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18 results for “Tweet IDs”
CoVaxxy Tweet IDs data set
<p>A collection of Tweet IDs related to Covid-19 Vaccines, gathered from Twitter since Jan 4, 2021. Please see <a href="https://arxiv.org/abs/2101.07694">https://arxiv.org/abs/2101.07694</a> for more information.</p>
#CdnPoli Tweet IDs for 29 December 2016
<p>These are the #CdnPoli TweetIDs for 29 December 2016, collected on 29 December 2016. They were collected using the DocNow project, available at http://docnow.io/. </p>
Twitter Timelines of British MPs (Tweet IDs)
<p>TSV file containing Twitter tweet_ids from the Timelines of 584 members of British Parliament (collected between 4th and 6th of March 2022). The users were identified from the link below:</p> <p>https://www.ukinbound.org/resources/list-of-mp-twitter-accounts/</p> <p> </p> <p>If you use this dataset for an academic work, please reference the following paper:</p> <pre>@article{tacchi2022signed, title={Signed ego network model and its application to Twitter}, author={Tacchi, Jack and Boldrini, Chiara and Passarella, Andrea and Conti, Marco}, journal={arXiv preprint arXiv:2206.15228}, year={2022} }</pre>
Tweet IDs for the #brexit tweet dataset collected for the Helsinki Digital Humanities Hackathon 2019
<p>This dataset contains lists of tweet ids for the tweets used as material by the "Brexit in Transna­tional So­cial Me­dia" group in the <a href="http://heldig.fi/dhh19/">Helsinki Digital Humanities Hackathon 2019</a>.</p> <p>Due to restrictions in Twitter's terms of service, the full tweet dataset cannot be made public. However, Twitter allows the publication of tweet ids, from which the dataset can be reconstituted, <em>with the exception of deleted tweets</em>.</p> <p>The dataset was gathered as follows: Between 2019-01-22T09:19Z and 2019-04-15T14:42Z, a <a href="https://github.com/DocNow/twarc">Twarc</a> version 1.6.1 script was called hourly to retrieve and archive tweets from Twitter matching the #brexit hashtag using the Twitter search API. All 5,547,585 tweet IDs returned by this run are listed in the file <code>original_ids.txt.gz</code>.</p> <p>However, upon further inspection, problems were identified in the archiving. For an unidentified reason, gathering did not occur between 2019-02-13T06:17Z and 2019-02-26T09:24Z. In addition, the Twarc script had been run without the <code>--extended</code> argument, so the tweet data contained only truncated contents for many tweets.</p> <p>Due to this, a decision was taken to rehydrate a new dataset of tweets falling between 2019-02-26T09:24Z and 2019-04-15T14:42Z. Of the original 4,197,059 tweets gathered for this time period (listed in <code>continuous_ids.txt</code>), 3,941,653 could be rehydrated (i.e., they had not been deleted in the interim). The ids of tweets in this dataset are listed in the file <code>continuous_rehydrated_ids.txt</code>.</p> <p>Finally, out of these rehydrated tweets, a subset was derived that filtered out all tweets that were pure retweets. This subset consisted of 1,104,514 tweets, whose ids are listed in the file <code>continuous_rehydrated_no_retweets_ids.txt</code>.</p>
IDs of Tweets containing data on Work Life balance before and after covid
<p>This dataset contains Tweet IDs that contain " "work-life balance" OR "worklife balance" OR "#work-lifebalance" OR "#worklifebalance". 1,768,628 tweets were downloaded based on this query. Specifically 915 402 „before covid“ and 853 226 „after covid“.<br> This dataset contains only Tweet IDs.</p>
Tweet IDs using History related hashtags
<p>This repository contains IDs of tweets that are related to history and that were collected for the purpose of analyzing how history-related content is disseminated in online social networks. Our <a href="https://link.springer.com/article/10.1007/s00799-020-00296-2">IJDL paper</a> shows the analysis results. The preliminary version of the analysis report is available <a href="https://dl.acm.org/doi/10.1145/3197026.3197057">here</a>.