Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
48
datasets available to search
ShareScore release 0.7.1
Dataset results
48 results for “social media data”
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>
DECIPHER Social Media Data
<p><span>The DECIPHER dataset contains social media data from Twitter (now X) and YouTube in seven countries: Germany (DE), Italy (IT), Spain (ES), Sweden (SV), the Netherlands (NL), the United Kingdom (UK), and the United States (US).<span> </span>The dataset covers the period from January 1st, 2020 to December 31st, 2021 and is divided into two parts: government data (i.e., government messages) and public data (i.e., public comments and replies to government messages).</span></p>
Data from: Spatial and temporal dynamics and value of nature-based recreation, estimated via social media
Open the record for dataset details and reuse information.
Data from: Identifying conservation priorities for gorgonian forests in Italian coastal waters with multiple methods including citizen science and social media content analysis
Open the record for dataset details and reuse information.
Data from: Limits of use of social media for monitoring biosecurity events
Open the record for dataset details and reuse information.
Data from: Social media and Bitcoin Metrics: which words matter
Open the record for dataset details and reuse information.
Data from: Understanding sentiment of national park visitors from social media data
Open the record for dataset details and reuse information.
Harnessing Artificial Intelligence technology and social media data to support Cultural Ecosystem Service assessments
Open the record for dataset details and reuse information.
Olympic International Federation environmental sustainability progress and social media data
Open the record for dataset details and reuse information.
Data from social learning development, during COVID-19 how international students use media platforms as crowdsource technology to solve learning difficulties
<p>This study lens cannot detach mobile-learning communication from efficient broadband, during social distance protocol, for continuous learning. The study aims to self-actualized knowledge-sourcing among individuals in an interconnected cluster of multi-community platforms. Crowdsource is characterized by a communication science perspective using mobile-learning in groups and/or clusters. Integrated TPB and Bandura's Social Learning Theory (SLT) induced and investigated 361 respondents, stratified of students, teachers, and researchers during the COVID-19. The IBM Amos v. 25 for the analysis found R² = 0.92 and (65.4, 34.6) for male and female demographic respectively. Results found significant direct and indirect Attitude, Self-learner usefulness, and crowdsource were positive to actual performance. Where broadband moderated on mobile learning behavior run-up significantly. Mobile learning mediation gives in magnificent interactive operation. Generally, crowdsource at the individual level enhanced collaborated problem-solving tasks pro-COVID-19. An Individual's sourcing knowledge is a creative task and timely routine learning in any critical period. Results suggested mobility of learning makes a mountain of molehills in knowledge interactivity among learners in groups. Therefore, encourages the clustering of network learning, easing learning, through effective broadband social intervention phenomena.</p>
FactDrill: A Data Repository of Fact-checked Social Media Content to Study Fake News Incidents in India
<p>A dataset containing 22,435 fact-checked social media content to study fake news incidents in India. The dataset comprises news stories from 2013 to the year 2020, covering 13 different languages spoken in the country. There are 14 different attributes present in the dataset.</p>
Data Set for Calculating Social Media Rating in Assam Assembly Election 2021
<p>This data set contains the data used for calculating the Social Media Rating (SMR) of selected political parties and party presidents contesting the Assam Assembly Election 2021.</p>
Supplementary material 5 from: Neill AM, O`Donoghue C, Stout JC (2023) Spatial analysis of cultural ecosystem services using data from social media: A guide to model selection for research and practice. One Ecosystem 8: e95685. https://doi.org/10.3897/oneeco.8.e95685
MaxEnt Supplementary Info
Supplementary material 4 from: Neill AM, O`Donoghue C, Stout JC (2023) Spatial analysis of cultural ecosystem services using data from social media: A guide to model selection for research and practice. One Ecosystem 8: e95685. https://doi.org/10.3897/oneeco.8.e95685
Moran's I Correlograms
Supplementary material 3 from: Neill AM, O`Donoghue C, Stout JC (2023) Spatial analysis of cultural ecosystem services using data from social media: A guide to model selection for research and practice. One Ecosystem 8: e95685. https://doi.org/10.3897/oneeco.8.e95685
Sampled PUD occurrence
Supplementary material 2 from: Neill AM, O`Donoghue C, Stout JC (2023) Spatial analysis of cultural ecosystem services using data from social media: A guide to model selection for research and practice. One Ecosystem 8: e95685. https://doi.org/10.3897/oneeco.8.e95685
InVEST model configuration
Supplementary material 1 from: Neill AM, O`Donoghue C, Stout JC (2023) Spatial analysis of cultural ecosystem services using data from social media: A guide to model selection for research and practice. One Ecosystem 8: e95685. https://doi.org/10.3897/oneeco.8.e95685
Sites used for validation
Data from: Social media enhances languages differentiation: a mathematical description
Open the record for dataset details and reuse information.
Data from social learning development, during COVID-19 how international students use media platforms as crowdsource technology to solve learning difficulties
Open the record for dataset details and reuse information.
Data from: Chatty maps: constructing sound maps of urban areas from social media data
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.