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51 results for “Misinformation”
Global misinformation spillovers in the online vaccination debate before and during COVID-19 - ex
<p>The dataset includes all the ids of the tweets analysed in the paper "Global misinformation spillovers in the online vaccination debate before and during COVID-19", divided by lang.</p> <p>They are all the tweets containing vaccines keywords in 18 different European languages, spanning the period October 2019 - March 2021 (excluded 2020 Jan - 2020 Jun).</p> <p>Note that a large fraction of the tweets can't be retrieved because of the suspension of the accounts and the removal of the posts by the users, so the study is only partially reproducible.</p> <p>The unzipped dataset has a dimension of 8,0G.</p> <p>The authors of the paper are: Lenti J, Mejova Y, Kalimeri K, Panisson A, Paolotti D, Tizzani M, Starnini M.</p>
#Coronavirus on TikTok: User engagement with misinformation as a potential threat to public health behavior
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Twitter vaccine misinformation data
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Misinformation, internet honey trading, and beekeepers drive a plant invasion
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Data from: The Achilles' heel hypothesis: misinformed keystone individuals impair collective learning and reduce group success
Many animal societies rely on highly influential keystone individuals for proper functioning. When information quality is important for group success, such keystone individuals have the potential to diminish group performance if they possess inaccurate information. Here we test whether information quality (accurate or inaccurate) influences collective outcomes when keystone individuals are the first to acquire it. We trained keystone or generic individuals to attack or avoid novel stimuli and implanted these seed individuals within groups of naïve colony-mates. We subsequently tracked how quickly groups learned about their environment in situations that matched (accurate information) or mismatched (inaccurate information) the training of the seed individual. We found that colonies with just one accurately informed individual were quicker to learn to attack a novel prey stimulus than colonies with no informed individuals. However, this effect was no more pronounced when the informed individual was a keystone individual. In contrast, keystones with inaccurate information had larger effects than generic individuals with identical information: groups containing keystones with inaccurate information took longer to learn to attack/avoid prey/predator stimuli and gained less weight than groups harboring generic individuals with identical information. Our results convey that misinformed keystone individuals can become points of vulnerability for their societies.
Data from: Backfire effects after correcting misinformation are strongly associated with reliability
<p>The backfire effect is when a correction increases belief in the very misconception it is attempting to correct, and it is often used as a reason not to correct misinformation. The current study aimed to test whether correcting misinformation increases belief more than a no-correction control. Furthermore, we aimed to examine whether item-level differences in backfire rates were associated with test-retest reliability or theoretically meaningful factors. These factors included worldview-related attributes, namely perceived importance and strength of pre-correction belief, and familiarity-related attributes, namely perceived novelty and the illusory truth effect. In two nearly identical experiments, we conducted a longitudinal pre/post design with N = 388 and 532 participants. Participants rated 21 misinformation items and were assigned to a correction condition or test-retest control. We found that no items backfired more in the correction condition compared to test-retest control or initial belief ratings. Item backfire rates were strongly negatively correlated with item reliability (⍴ = -.61 / -.73) and did not correlate with worldview-related attributes. Familiarity-related attributes were significantly correlated with backfire rate, though they did not consistently account for unique variance beyond reliability. While there have been previous papers highlighting the non-replicable nature of backfire effects, the current findings provide a potential mechanism for this poor replicability. It is crucial for future research into backfire effects to use reliable measures, report the reliability of their measures, and take reliability into account in analyses. Furthermore, fact-checkers and communicators should not avoid giving corrective information due to backfire concerns.</p>
Misinformation from various social media platforms (August 21, 2018 - August 21, 2023)
<p>The dataset contains 226,681 misinformation from 6,235 distinct sources, <span>span</span><span>ning the period from August 21, 2018, to August 21, 2023.</span></p> <p>Here is an example from the dataset: </p> <p>{<br> "source": {<br> "id": null,<br> "name": "AllAfrica - Top Africa News"<br> },<br> "author": "",<br> "title": "Court of Public Opinion Is Real",<br> "description": "[The Herald] Tomorrow Zimbabwe is going to experience a reality show as the Constitutional Court presides over the election petition against President Mnangagwa's electoral victory.",<br> "url": "https://allafrica.com/stories/201808210151.html",<br> "urlToImage": "http://allafrica.com/static/images/structure/aa-logo-rgba-no-text-128x128.png",<br> "publishedAt": "2018-08-21T08:12:01Z",<br> "content": "opinion Tomorrow Zimbabwe is going to experience a reality show as the Constitutional Court presides over the election petition against President Mnangagwa's electoral victory. Lawyers are going to t… [+9083 chars]",<br> "filename": "Court of Public Opinion Is Real",<br> "createdon": "2023-08-22T08:01:22.056Z",<br> "md5": "14aff384df16e3f03e692ba092791e94",<br> "text": "[The Herald] Tomorrow Zimbabwe is going to experience a reality show as the Constitutional Court presides over the election petition against President Mnangagwa's electoral victory."<br>}</p> <p> </p>
Data for: Measuring receptivity to misinformation at scale on a social media platform
