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61 results for “Twitter data”
My Twitter data export
<p>Mis datos públicos de Twitter exportador por Twitter Inc, usados aquí para propósitos de investigación de activismo de datos y discurso político en redes sociales.</p>
Twitter and Google Trend data about heat waves in India 2010-2017
<p>The dataset contains:</p> <p>1) The list of tweets corresponding to the keywords "heat wave india" and "heatwave india" between 2010 and 2017.</p> <p>2) The daily count of the same tweets</p> <p>3) The monthly Google Trends data corresponding to the keywords "heat wave", "heatwave", "heat wave india", and "heatwave india" limited to the searches from India in the period 2010-2017</p> <p>The Twitter data has been obtained wth the Python package Get-Old-Tweets (https://github.com/Jefferson-Henrique/GetOldTweets-python); the Google Trends data are obtained from the Google Trends webpage (https://trends.google.com/trends/?geo=US).</p>
Cashtag Piggybacking dataset - Twitter dataset enriched with financial data
<p>This dataset is composed of </p> <ul> <li>Twitter dataset of ~9M tweets mentioning stocks (cashtags) traded on the most important US markets, shared between May and September 2017 (users data enriched with bot classification label)</li> <li>Financial information about ~30k companies found in those tweets, retrieved from Google Finance</li> </ul> <p>Refer to the paper below for more details.</p> <p>Cresci, S., Lillo, F., Regoli, D., Tardelli, S., & Tesconi, M. (2019). Cashtag Piggybacking: Uncovering Spam and Bot Activity in Stock Microblogs on Twitter. <em>ACM Transactions on the Web (TWEB)</em>, <em>13</em>(2), 11.</p>
Data for: To Tweet or Not to Tweet, That is the Question: A Randomized Trial of Twitter Effects in Medical Education
<p>This is the raw data for the manuscript: To Tweet or Not to Tweet, That is the Question: A Randomized Trial of Twitter Effects in Medical Education. The abstract for the study is as follows: </p> <p>Introduction: Many medical education journals use Twitter to garner attention for their articles. The purpose of this study was to test the effects of tweeting on article page views and downloads.</p> <p>Methods: The authors conducted a randomized trial using <em>Academic Medicine</em> articles published in 2015. Beginning in February through May 2018, one article per day was randomly assigned to a Twitter (case) or control group. Daily, an individual tweet was generated for each article in the Twitter group that included the title, #MedEd, and a link to the article. The link delivered users to the article’s landing page, which included immediate access to the HTML full text and a PDF link. The authors extracted HTML page views and PDF downloads from the publisher. To assess differences in page views and downloads between cases and controls, a time-centered approach was used, with outcomes measured at 1, 7, and 30 days.</p> <p>Results: In total, 189 articles (94 cases, 95 controls) were analyzed. After days 1 and 7, there were no statistically significant differences between cases and controls on any metric. On day 30, HTML page views exhibited a 63% increase for cases (M=14.72, SD=63.68) when compared to controls (M=9.01, SD=14.34; incident rate ratio=1.63, p=0.01). There were no differences between cases and controls for PDF downloads on day 30.</p> <p>Discussion: Contrary to the authors’ hypothesis, only one statistically significant difference in page views between the Twitter and control groups was found. These findings provide preliminary evidence that after 30 days a tweet can have a small positive effect on article page views.</p>
A Twitter Streaming Data Set collected before and after the Onset of the War between Russia and Ukraine in 2022
<p>Social media can be mirrors of human interaction, society, and world events. Their reach enables the global dissemination of information in the shortest possible time and thus the individual participation of people all over the world in global events in almost real-time. However, equally efficient, these platforms can be misused in the context of information warfare in order to manipulate human perception and opinion formation. The outbreak of war between Russia and Ukraine on February 24, 2022, demonstrated this in a striking manner. </p> <p>Here we publish a dataset of raw tweets collected by using the Twitter Streaming API in the context of the onset of the war which Russia started on Ukraine on February 24, 2022. A distinctive feature of the dataset is that it covers the period from one week before to one week after Russia's invasion of Ukraine. We publish the IDs of all tweets we streamed during that time, the time we rehydrated them using Twitter's API as well as the result of the rehydration. If you use this dataset, please cite our related Paper: </p> <blockquote> <p>Pohl, Janina Susanne and Seiler, Moritz Vinzent and Assenmacher, Dennis and Grimme, Christian, A Twitter Streaming Dataset collected before and after the Onset of the War between Russia and Ukraine in 2022 (March 25, 2022). Available at SSRN: <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4066543">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4066543</a></p> </blockquote>
