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Dataset results
6 results for “us elections”
Public Dataset for "Did State-sponsored Trolls Shape the 2016 US Presidential Election Discourse? Quantifying Influence on Twitter"
<p>Dataset for the "Did State-sponsored Trolls Shape the 2016 US Presidential Election Discourse? Quantifying Influence on Twitter" paper. </p> <p>The full text of the paper can be found <a href="https://zenodo.org/record/4699959#.YngKatNBy3K">here</a>.</p> <p>The folder "Tweet_IDs" contains the complete list of the 152,514,929 tweet IDs (together with their timestamps) which we used for the analysis in the study: "Did State-sponsored Trolls Shape the 2016 US Presidential Election Discourse? Quantifying Influence on Twitter"</p> <p>by Nikos Salamanos, Michael J. Jensen, Costas Iordanou and Michael Sirivianos</p> <p>We have split the tweets into separate .zip files based on the date listed in their timestamps.</p> <p>The crawling took place from September 21 to November 7, 2016 (47 days; we did not collect data on 02/10/2016).</p> <p>Each "tweet_day_X.zip" file contains the file "tweet_day_X.csv", where X in [1,2,...,47]. For instance, the file "tweets_day_1.zip" contains the tweets of the 1st day: 09/21/2016.</p> <p>Please cite the paper in any published work that uses any of these resources. </p> <p>@misc{nikos_salamanos_2021_4699959,<br> author = {Nikos Salamanos and<br> Michael J. Jensen and<br> Costas Iordanou and<br> Michael Sirivianos},<br> title = {{Did State-sponsored Trolls Shape the 2016 US <br> Presidential Election Discourse? Quantifying<br> Influence on Twitter}},<br> month = apr,<br> year = 2021,<br> publisher = {Zenodo},<br> version = 3,<br> doi = {10.5281/zenodo.4699959},<br> url = {https://doi.org/10.5281/zenodo.4699959}<br> }</p>
LLMs Languages Least Moderated: Testing Cross-National Moderation in the context of the EU and the US Elections on Chatbots
<p>AI Forensics had <a href="https://aiforensics.org/work/bing-chat-elections">previously exposed</a> that Microsoft Copilot's answers to simple election-related questions contained factual errors 30% of the time. In collaboration with Nieuwsuur, we uncovered how chatbots can recommend and support the dissemination of disinformation as a campaign strategy. Following those investigations as well as a request for information from the European Commission, Microsoft and Google introduced “moderation layers" to their chatbots so that they refuse to answer election-related prompts.</p> <p><strong>This dataset was produced as part<span> of project "LLMs: Languages Least Moderated" at the 2024 Digital Methods Summer School and Data Sprint, which AI Forensics facilitated</span> to allow participants to evaluate and compare the effectiveness of these safeguards in different scenarios.</strong> In particular, we investigated the consistency with which electoral moderation was triggered, depending the language of the prompt and the electoral context.</p>
Multifaceted Online Coordinated Behavior in the 2020 US Presidential Election
<p>This dataset contains ~140M tweets related to the 2020 United States Presidential Election, published and collected between October 2, 2020, and December 2, 2020. In addition, we provide nodes and edges of the superspreader user similarity network, as described in the paper below.</p> <p><strong>Tardelli, S., Nizzoli, L., Avvenuti, M., Cresci, S., & Tesconi, M. Multifaceted Online Coordinated Behavior in the 2020 US Presidential Election.</strong></p> <p>In detail, the dataset consists of:</p> <ul> <li><em>tweet-ids.csv.zip</em></li> <li><em>user_similarity_nework_nodes.csv</em>: a CSV file with the columns "id" and "cluster," relating to the nodes of the superspreader user similarity network mentioned in the paper.</li> <li><em>user_similarity_nework_edges.csv</em>: a CSV file with the columns "source," "target," "weight," and "alpha" relating to the edges of the superspreader user similarity network mentioned in the paper.</li> </ul>
Viral tweets with fakenews on 2016 US election day
<p>Collection of tweets related to the 2016 US election that went viral during the election day (Nov 8th). Viral tweets are those that achieved the 1000-retweet threshold duing the collection period.</p> <p>We queried Twitter's streaming API using the hashtags #MyVote2016, #ElectionDay, #electionnight, and the user handles @realDonaldTrump and @HillaryClinton.</p> <p>Tweets have been labelled as containing fake news or not by one expert. A fake news is one the following:</p> <p>Serious fabrication<br> Large-scale hoaxes<br> Jokes taken at face value<br> Slanted reporting of real facts<br> Stories where the 'truth' is contentious</p>
Fakenews on 2016 US elections viral tweets (November 2016 - March 2017)
<p>Collection of tweets related to the 2016 US election that went viral between election day (Nov 8th) and March 2017. Viral tweets are those that achieved the 1000-retweet threshold during the collection period. We queried Twitter's streaming API using the hashtags <em>#MyVote2016</em>, <em>#ElectionDay</em>, <em>#electionnight</em>, and the user handles <em>@realDonaldTrump</em> and<em> @HillaryClinton</em>.</p> <p>Tweets have been labelled as containing fake news or not by two sets of people. A fake news is one the following:</p> <ul> <li>Serious fabrication</li> <li>Large-scale hoaxes</li> <li>Jokes taken at face value</li> <li>Slanted reporting of real facts</li> <li>Stories where the 'truth' is contentious</li> </ul>
Dexmedetomidine and Morphine as Adjuvants to US Guided Erector Spinae Plane Blocks in Elective Thoracic Surgeries
ClinicalTrials.gov study NCT05843344. IPD Sharing: NO. Countries: 1. Publications: 10.
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OpenNeuro
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