<p><strong>General Information</strong></p> <p>This contains the data for the publication:</p> <blockquote> <p>Tokita CK, Aslett K, Godel WP, Sanderson Z, Tucker JA, Nagler J, Persily N, Bonneau RA. (2024). Measuring receptivity to misinformation at scale on a social media platform. <em>PNAS Nexus</em>.</p> </blockquote> <p>Please see the above peer-reviewed article that resulted from this data for more details.</p> <p>Raw and original data are located in the `<em>data/</em>` directory, while data that is generated from intermediate analysis is found in the `<em>data_derived/</em>` directory.</p> <p>Please see the directory in the Github repository <a href="https://github.com/christokita/news-belief-at-scale">https://github.com/christokita/news-belief-at-scale </a>for the code that analyzes this data and generates derived data. The code expects both the `<em>data/</em>` and `<em>data_derived/</em>` folders to reside within the same directory.</p> <p>We also included a README with a description of each directory and subdirectory of the data.</p> <p> </p> <p><strong>Abstract (for main paper)</strong></p> <p>Measuring the impact of online misinformation is challenging. Traditional measures, such as user views or shares on social media, are incomplete because not everyone who is exposed to misinformation is equally likely to believe it. To address this issue, we developed a method that combines survey data with observational Twitter data to probabilistically estimate the number of users both exposed to and likely to believe a specific news story. As a proof of concept, we applied this method to 139 viral news articles and find that although false news reaches an audience with diverse political views, users who are both exposed and receptive to believing false news tend to have more extreme ideologies. These receptive users are also more likely to encounter misinformation earlier than those who are unlikely to believe it. This mismatch between overall user exposure and receptive user exposure underscores the limitation of relying solely on exposure or interaction data to measure the impact of misinformation, as well as the challenge of implementing effective interventions. To demonstrate how our approach can address this challenge, we then conducted data-driven simulations of common interventions used by social media platforms. We find that these interventions are only modestly effective at reducing exposure among users likely to believe misinformation, and their effectiveness quickly diminishes unless implemented soon after misinformation's initial spread. Our paper provides a more precise estimate of misinformation's impact by focusing on the exposure of users likely to believe it, offering insights for effective mitigation strategies on social media.</p> <p> </p> <p><strong>Significance Statement (for main paper)</strong></p> <p>As social media platforms grapple with misinformation, our study offers a new approach to measure its spread and impact. By combining survey data with social media data, we estimate not only the number of users exposed to false (and true) news but also the number of users likely to believe these news stories. We find that the impact of misinformation is not evenly distributed, with ideologically extreme users being more likely to see and believe false content, often encountering it before others. Our simulations suggest that current interventions may have limited effectiveness in reducing the exposure of receptive users. These findings highlight the need to consider individual user receptiveness when measuring misinformation's impact and developing policies to combat its spread.</p>
Marketing social e Misinformation no MCMV
<p>Video resumen de un artículo presentado en el VI Congreso Latinoamericano de Marketing Social en Brasil</p>
Global misinformation spillovers in the online vaccination debate before and during COVID-19
<p>The dataset includes all the ids of the tweets analysed in the paper "Global misinformation spillovers in the online vaccination debate before and during COVID-19", divided by lang.</p> <p>They are all the tweets containing vaccines keywords in 18 different European languages, spanning the period October 2019 - March 2021 (excluded 2020 Jan - 2020 Jun).</p> <p>Note that a large fraction of the tweets can't be retrieved because of the suspension of the accounts and the removal of the posts by the users, so the study is only partially reproducible.</p> <p>The unzipped dataset has a dimension of 8,0G.</p> <p>The authors of the paper are: Lenti J, Mejova Y, Kalimeri K, Panisson A, Paolotti D, Tizzani M, Starnini M.</p>
Community Partnership for Telehealth Solutions to Counter Misinformation and Achieve Equity
ClinicalTrials.gov study NCT06542835. IPD Sharing: NO. Countries: 1. Publications: 1.
Brief Informational Intervention for COVID-19 Misinformation Prophylaxis
ClinicalTrials.gov study NCT04557241. IPD Sharing: YES. Countries: 1. Publications: 6.
Impact of a Mock-up Fact-checking Extension on HPV Vaccine Misinformation: A Survey Experiment
ClinicalTrials.gov study NCT06405048. IPD Sharing: YES. Countries: 1. Publications: 17.
Contagious Misinformation Trial
ClinicalTrials.gov study NCT04112680. IPD Sharing: YES. Countries: 1. Publications: 1.
Data from: Misinformed leaders lose influence over pigeon flocks
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Data from: Backfire effects after correcting misinformation are strongly associated with reliability
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Data from: The Achilles' heel hypothesis: misinformed keystone individuals impair collective learning and reduce group success
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Data from: Neutralizing misinformation through inoculation: exposing misleading argumentation techniques reduces their influence
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Data from: Processing political misinformation: comprehending the Trump phenomenon
This study investigated the cognitive processing of true and false political information. Specifically, it examined the impact of source credibility on the assessment of veracity when information comes from a polarizing source (Experiment 1), and effectiveness of explanations when they come from one's own political party or an opposition party (Experiment 2). These experiments were conducted prior to the 2016 Presidential election. Participants rated their belief in factual and incorrect statements that President Trump made on the campaign trail; facts were subsequently affirmed and misinformation retracted. Participants then re-rated their belief immediately or after a delay. Experiment 1 found that (i) if information was attributed to Trump, Republican supporters of Trump believed it more than if it was presented without attribution, whereas the opposite was true for Democrats and (ii) although Trump supporters reduced their belief in misinformation items following a correction, they did not change their voting preferences. Experiment 2 revealed that the explanation's source had relatively little impact, and belief updating was more influenced by perceived credibility of the individual initially purporting the information. These findings suggest that people use political figures as a heuristic to guide evaluation of what is true or false, yet do not necessarily insist on veracity as a prerequisite for supporting political candidates.
MiDe22: An Annotated Multi-Event Tweet Dataset for Misinformation Detection
<p>The dataset is composed of 10,348 tweets: 5,284 for English and 5,064 for Turkish. Tweets in the dataset are human-annotated in terms of "false", "true", or "other". The dataset covers multiple topics: the Russia-Ukraine war, COVID-19 pandemic, Refugees, and additional miscellaneous events. The details can be found at https://github.com/avaapm/mide22</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.