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1,041 results for “elections”
#cdnpoli and the Twittersphere: User mentions during the 2019 Federal Election
<p>This is a raw dataset containing the @mention (username) frequencies within tweets that contained one of 150 various keywords and hashtags related to Canadian politics. The dataset begins on October 1 2019 and ends on November 15 2019, providing a roughly 6-week window around the October 21 election. Data was captured using the Digital Methods Initiative's DMI-TCAT toolkit (github.com/dmi-tcat). Part of a larger project supported by a grant from the Canadian Heritage Fund.</p>
Data from: Statistical analysis of the presidential elections in Belarus in 2020
<p>Elections in Belarus attract much attention around the world. The election result is declared as a victory of Mr. Lukashenko with 80% votes. It is interesting to give the simplest statistical analysis of this victory. According to Belarus law, protocols of precinct election commissions (PECs) must be posted up just after the election procedure, so that everybody could take a photograph of the protocols. Currently, 1527 of the 5767 protocols of PECs are available in the open access at <a href="https://docs.google.com/spreadsheets/d/17aK3JxBTGtzULB0-YZGOF0hJwhuViHO3/edit#gid=84585767">https://docs.google.com/spreadsheets/d/17aK3JxBTGtzULB0-YZGOF0hJwhuViHO3/edit#gid=84585767</a>. We focus an attention on two arrays of numbers taken from these photographs. Namely, the number N<sub>i</sub> of voters at some polling station and the number M<sub>i</sub> of voters for Mr. Lukashenko at the same polling station. These numbers give a possibility to calculate the average percentage of those who voted for Mr. Lukashenko, which turns out to be <span>about 60%.</span> That is, a random sample approximately of ¼ of total number of protocol gives a value that differs at about 20% from the declared total value 8<span>0%.</span> Using Monte-Carlo simulation we have calculated a probability of this event and obtain <span>less than one part in million.</span> Next we have considered N<sub>i </sub>and M<sub>i</sub> as the random variables and calculate probability distribution functions for M<sub>i</sub>/N<sub>i</sub> and M<sub>i</sub>/<N<sub>i</sub>> quantities. First function f(x) is of non Gaussian form and has a maximum at x≈0.6, and, an additional maximum at x≈0.8. Second function f(y) has only one maximum at y≈0.6. <span>One the possible explanations is that the correlation (i.e. maximum) in distribution </span>f(x) at x≈0.8 <span> arises due to artificial trimming of the percentage of those who voted for Lukashenko to </span>8<span>0% in some polling stations.</span></p>
Brazilian Elections YouTube Comments Dataset (2018-2022)
<p>This dataset provides a comprehensive analysis of YouTube comments related to Brazilian election candidates for the years 2018 and 2022. The dataset is organized into two folders:</p><p><strong>video_gender_mapping_BR_elections</strong>: This folder contains individual files, each corresponding to a YouTube channel analyzed. Each file includes the following fields:</p><ul><li><strong>channelId</strong>: YouTube channel identifier</li><li><strong>channelTitle</strong>: YouTube channel title</li><li><strong>videoId</strong>: YouTube video identifier</li><li><strong>publishedAt</strong>: Datetime of when the video was published</li><li><strong>year_month</strong>: Year and month of when the video was published</li><li><strong>views</strong>: Number of views of the video</li><li><strong>comments</strong>: Number of total comments on the video</li><li><strong>likes</strong>: Number of total likes on the video</li><li><strong>match_type</strong>: Type of match considering candidate name and video title/description (T for Title, D for Description, TD for both)</li><li><strong>match_result</strong>: Array of JSON containing positive matches based on race, elective office, title/description match, and gender</li><li><strong>match_cargos</strong>: List of elective offices that appear in the match</li><li><strong>match_genero</strong>: Overview of gender types (M for male, F for female, MF for both)</li><li><strong>match_raca</strong>: Overview of races for all matched elective offices (BR for white, AM for yellow, PA for brown, PR for black, IN for Indian, NA for None of the above)</li><li><strong>match_genero_raca</strong>: Array of gender and race concatenated for each matched candidate</li></ul><p><strong>comments_folders</strong>: This file provides a sample of comments collected from the videos analyzed in the first file. It includes the following fields:</p><ul><li><strong>id</strong>: Comment identifier</li><li><strong>videoId</strong>: Video identifier where the comment is published</li><li><strong>textDisplay</strong>: Comment as displayed on the YouTube platform</li><li><strong>textDisplay_len</strong>: Number of characters in the displayed comment</li><li><strong>textOriginal</strong>: Encoded comment text</li><li><strong>textOriginal_len</strong>: Number of characters in the original comment text</li><li><strong>authorDisplayName</strong>: Name of the comment author</li><li><strong>authorChannelId</strong>: Comment author channel identifier</li><li><strong>publishedAt</strong>: Timestamp of when the comment was published</li><li><strong>likes</strong>: Number of likes on the comment</li></ul><p>This dataset offers valuable insights into YouTube interactions surrounding Brazilian elections, including demographic and sentiment analysis based on candidate information, gender, and race. Researchers can utilize this dataset for a detailed examination of public engagement and sentiment on political content during the specified election periods.</p>
