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61 results for “Twitter data”
SSIX BREXIT Twitter Annotated Data Set
<p><strong>SSIX BREXIT Gold Standard</strong></p> <p>This repository contains the BREXIT Twitter Gold Standard produced by the SSIX Project <a href="https://ssix-project.eu/">https://ssix-project.eu/</a>.</p> <p>Only a sample is available here, to rebuild the full dataset, follow the instructions on the SSIX Project code repository: </p> <p><a href="https://bitbucket.org/ssix-project/brexit-gold-standard">https://bitbucket.org/ssix-project/brexit-gold-standard</a></p>
MMoveT15: A Twitter Dataset for Extracting and Analysing Migration-Movement Data of the European Migration Crisis 2015
<p>In the 2015 migration crisis thousands of refugees and migrants crossed the border to Hungary, Austria and Germany. The movements of these people are reflected in social media, especially on Twitter. We present a dataset of 3275 Tweets form the months September and October 2015. These Tweets are annotated regarding their relevance to the quantitative movement of refugees/migrants into Hungary, Austria and Germany. We present this dataset for a posterior analysis of the 2015 migration crisis or as a basis for an early warning or forecasting system</p>
Tagged Twitter timelines for users reporting SARS-CoV-2 infections and related data
<p>Twitter data was collected through the Twitter API v2.0, specifically through the timeline endpoint. Details about the inference of SARS-CoV-2 self-reports and the tagging of the full timeline of each user can be found in the <a href="https://github.com/digitalepidemiologylab/content_changes_paper">GitHub repository</a>. The larger dataset ("preprocessed_data.csv") consists of a total of 8,534,171 tweets posted by 30,856 users from January 1, 2020 to October to September 30, 2021.</p> <p>The raw data from Twitter, including tweet and user IDs, has been removed or anonymized in order to comply with the EPFL guidelines for data sharing.</p> <p>In particular, the date of the tweets was removed, the text of the tweets, URLs and URL domains have been substituted with the "text", "<URL>" and "<URL_DOMAIN>" token respectively.</p> <p>User IDs have been substitued with new IDs in the [0, number of users] range (e.g. U0, U1, ...) .</p> <p>Tweet IDs have been substitued with new IDs in the [0, number of tweets] range (e.g. T0, T1, ...) .</p> <p>In addition to self-explanatory columns about topics, emotions, URL classification and symptoms we tagged, we also share the columns:</p> <ul> <li>pdate: date of the SARS-CoV-2 infection self-report for that user (adjusted with SUTime)</li> <li>effective_date: date of the tweet adjusted with SUTime, when the SUTime columns is available.</li> <li>rel_effective_day(week, month): days (weeks, months) computed with respect to the positivity date (i.e. "pdate" column). Negative numbers refer to tweets posted before the user reported a COVID-19 infection on Twitter.</li> </ul>
Data and code for the paper "Attention, sentiments and emotions towards emerging climate technologies on Twitter"
<p>This is the code and data for the paper "Attention, sentiments and emotions towards emerging climate technologies on Twitter" by Müller-Hansen et al. (Global Environmental Change, 2023).</p><p>This archive contains the following materials:</p><ul><li>Data set of tweets</li><li>Table of subqueries for searching Twitter</li><li>Code for figure generation</li></ul><p>Please see the Readme for further details.</p><p> </p>
Moral Values of Twitter COVID-19 Vaccine Data
<p>This data is part of our accepted paper "Learning to Adapt Domain Shifts of Moral Values via Instance Weighting" at the 33rd ACM Conference on Hypertext and Social Media (HT ’22). We annotate moral values of COVID-19 vaccine-related tweets. </p>
Twitter data reveal six distinct environmental personas