</p> <p> </p> <p>We used the <a href="https://developer.twitter.com/en/docs/tweets/search/api-reference/get-search-tweets.html">Twitter official search API</a> provided by Twitter to collect tweets. Note that three kinds of tweets are typically found in Twitter: tweets, retweets and quote tweets. Tweet is an original text issued as a post by a Twitter user. A retweet is a copy of an original tweet for the purpose of propagating the tweet content to more users (i.e., one's followers). Finally, a quote tweet copies the content of another tweet and allows also to add new content. A quote tweet is sometimes called a retweet with a comment. In this work, we simply treat all quote tweets as original tweets since they include additional information/text. There were however only 1,877 (0.2%) tweets recognized as quote tweets in our dataset. </p> <p> </p> <p>To collect tweets that refer to the past or are related to collective memory of past events/entities, we performed hashtag based crawling together with bootstrapping procedure. <br> At the beginning, we gathered several <a href="http://blog.historians.org/2013/08/history-hashtags-exploring-a-visual-network-of-twitterstorians/">historical hashtags selected by experts</a> (e.g. <strong>#HistoryTeacher</strong>, <strong>#history</strong>, <strong>#WmnHist</strong>). <br> In addition, we prepared several hashtags that are commonly used when referring to the past: <strong>#onthisday</strong>, <strong>#thisdayinhistory</strong>, <strong>#throwbackthursday</strong>, <strong>#otd</strong>. We then collected tweets that contain these hashtags by using Twitter official search API.</p> <p> </p> <p>The collected tweets were issued from 8 March 2016 to 2 July 2018. <br> Bootstrapping allowed us to search for other hashtags frequently used with the seed hashtags. The tweets tagged by such hashtags were then included into the seed set after the manual inspection of all the discovered hashtags as of their relation to the history, and filtering ones that are unrelated. <br> In total, we gathered 147 history-related hashtags which allowed us to collect 2,370,252 tweet IDs pointing to 882,977 tweets and 1,487,275 re-tweets.</p> <p> </p> <p>Related papers:</p> <ol> <li>Yasunobu Sumikawa, Adam Jatowt, and Marten During, <strong>"Digital History meets Microblogging: Analyzing Collective Memories in Twitter"</strong>, In Proceedings of the 18th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL'18, IEEE/ACM, pp. 213 -- 222, 2018. [<a href="https://dl.acm.org/doi/10.1145/3197026.3197057">paper</a>]</li> <li>Yasunobu Sumikawa and Adam Jatowt, <strong>"Analyzing History-related Posts in Twitter"</strong>, International Journal on Digital Libraries, Springer, 2020. https://doi.org/10.1007/s00799-020-00296-2 [<a href="https://link.springer.com/article/10.1007/s00799-020-00296-2">paper</a>]</li> <li>Yasunobu Sumikawa and Adam Jatowt, <strong>"Annotated Dataset of History-related Tweets"</strong>, Data in Brief, Vol. 38, pp. 107344, Elsevier, 2021. [<a href="https://www.sciencedirect.com/science/article/pii/S2352340921006284">paper</a>][<a href="https://zenodo.org/record/4657223">annotated dataset</a>]</li> </ol>
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>
Tweet IDs for ELXN42 Dataset
<p>These are tweet IDs for a dataset created by twarc. It is also a test of Zenodo.</p>
Dataset of user and tweet ids of followers of @nytimes.
<p><br> ## Data Format</p> <p>There are three files containing the ids for different types of Twitter entities.<br> All files are [xz compressed archives](https://en.wikipedia.org/wiki/XZ_Utils) and<br> can be unpacked with, e.g., `unxz user_ids.dat.xz`. Each file contains one id per<br> line, which are sorted ascendingly to improve compressibility.</p> <p>* `user_ids.dat.xz`<br> * `tweet_ids.dat.xz`<br> * `retweet_ids.dat.xz`</p> <p>The ids of `user_ids.dat.xz` correspond to user_ids of Twitter.<br> The ids of `tweet_ids.dat.xz` and `retweet_ids.dat.xz` correspond to tweet_ids of Twitter,<br> but for convenience we split categorized them into retweets and regular tweets.</p> <p>Further there are two files mapping tweet ids to nyt ids, if they contain a link to an<br> article of the NYT.</p> <p>* `tweet_nyt_ids.dat.xz`<br> * `tweet_nyturl_ids.dat.xz`</p> <p>The file `tweet_nyt_ids.dat.xz` contains id from tweets tweeted by the @nytimes account and in the second column<br> the id of the linked article (or `None` if the tweet did not link to an article).<br> The file `tweet_nyturl_ids.dat.xz` contains tweet ids of tweets from @nytimes followers which<br> contain a link to an article of the NYT and in the second column the id of the article.</p>
IDs of Tweets from 1/1/2019-31/7/2022 that contain the hashtag #burnout.
<p>IDs of Tweets from 1/1/2019-31/7/2022 that contain the hashtag #burnout.</p>
Tweets in Space: Geo-tagged Tweet IDs 08/03/18 - 01/12/20
<p>Geo-tagged tweet IDs from August 3, 2018 to January 12, 2020. Intended for academic use only. Commercial use violates Twitter's Developer Terms and Policy.</p> <p>Utilities and documentation: https://github.com/MunifMujib/tweets-in-space</p>
Ids of Tweets including ZeroWaste from Twitter for A Case of Marketing Segmentation tool in Social Networks Research
Open the record for dataset details and reuse information.