Descriptive representation of immigrant-origin citizens at the 2018 Local elections in Flanders
<p>This dataset contains information about the descriptive representation of immigrant-origin citizens at the 2018 local elections in Flanders. It covers 31,176 candidates over 1,312 lists. For each candidate list, this dataset contains information about the percentage of immigrant-origin candidates, their average relative list position and the proportion of elected immigrant-origin candidates. Extra variables are added at the party level (e.g. whether it was part of the local government prior to the 2018 local elections or whether the head of list had an immigrant background) and municipality level (e.g. population density or percentage of the immigrant-origin population) to allow a profound examination of which factors steer the descriptive representation of immigrant-origin citizens.</p> <p>The updated version also includes information about the gendered selection of the immigrant-origin candidates. During this additional coding process, I discovered some small coding errors included in the original version. These errors are corrected in the updated version. </p>
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>
Dataset [Study on Algorithms in Election Campaigns: Analysis with IRaMuteQ]
<p>This is the raw data behind the publication:</p> <p>Carvalho, P. R; Ramos, M. G.; Schneider, M. A. F. Study on algorithms in election campaigns: analysis with Iramuteq. XX ENANCIB 2019.</p> <p>The present work reports the exploratory empirical research carried out in <strong>the Scopus database</strong> with the objective of identifying, quantifying and analyzing the scientific production of the topic algorithms in politics in electoral campaigns, <strong>from 2008 to 2018,</strong> by means of Scientometric techniques. In addition, the Content Analysis of abstracts of the articles collected through Iramuteq, open source and free software. The methodological proposal refers to the construction of a textual corpus composed of <strong>150 articles </strong>retrieved, following analyzes such as: pre-analysis of the material; data mining; simple statistical analysis; Descending Hierarchical Classification; Factorial Correspondence Analysis; similitude analysis; and frequency analysis with word cloud visualization. The result of the study demonstrated the efficiency of the chosen keywords in the retrieval of information. In addition, we identified the convergence and repetition of themes of the articles represented by the text segments.</p> <p>Link: <a href="https://brapci.inf.br/index.php/res/v/122943">https://brapci.inf.br/index.php/res/v/122943</a></p>
Political Advertising on Facebook During the 2022 Australian Federal Election
<p>This repository contains data on political advertisements posted on Facebook and Instagram during the three months leading up to the 2022 Australian federal election. The data was collected using the Meta Ad Library API and provides insights into digital political campaigning strategies in Australia.</p> <h2>Contents</h2> <p>The repository includes the following files:</p> <ul> <li><code>datasheet.txt</code>: A detailed datasheet following the guidelines of Gebru et al. (2021), providing transparency about data collection, preprocessing, and handling procedures.</li> <li><code>party_information.csv</code>: Manually annotated data matching candidates, electorates, and parties based on ad funding entity information.</li> <li><code>maps_australia.zip</code>: A set of Australian maps used for visualizing the geographical distribution of ads during the campaign period.</li> <li><code>2022_AU_election_raw.zip</code>: Raw data collected from the Meta Ad Library API, containing comprehensive information on political ads run on Facebook and Instagram in Australia during the specified period.</li> <li><code>keywords.csv</code>: A curated list of keywords relevant to the 2022 Australian federal election, useful for content analysis and topic modeling of the advertisements.</li> </ul> <h2>Related Code Repository</h2> <p>The code used to analyze this data and reproduce the results presented in the paper "Political Advertising on Facebook During the 2022 Australian Federal Election" can be found in a separate repository:</p> <p><a href="https://anonymous.4open.science/r/Political-Advertising-2022-AU-Federal-Election-3E75/README.md">https://anonymous.4open.science/r/Political-Advertising-2022-AU-Federal-Election-3E75/README.md</a></p> <h2>References</h2> <p>Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Iii, H. D., & Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86–92.</p>
UK consumer sentiment data for McMenamin et al., Institutions and Elections, Journalism, 2021.