<p>Effective digital environmental communication is integral to galvanizing public support for conservation in the age of social media. Environmental advocates require messaging strategies suited to social media platforms, including ways to identify, target, and mobilize distinct audiences. Here, we provide – to the best of our knowledge – the first systematic characterization of environmental personas on social media. Beginning with 1 million environmental nongovernmental organization (NGO) followers on Twitter, of which 500,000 users met data quality criteria, we identified six personas that differ in their expression of 21 environmental issues. General consistency in the proportional composition of personas was detected across 14 countries with sufficiently large samples. Within the US, although the six personas varied in their mean political ideology, we did not observe that the personas split along political party lines. Our results pave the way for environmental advocates – including NGOs, public agencies, and researchers – to use audience segmentation methods like the one discussed here to target and tailor messages to distinct constituencies at speed and scale. This repository contains several tabular files that can be used to query user data from Twitter or reproduce the main results in the main text of the article.</p>
Dataset for Twitter Sentiment Analysis on Criminal Data Propagation using Naive Bayes Algorithm
<p>This study presents a dataset tailored for conducting sentiment analysis on Twitter regarding the propagation of criminal data. Leveraging the Naive Bayes algorithm, the dataset aims to facilitate research into public perceptions surrounding the dissemination of criminal data on social media platforms. Through a currated collection of tweets, researchers can explore the nuanced sentiments and attitudes expressed by users in response to this phenomenon.</p>
Coronavirus Twitter Data: A collection of COVID-19 tweets with automated annotations
<p>This dataset contains tweets related to COVID-19. The dataset contains Twitter ids, from which you can download the original data directly from Twitter. Additionally, we include the date, keywords related to COVID-19 and the inferred geolocation. Check detailed information at <a href="http://twitterdata.covid19dataresources.org/index">http://twitterdata.covid19dataresources.org/index</a>.</p>
Rome - Parks - Twitter data
<p>Twitter dataset retrieved from Rome's urban parks and coded into Cultural Ecosystem Services (CES) following the standard protocol (version 3) published at this <a href="https://doi.org/10.5281/zenodo.7957219">link</a>.</p>
Data mining and sentiment analysis on Twitter and Facebook
<p>Les données récoltées sont sur le sujet "Data mining and sentiment analysis on Twitter and Facebook". Ce jeu de donnée contient la liste des attributs principaux suivants :</p> <ul> <li>titles, titre du fichier PDF,</li> <li>authors, auteurs du fichier PDF,</li> <li>years, année de création du fichier PDF,</li> <li>ncitedby, nombre de citation,</li> <li>linkfiles, liens du fichier PDF,</li> </ul> <p>mais également des métadonnées. </p> <p>La récupération du jeu de données a été récolté sur Google Scholar. Plusieurs recherches sur Google Scholar ont été faites pour ce dernier (voir liens ci-dessous) :</p> <ul> <li>https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=twitter+data+mining+filetype%3Apdf&btnG=</li> <li>https://scholar.google.com/scholar?start=490&q=facebook+data+mining+-Twitter+filetype:pdf&hl=en&as_sdt=0,5</li> <li>https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=seniment+analyse+twitter+filetype%3Apdf&btnG=</li> </ul>
Preprocessed Indonesian Twitter Dataset on UU Perlindungan Data Pribadi for Sentiment Analysis Research
<p>This dataset, titled 'Preprocessed Indonesian Twitter Dataset on UU Perlindungan Data Pribadi for Sentiment Analysis Research,' is curated and prepared for the purpose of conducting sentiment analysis research as outlined in the project 'ANALISIS SENTIMEN MASYARAKAT TERHADAP UU PERLINDUNGAN DATA PRIBADI PADA APLIKASI X DENGAN METODE SUPPORT VECTOR MACHINE' (Sentiment Analysis of the Community Towards the Personal Data Protection Law on Application X Using Support Vector Machine Method).</p>
Twitter data reveal six distinct environmental personas
Open the record for dataset details and reuse information.
