate with their susceptibility to COVID-19 misinformation. This work brings unique contributions: First, contrary to the prior studies on bot influence, our analysis shows that social bots' contribution to misinformation sharing was surprisingly low, and human-like users' misinformation behaviors exhibit heterogeneity and temporal variability. While the sharing of misinformation was highly concentrated, the risk of occasionally sharing misinformation for average users remained alarmingly high. Second, our findings highlight the political sensitivity activeness and responsiveness to emotionally-charged content among susceptible users. Third, we demonstrate a feasible solution to efficiently predict users' transient susceptibility solely based on their short-term news consumption and exposure from their networks. Our work has an implication in designing effective intervention mechanism to mitigate the misinformation dissipation.more » « less
-
more » « less
Social scientists and computer scientists are increasingly using observational digital trace data and analyzing these data post hoc to understand the content people are exposed to online. However, these content collection efforts may be systematically biased when the entirety of the data cannot be captured retroactively. We call this often unstated assumption the problematic assumption of accessibility. To examine the extent to which this assumption may be problematic, we identify 107k hard news and misinformation web pages visited by a representative panel of 1,238 American adults and record the degree to which the web pages individuals visited were accessible via successful web scrapes or inaccessible via unsuccessful scrapes. While we find that the URLs collected are largely accessible and with unrestricted content, we find there are systematic biases in which URLs are restricted, return an error, or are inaccessible. For example, conservative misinformation URLs are more likely to be inaccessible than other types of misinformation. We suggest how social scientists should capture and report digital trace and web scraping data.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1177/14614448231196863
