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15 results for “social media platforms”
iDRAMA-Scored-2024: A Dataset of the Scored Social Media Platform from 2020 to 2023
<p>ABSTRACT<br>---------------<br>Online web communities often face bans for violating platform policies, encouraging their migration to alternative platforms. This migration, however, can result in increased toxicity and unforeseen consequences on the new platform. In recent years, researchers have collected data from many alternative platforms, indicating coordinated efforts leading to offline events, conspiracy movements, hate speech propagation, and harassment. Thus, it becomes crucial to characterize and understand these alternative platforms. To advance research in this direction, we collect and release a large-scale dataset from Scored -- an alternative Reddit platform that sheltered banned fringe communities, for example, c/TheDonald (a prominent right-wing community) and c/GreatAwakening (a conspiratorial community). Over four years, we collected approximately 57M posts from Scored, with at least 58 communities identified as migrating from Reddit and over 950 communities created since the platform's inception. Furthermore, we provide sentence embeddings of all posts in our dataset, generated through a state-of-the-art model, to further advance the field in characterizing the discussions within these communities. We aim to provide these resources to facilitate their investigations without the need for extensive data collection and processing efforts.</p> <ul> <li>Scored platform: <a href="https://scored.co">https://scored.co</a></li> <li>Link to paper: <a href="https://arxiv.org/abs/2405.10233">https://arxiv.org/abs/2405.10233</a></li> <li>License: <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en">CC BY-NC-SA 4.0</a></li> </ul> <h1>Repository links</h1> <ul> <li><strong>Zenodo:</strong> From Zenodo, researchers can download `lite` version of this dataset, which includes only 57M posts from Scored (not the sentence embeddings).</li> <li><strong>Github:</strong> The main repository of this dataset, where we provide code-snippets to get started with this dataset. <ul> <li>Link here: <a href="https://github.com/idramalab/iDRAMA-scored-2024">https://github.com/idramalab/iDRAMA-scored-2024</a></li> </ul> </li> <li><strong>Huggingface:</strong> On Huggingface, we provide complete dataset with senetence embeddings.<br> <ul> <li>Link here: <a href="https://hf.co/datasets/iDRAMALab/iDRAMA-scored-2024">https://hf.co/datasets/iDRAMALab/iDRAMA-scored-2024</a></li> </ul> </li> </ul> <h1>Dataset Info</h1> <table> <tbody> <tr> <td><strong>File-name</strong></td> <td><strong>Data-points</strong></td> </tr> <tr> <td>comments-2020</td> <td>12,774,203</td> </tr> <tr> <td>comments-2021</td> <td>16,097,941</td> </tr> <tr> <td>comments-2022</td> <td>12,730,301</td> </tr> <tr> <td>comments-2023</td> <td>8,919,159</td> </tr> <tr> <td>submissions-2020-to-2023</td> <td>6,293,980</td> </tr> </tbody> </table> <h1>Authorship</h1> <p>This dataset is published at "AAAI ICWSM 2024 (INTERNATIONAL AAAI CONFERENCE ON WEB AND SOCIAL MEDIA)" hosted at Buffalo, NY, USA.</p> <ul> <li><strong>Academic Organization: </strong><a href="https://idrama.science/people/">iDRAMA Lab</a></li> <li><strong>Affiliation:</strong> Binghamton University, Boston University, University of California Riverside</li> </ul> <h1>Licensing</h1> <p>This dataset is available for free to use under terms of the non-commercial license <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en">CC BY-NC-SA 4.0</a>.</p> <h1>Citation</h1> <blockquote> <p>@inproceedings{patel2024idrama,<br> title={iDRAMA-Scored-2024: A Dataset of the Scored Social Media Platform from 2020 to 2023},<br> author={Patel, Jay and Paudel, Pujan and De Cristofaro, Emiliano and Stringhini, Gianluca and Blackburn, Jeremy},<br> booktitle={Proceedings of the International AAAI Conference on Web and Social Media},<br> volume={18},<br> pages={2014--2024},<br> year={2024},<br> issn = {2334-0770},<br> doi = {10.1609/icwsm.v18i1.31444},<br>}</p> </blockquote>
Generative AI in Crowdwork for Web and Social Media Research: A Survey of Workers at Three Platforms
<p>Data generated for the paper "<span>Generative AI in Crowdwork for Web and Social Media Research:</span><br><span>A Survey of Workers at Three Platforms</span>" presented at ICWSM 2024.</p>
Incentivizing news consumption on social media platforms using large language models and realistic bot accounts
<p>This project examines how to enhance users' exposure to and engagement with verified and ideologically balanced news in an ecologically valid setting. We rely on a large-scale two-week long field experiment on 28,457 Twitter users. We created 28 bots utilizing GPT-2 that replied to users tweeting about sports, entertainment, or lifestyle with a contextual reply containing two hardcoded elements: a URL to the topic-relevant section of quality news organization and an encouragement to follow its Twitter account. Treated users were randomly assigned to receive responses by bots presented as female or male. We examine whether our intervention enhances the following of news media organization, the sharing/liking of news content and the tweeting/liking of political content. We find that the treated users followed more news accounts and the users in the female bot treatment were more likely to like news content than the control.</p>
Modelling Political Aggression On Social Media Platforms
<p>This dataset was curated from social media for the purpose of research. LICENSE: CC-BY-SA-NC 4.0</p>
Incentivizing news consumption on social media platforms using large language models and realistic bot accounts
Open the record for dataset details and reuse information.
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 Shouting into the void: A database of the alternative social media platform Gab
<p>As social media platforms have increased their role as content moderators, alternative social media platforms have emerged with fewer rules and policies on moderating content. One such platform is Gab, a social media platform similar to Twitter that champions free speech with minimal standards on content. Early research has linked this platform with alt-right, hate speech, conspiracy theories, and other alternative content that is sometimes marginalized in mainstream social media platforms like Twitter and Facebook. In an effort to provide a means for researchers to study this platform, we introduce a database of 35,851,186 posts (with additional edit histories) and 819,957 user profiles web-scraped from Gab between August 2016 and December 2018. In this paper, we outline our data collection process, provide descriptive statistics, consider the ethics of our data collection process, and provide suggested avenues of inquiry for researchers interested in analyzing our database.</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>
A multi-platform collection of social media posts about the 2022 U.S. midterm elections
<p>The details and full author list can be found at https://github.com/osome-iu/MEIU22 .</p> <p>Here we only host the 4chan data. Data from other platforms (Twitter, Facebook, and Reddit) are on GitHub.</p>
The Use of Social Media Platform to Continuously Support Pregnant Women With Gestational Diabetes Mellitus
ClinicalTrials.gov study NCT06292871. IPD Sharing: NO. Countries: 1. Publications: 4.
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>
An Exploratory Empirical Study of Trust & Safety Engineering in Open-Source Social Media Platforms
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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.
A Bibliometric Study on the Filter Bubble Phenomenon in Online Social Media Platforms
Open the record for dataset details and reuse information.
Leveraging WeChat Social-Media and Messaging Platform to Increase Physical Activity in Chinese Glaucoma Patients
ClinicalTrials.gov study NCT03567226. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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