<p>UK consumer sentiment data (STATA) for McMenamin et al., Institutions and Elections, Journalism, 2021.</p> <p>Code and other datasets for this article also available on Zenodo.</p>
German poll data for McMenamin et al., Institutions and Elections, Journalism, 2021.
<p>German poll data (STATA) for McMenamin et al., Journalism, 2021.</p> <p>Code and other datasets for this article also available on Zenodo.</p>
German newspaper data for McMenamin et al., Institutions and Elections, Journalism, 2021.
<p>German newspaper data (in STATA format) from McMenamin et al., Journalism, 2021.</p> <p>Code and other datasets also available on Zenodo.</p>
Annotation of Facebook ads in 2019 EU parliamentary elections
<p>This dataset contains manual annotation for ads from parties from UK, Italy, Germany, Spain, and Poland that ran ads on Facebook during the 2019 EU parliamentary elections and that were defined as "populist" by popu-list.org. For each ad, we qualitatively identified a primary issue, a sub-issue, and a stance with respect to the sub-issue. The annotation was performed through an open coding procedure. More information is reported in our paper "The Thin Ideology of Populist Advertising on Facebook during the 2019 EU Elections".<br> More information about the same ads is reported in: https://zenodo.org/record/6597765<br> <br> This TSV file contains the following columns:<br> - "state": the country of the ad.<br> - "ad_id": the original id of the ad.<br> - "link": a link to the ad.<br> - "page_name": name of the page running the ad.<br> - "is_local": whether the ad is related to local elections or not (if not, it's related to the 2019 EU Elections).<br> - "issue": a comma-separated list of the main issues of the ad.<br> - "sub-issue": a comma-separated list of sub-issues of the ad.<br> - "stance": a comma-separated list of stances of the ad.</p>
Dataset from: Design and analysis of tweet-based election models for the 2021 Mexican legislative election
<p>Processed data used for the analysis of the manuscript with title "Design and analysis of tweet-based election<br> models for the 2021 Mexican legislative election", by Vigna-Gómez et al.</p>
Twitter links to tweets from Flemish parties and chairmen in advance of the 2019 elections
<p>This is a list of al tweets used in the Master's Thesis of Toon Tabruyn to obtain the degree of Master of Sociology.</p>
IINB vs. QLB for Elective Open Inguinal Herniorrhaphy
ClinicalTrials.gov study NCT03007966. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Remote Ischemic Preconditioning and Contrast Induced - Acute Kidney Injury in Patients Undergoing Elective PCI
ClinicalTrials.gov study NCT03761368. IPD Sharing: YES. Countries: 1. Publications: 1.
The Effects of Dexmedetomidine on the Heart Beat During Elective Surgery in Children
ClinicalTrials.gov study NCT02353169. IPD Sharing: NO. Countries: 1. Publications: 2.
Impact of Different Educational Approaches on Post-operative Opiate Utilization After Elective Lower Extremity Surgery
ClinicalTrials.gov study NCT02997644. IPD Sharing: YES. Countries: 1. Publications: 7.
Efficacy of Ginger on Intraoperative and Postoperative Nausea and Vomiting in Elective Cesarean Section Patients
ClinicalTrials.gov study NCT01733212. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Prospective, Randomized, Parallel-arm Clinical Trial of CleanCision or Alexis O in Elective Colorectal Surgery
ClinicalTrials.gov study NCT03816995. IPD Sharing: NO. Countries: 1. Publications: 15.
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