Twitter vaccine misinformation data
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Replication data and code for: Environmental discourse exhibits consistency and variation across spatial scales on Twitter
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COVID-19 Twitter data, keyword stream 2020-01-13 to 2020-06-06
<p>Twitter data was collected through the Twitter API, specifically through the filter streaming endpoint, using the Crowdbreaks platform (<a href="http://crowdbreaks.org">crowdbreaks.org</a>) The data used in this work consists of a total of 353,993,900 tweets (thereof 267,026,740 retweets) posted by 26,262,332 users in a 146 day observation period, i.e. from January 13 to June 7, 2020. These tweets have been identified by Twitter to be in English language and match one or more of the keywords "wuhan", "ncov", "coronavirus", "covid" and "sars-cov-2".</p> <p>The data is complete with respect to these keywords, except during a period between mid-March to mid-April when volume exceeded the 1% threshold imposed by Twitter and was subsampled by an (unknown) degree.</p> <p>The following fields are published:</p> <ul> <li>id: Tweet ID</li> <li>is_retweet: Whether or not tweet is a retweet</li> <li>num_retweets: Number of retweets</li> <li>user.id: Id of tweeting user</li> <li>country_code: country code as predicted by local-geocode (https://github.com/mar-muel/local-geocode)</li> </ul>
Data from: Estimating mobile traffic demand using Twitter
In this letter, the authors show that structured social media data can act as an accurate predictor for wireless data demand patterns at a high spatial-temporal resolution. A casestudy is performed on Greater London covering a 5000 km2 area. The data used includes over 0.6 million geo-tagged Twitter data, over 1 million mobile phone data demand records, and U.K. census data. The analysis shows that social media activity (Tweets/s n) can accurately predict the long-term traffic demand for both the uplink and downlink channels. The relationship between social media activity and traffic demand obeys a power law and the model explains for over 71%-79% of the variance in real traffic demand. This is a significant improvement over existing methods of long-term traffic prediction such as census population data (R2 = 0.57). The authors also show that social media data can also forward predict short-term traffic demand for up to 2 h on the same day and for the same time in the following 2-3 days.
Climate Security on social media: raw and processed data from Twitter
<p>This dataset reflects climate security dialogues on Twitter, from January 2014 to May 2023.</p>
Twitter Data of the #BlackLivesMatter Movement And Counter Protests: 2013 to 2021
<p>This data set contains all currently available tweets from the beginning of the Black Lives Matter (BLM) movement in 2013 to 2021. All tweets contain the following keywords BlackLivesMatter, AllLivesMatter, and BlueLivesMatter, or some variation (e.g., #blacklivesmatter or black lives matter). The data set consists of 63.9 million tweets from 13.0 million users from over 100 countries.</p> <p>Instructions on pulling the Twitter data from the Twitter API are <a href="https://github.com/sjgiorgi/blm_twitter_corpus">here</a>.</p> <p>Please use the following citation if you use this data in a paper:</p> <blockquote>Giorgi, S., Guntuku, S. C., Himelein-Wachowiak, M., Kwarteng, A., Hwang, S., Rahman, M., & Curtis, B. (2022). Twitter Corpus of the #BlackLivesMatter Movement And Counter Protests: 2013 to 2021. In <em>Proceedings of the International AAAI Conference on Web and Social Media</em></blockquote>
Dataset for paper: How Twitter Data Sampling Biases U.S. Voter Behavior Characterizations
<p>This repository contains the data and code for the paper "How Twitter Data Sampling Biases U.S. Voter Behavior Characterizations."</p>
DATA BASE-OFFENSIVE DISCOURSE ON TWITTER: VOX IN THE CATALAN PARLIAMENTARY ELECTIONS
<p>DATA BASE-OFFENSIVE DISCOURSE ON TWITTER: VOX IN THE CATALAN PARLIAMENTARY ELECTIONS</p>
Twitter Data
<p>Twitter data</p>
Twitter data export
<p>My latest Twitter public data as exported by Twitter Inc., used for political activism purposes.</p>
Mi data de Twitter
<p>Esta es mi _data_ de Twitter.</p>
My Twitter data
<p>Data selfie. My Twitter public data exported on April 14th, 2018.</p>
My Twitter data export
<p>This is my Twitter data as exported as exported by Twitter Inc.</p>
Earthquake Logistics in Indonesia: Twitter (X) Data
Open the record for dataset details and reuse information.
Mining the first 100 days: Human and data ethics in Twitter research
<p>This dataset consists of tweet identifiers for tweets harvested between November 28, 2016, following the election of Donald Trump through the end of the first 100 days of his administration. Data collection ended May 1, 2017.</p> <p>Tweets were harvested using multiple methods described below. The total dataset consists of 218,273,152 tweets. Because of the different methods used to harvest tweets, there may be some duplication.</p>
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.