Linking Twitter & Survey Data [online workshop]
<p>Twitter has become an important data source for many social scientists. While using data from Twitter can help to avoid or alleviate some of the issues related to self-report data, such as social desirability or problems with recollection, Twitter data have their own sets of limitations, including the lack of information about individuals or missing outcome variables of interest. Linking data from surveys and Twitter is a way to combine the unique strengths of the two data types and overcome some of their respective limitations. The online workshop led by Luke Sloan (Cardiff University), Johannes Breuer (GESIS), and Libby Bishop (GESIS) covered the key steps in the research process of linking Twitter and survey data. Specifically, the workshop addressed the phases of study planning, (Twitter) data collection, data processing, and archiving and sharing. The workshop drew on recent experiences from different studies that the instructors were involved in and provided guidance on how to address the ethical and operational issues associated with linking Twitter and survey data in the different phases of the research process.</p> <p>The video is also available for viewing on <a href="https://youtu.be/IG1jJ3WP2h8">YouTube</a>.</p>
English Vaccine-related Twitter data (Tweet IDs) 2018-07-01 to 2020-09-30
<p>Twitter data collected through the Crowdbreaks platform [1]. Between July 1st, 2017 and October 1st, 2020 a total of 57.5M tweets (including 39.7M retweets) in English language by 9.9M unique users were collected using the public filter stream endpoint of the Twitter API. The tweets matched one or more of the keywords "vaccine", "vaccination", "vaxxer", "vaxxed", "vaccinated", "vaccinating", "vacine", "overvaccinate", "undervaccinate", "unvaccinated". The data can be considered complete with respect to these keywords.</p> <p> </p> <p>The files contain a single column which corresponds to the Tweet ID (one file per day). Using the IDs the full tweet objects can be restored.</p>
Data to support the publication The use of Twitter for knowledge exchange on sustainable soil management
<p>5 farmer semi-structured interviews</p>
Twitter vaccine misinformation data
<p>Anti-vaccine content is rapidly propagated via social media, fostering vaccine hesitancy, while pro-vaccine content has not replicated the opponent's successes. Despite this disparity in the dissemination of anti- and pro-vaccine posts, linguistic features that facilitate or inhibit the propagation of vaccine-related content remain less known. Moreover, most prior machine-learning algorithms classified social-media posts into binary categories (e.g., misinformation or not) and have rarely tackled a higher-order classification task based on divergent perspectives about vaccines (e.g., anti-vaccine, pro-vaccine, and neutral). Our objectives are (1) to identify sets of linguistic features that facilitate and inhibit the propagation of vaccine-related content and (2) to compare whether anti-vaccine, pro-vaccine, and neutral tweets contain either set more frequently than the others. To achieve these goals, we collected a large set of social media posts (over 120 million tweets) between Nov. 15 and Dec. 15, 2021, coinciding with the Omicron variant surge. A two-stage framework was developed using a fine-tuned BERT classifier, demonstrating over 99 and 80 percent accuracy for binary and ternary classification. Finally, the Linguistic Inquiry Word Count text analysis tool was used to count linguistic features in each classified tweet. Our regression results show that anti-vaccine tweets are propagated (i.e., retweeted), while pro-vaccine tweets garner passive endorsements (i.e., favorited). Our results also yielded the two sets of linguistic features as facilitators and inhibitors of the propagation of vaccine-related tweets. Finally, our regression results show that anti-vaccine tweets tend to use the facilitators, while pro-vaccine counterparts employ the inhibitors. These findings and algorithms from this study will aid public health officials' efforts to counteract vaccine misinformation, thereby facilitating the delivery of preventive measures during pandemics and epidemics.</p>
Replication data and code for: Environmental discourse exhibits consistency and variation across spatial scales on Twitter
<p>Social media platforms, such as Twitter, are an increasingly important source of information and are forums for discourse within and between interest groups. Research highlights how social media communities have amplified movements such as the Arab Spring, #MeToo, and Black Lives Matter. But environmental digital discourse remains underexplored. In the present article, we apply automated text analysis to 200,000 Twitter users in several countries following leading environmental nongovernmental organizations. Some issues such as public action to decarbonize society or species conservation were discussed more intensely than agriculture or marine conservation. Our results illustrate where environmental discourse diverges and converges on Twitter across countries, states, and characteristics, such as political ideology. Using the coterminous United States as a case study, we observed that the prominence of issues varies across states and, in some cases, covaries with political ideology across counties. Our findings show paths forward to characterizing environmental priorities across many issues at unprecedented scale and extent. In this repository, we provide data and code to reproduce the results in the main text of this manuscript. This replication code and dataset accompany this manuscript: https://doi.org/10.1093/biosci/biac051</p>
DMP Examine the correlation between Twitter Sentiment data and the stock data of the 4 big tech companies Apple, Amazon, Google and Microsoft
<p><span>The purpose of using these specific datasets are, the possibilities they give to analyze the stock data changes, based on the sentiment analysis of the previous day. Including this output information it is possible to analyze our goal of searching for possible correlation between those two. </span></p>
My Twitter data export
<p>This is my Twitter public data as exported by Twitter Inc. Is used with research purposes on Data Activism and political discourse on social networks.</p>
Full Data Set of 16814 tweets from 17 different Twitter accounts of Catalonian Public Servants and Delegates collected between August 19 2013 and September 17 2017 in both original language and translated into Spanish later codified under para-diplomacy or nation branding macro-categories. Includes results for each year.
<p>Full Data Set of 16814 tweets from 17 different Twitter accounts of Catalonian Public Servants and Delegates collected between August 19 2013 and September 17 2017 in both original language and translated into Spanish later codified under para-diplomacy or nation branding macro-categories. Includes results for each year out of a significant sample of 1638 tweets.</p>
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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.