Digital Platforms’ Governance: missing data & information to monitor, audit & investigate platforms’ misinformation interventions
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Misinformation
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There is concern that many ills in Western societies are caused by misinformation. Some researchers argue that misinformation is merely a symptom, not a cause. This is a false dichotomy, and research should differentiate between dimensions of misinformation in these evaluations. There is concern that many ills in Western societies are caused by misinformation. Some researchers argue that misinformation is merely a symptom, not a cause. This appears a false dichotomy, and research should differentiate between dimensions of misinformation in these evaluations.
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Although billions of COVID-19 vaccines have been administered, too many people remain hesitant. Misinformation about the COVID-19 vaccines, propagating on social media, is believed to drive hesitancy towards vaccination. However, exposure to misinformation does not necessarily indicate misinformation adoption. In this paper we describe a novel framework for identifying the stance towards misinformation, relying on attitude consistency and its properties. The interactions between attitude consistency, adoption or rejection of misinformation and the content of microblogs are exploited in a novel neural architecture, where the stance towards misinformation is organized in a knowledge graph. This new neural framework is enabling the identification of stance towards misinformation about COVID-19 vaccines with state-of-the-art results. The experiments are performed on a new dataset of misinformation towards COVID-19 vaccines, called CoVaxLies, collected from recent Twitter discourse. Because CoVaxLies provides a taxonomy of the misinformation about COVID-19 vaccines, we are able to show which type of misinformation is mostly adopted and which is mostly rejected.
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Research suggests that many Americans consume news from social media. This paper examines the role the social media site, Reddit, has in propagating misinformation and Covid-related misinformation in the subreddits r/liberal and r/conservative. The results produced in this paper suggest that both subreddits contribute significantly to the propagation of misinformation. Both misinformation and reception steadily increase throughout time from 01 January 2015 to 01 January 2022. The increase in reception over time could be indicative of an increase in engagement. The accompanying increase in misinformation could signify a continued increase in the propagation of misinformation in the future.
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Our study examines Facebook posts containing nine prominent COVID-19 vaccine misinformation topics that circulated on the platform between March 1st, 2020 and March 1st, 2021. We first identify misinformation spreaders and fact checkers, further dividing the latter group into those who repeat misinformation to debunk the false claim and those who share correct information without repeating the misinformation. Our analysis shows that, on Facebook, there are almost as many fact checkers as misinformation spreaders. In particular, fact checkers’ posts that repeat the original misinformation received significantly more comments than posts from misinformation spreaders. However, we found that misinformation spreaders were far more likely to take on central positions in the misinformation URL co-sharing network than fact checkers. This demonstrates the remarkable ability of misinformation spreaders to coordinate communication strategies across topics.
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We define scientific misinformation as publicly available information that is misleading or deceptive relative to the best available scientific evidence and that runs contrary to statements by actors or institutions who adhere to scientific principles. Scientific misinformation violates the supposition that claims should be based on scientific evidence and relevant expertise. As such, misinformation is observable and measurable, but research on scientific misinformation to date has often missed opportunities to clearly articulate units of analysis, to consult with experts, and to look beyond convenient sources of misinformation such as social media content. We outline the ways in which scientific misinformation can be thought of as a disorder of public science, identify its specific types and the ways in which it can be measured, and argue that researchers and public actors should do more to connect measurements of misinformation with measurements of effect.
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The spread of online misinformation has become a major global risk. Understanding how misinformation propagates on social media is vital. While prior studies suggest that the content factors, such as emotion and topic in texts, are closely related to the dissemination of misinformation, the effect of users' commentary on misinformation during its spreading on social media has been long overlooked. In this paper, we identify the patterns of "misinformation mutation" which captures ways misinformation is commented and shared by social media users. Our study focus on misinformation originated from digital news outlets and shared on Twitter. Through an analysis of over 240 thousand tweets capturing how users share COVID-19 pandemic-related misinformation news over a five-month period, we study the prevalence and factors of the misinformation mutation. We examine the different kinds of mutation in terms of how the article was cited from the news source, and how the content was edited, compared with its original text, and test the relationship between misinformation's mutation and its spread on Twitter. Our results indicate a positive relationship between information mutation and spreading outcome – and such a relationship is stronger for news articles shared from non-credible outlets than those from credible ones. This study provides the first quantitative evidence of how misinformation propagation may be exacerbated by users' commentary. Our study contributes to the understanding of misinformation spreading on social media and has implications for countering misinformation.
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2019-20 coronavirus outbreak
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How do Danes interact with misinformation on social media? Which statements and arguments do they use to spread and reject misinformation? We investigate digital misinformation during the Covid-19 pandemic and analyze how citizens spread and reject information about facemasks on Twitter in Denmark. Our study shows that the amount of misinformation is limited, but that false claims are not predominantly countered through fact-checking or dialogue. Instead, users who reject the misinformation often use irony and condescending comments to distance themselves from those who spread misinformation and whose concerns are thus not taken seriously. Our findings question citizens’ ability to effectively correct misinformation online and point to the importance of group affiliation and social status not only in spreading, but also in rejecting digital misinformation.
Keywords: Covid-19, humor, misinformation, social status, Twitter
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