Tweets IDs related to Twitter communication in the CSR area (2017-2022)
<p>Tweets IDs related to Twitter communication in the CSR area (2017-2022)</p>
Twitter Timelines of Generic Users from Various Regions (Tweet IDs)
<p>TSV file containing Twitter tweet_ids from the Timelines of 6,905 generic Twitter users collected from 4 distinct regions. These users come from the Mediterranean (2,529), Northern Europe (1,504), South America (1,203) and West Africa (1,669).</p> <p>The datasets were collected between 16th of June and 26th of July 2022.</p> <p>If you use this dataset for an academic work, please reference the following paper:</p> <pre>@inproceedings{tacchi2023cultural, title={Cultural Differences in Signed Ego Networks on Twitter: An Investigatory Analysis}, author={Tacchi, Jack and Boldrini, Chiara and Passarella, Andrea and Conti, Marco}, booktitle={Companion Proceedings of the ACM Web Conference 2023}, pages={1039--1049}, year={2023} }</pre>
Baseline Twitter Timelines Dataset (Tweet IDs)
<p>TSV file containing Twitter tweet_ids from the Timelines of 12,200 Twitter users collected using a snowball sampling methodology (the timelines are therefore interconnected). The initial users were pseudorandomly taken from another dataset, itself a snowball sampling with Barack Obama as the initial seed.</p> <p>The dataset was collected between 27th of April and 25th of May 2022.</p> <p><br> If you use this dataset for an academic work, please reference the following paper:</p> <pre>@inproceedings{tacchi2023cultural, title={Cultural Differences in Signed Ego Networks on Twitter: An Investigatory Analysis}, author={Tacchi, Jack and Boldrini, Chiara and Passarella, Andrea and Conti, Marco}, booktitle={Companion Proceedings of the ACM Web Conference 2023}, pages={1039--1049}, year={2023} }</pre>
Twitter Timelines of Reality TV Watchers from Italy, Netherlands and Brazil (Tweet IDs)
<p>TSV file containing Twitter tweet_ids from the Timelines of 3,173 Twitter users who engage with reality TV. These users come from Italy (1,039), Netherlands (1,139) and Brazil (995). The datasets were collected between 21st and 29th of January 2023.</p> <p>If you use this dataset for an academic work, please reference the following paper:</p> <pre>@inproceedings{tacchi2023cultural, title={Cultural Differences in Signed Ego Networks on Twitter: An Investigatory Analysis}, author={Tacchi, Jack and Boldrini, Chiara and Passarella, Andrea and Conti, Marco}, booktitle={Companion Proceedings of the ACM Web Conference 2023}, pages={1039--1049}, year={2023} }</pre>
Dataset of user and tweet ids of followers of news media accounts
<p>## Data</p> <p>All files are [xz compressed archives](https://en.wikipedia.org/wiki/XZ_Utils) and<br> can be unpacked with, e.g., `unxz user_ids.dat.xz`. Each file contains one id per<br> line, which are in some cases sorted ascendingly to improve compressibility.</p> <p>The ids of `tweet_ids.dat.xz` and `retweet_ids.dat.xz` correspond to tweet_ids of Twitter,<br> but for convenience we split categorized them into retweets and regular tweets.</p> <p>For each news outlet Twitter account we provide a file with all follower ids which<br> followed them at one point in the end of March 2021. These are suffled and the tweets<br> provided in the files above originate from the first few percent.</p> <p>* `AP.ids.xz` followers of @AP, belonging to the Associated Press news agency<br> * `CNN.ids.xz` followers of @CNN, belonging to the CNN TV news network<br> * `FoxNews.ids.xz` followers of @FoxNews, belonging to the Fox News TV news network<br> * `nytimes.ids.xz` followers of @nytimes, belonging to the New York Times newspaper<br> * `Reuters.ids.xz` followers of @Reuters, belonging to the Reuters news agency<br> * `TIME.ids.xz` followers of @TIME, belonging to the bi-weekly TIME magazine<br> * `washingtonpost.ids.xz` followers of @washingtonpost, belonging to the Washington Post newspaper<br> * `WSJ.ids.xz` followers of @WSJ, belonging to the Wall Street Journal newspaper</p>
Dataset - Open IDs of Tweets containing data on Healthy food
Open the record for dataset details and reuse information.
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