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104 results for “Twitter dataset”

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zenodo48/100

Twitter Dataset for "Will You Take the Knee? Italian Twitter Echo Chambers' Genesis During EURO 2020"

Echo chambers can be described as situations in which individuals encounter and interact only with viewpoints that confirm their own, thus moving, as a group, to more polarized and extreme positions. Recent literature mainly focuses on characterizing such entities via static observations, thus disregarding their temporal dimension. In this work, distancing from such a trend, we study, at multiple topological levels, echo chambers genesis related to the social discussions that took place in Italy during the EURO 2020 Championship. Our analysis focuses on a well-defined topic (i.e., BLM/racism) discussed on Twitter during a perfect temporally bound (sporting) event. Such characteristics allow us to track the rise and evolution of echo chambers in time, thus relating their existence to specific episodes.

opencc-by-4.0Jul 2022View details →

zenodo48/100

Twitter dataset of flood-related images for September 2021, Thailand and June/July 2021, Nepal floods

Twitter dataset related to flood events onsets in Thailand and Nepal, focused on September 26/27, 2022, June 16/17 2021 and July 01/02 2021. The dataset has been processed with a VisualCit pipeline in order to automatically filter a relevant subset of posts through automated image analysis, using deep learning techniques. The posts were then geolocated using the CIME algorithm. Additional information about the data collection and data processing are described in <a href="http://arxiv.org/abs/2202.12014">http://arxiv.org/abs/2202.12014</a>

opencc-by-4.0Mar 2022View details →

zenodo48/100

Twitter Dataset for ≈15,000 accounts over a 1 day period

This is almost 15,000 accounts on Twitter. Accounts were collected from a 1% sampled stream over a 24-hour period, from 2022-07-02T04:18:23.000Z to 2022-07-03T04:18:23.000Z. This data includes data from labeled data from https://doi.org/10.5281/zenodo.2653137, with an additional random sample of accounts from the stream to get to 15,000 accounts. Private profiles were removed. This data was collected with the intention of using it for unsupervised machine learning. You are free to do what you want with proper citation.

opencc-by-4.0Jul 2022View details →

zenodo48/100

Dia-Pol: A large scale BlackLivesMatter and MeToo Twitter dataset

This dataset (tweets_id_list.json) contains 258609 number of tweets sent in English extracted from Twitter API using the query word “#blacklivesmatter.” The dataset spans the period from 2020-01-01 to 2021-12-31 and was retrieved on 2022-06-10.

opencc-by-4.0Aug 2022View details →

zenodo48/100

SenTopX: A Benchmark Twitter Dataset for User Sentiment on Various Topics

This is a longitudinal Twitter dataset of 143K users during the period 2017-2021. The following is the detail of all the files: <ul> <li><a href="11243662" target="_blank" rel="noopener noreferrer">SenTopX_userIDs.txt</a>: contains user IDs of 143K Twitter users.</li> <li><a href="../api/records/11243662/draft/files/userIDs_tweetIDs.zip/content" target="_blank" rel="noopener noreferrer">userIDs_tweetIDs.zip</a>: contains Tweet IDs of users, the name of the file is the user ID and the file contains the list of all the tweet IDs.</li> <li><a href="../api/records/11243662/draft/files/users_16_perspective_toxicity_scores.csv/content" target="_blank" rel="noopener noreferrer">users_16_perspective_toxicity_scores.csv</a> contains user IDs and 16 median Perspective API scores, the vector is shared as mean, median, and Gini Index of scores calculated over all tweets of a user.</li> <li><a href="../api/records/11243662/draft/files/LDAvis_top30_words_for_extracted_topics.csv/content" target="_blank" rel="noopener noreferrer">LDAvis_top30_words_for_extracted_topics.csv</a> contains the top 30 most relevant words extracted from each topic extracted by tweet-level topic modeling using the BERTweet topic model.</li> <li><a href="../api/records/11243662/draft/files/topic_modelling_statistics_per_user.csv/content" target="_blank" rel="noopener noreferrer">topic_modelling_statistics_per_user.csv</a> contains important and relevant statistics related to topic modeling results: <ul> <li> 1. user: This column represents the identifier for the user. Each row in the CSV corresponds to a specific user, and this column helps to track and differentiate between the users. 2. avg_topic_probability: This column contains the average probability of the topics for each user calculated across all of the tweets in order to compare users in a meaningful way. It represents the average likelihood that a particular user discusses various topics over the observed period. 3. maximum_topic_avg: This column holds the value of the highest average probability among all topics for each user. It indicates the topic that the user most frequently discusses, on average. 4. index_max_avg_topic_probability_200: This column specifies the index or identifier of the topic with the highest average probability out of 200 possible topics. It shows which topic (out of 200) the user discusses the most. 5. global_avg: This column includes the global average probability of topics across all users. It provides a baseline or overall average topic probability that can be used for comparative purposes. 6. max_global_avg: This column contains the maximum global average probability across all topics for all users. It identifies the most discussed topic across the entire user base. 7. index_max_global_avg: This column shows the index or identifier of the topic with the highest global average probability. It indicates which topic (out of 200) is the most popular across all users. 8. entropy_200_topic: This column represents the entropy of the topics for each user, calculated over 200 topics. Entropy measures the diversity or unpredictability in the user's discussion of topics, with higher entropy indicating more varied topic discussion. In summary, these columns are used to analyze the topic engagement and preferences of users on a platform, highlighting the most frequently discussed topics, the variability in topic discussions, and how individual user behavior compares to overall trends. </li> </ul> </li> </ul>

opencc-by-4.0May 2024View details →

zenodo48/100

[Dataset] Analysis of ego-networks of two CS-related Twitter accounts

Explanation/Overview: This is the dataset for the analyses and results on Twitter Ego-Networks of two CS-related accounts (<a href="https://twitter.com/EuCitSci">@EuCitSci</a> & <a href="https://twitter.com/SciStarter">@SciStarter</a>). The username have been anonymised. Purpose: The purpose of this dataset is to provide the basis to reproduce the results reported in the associated deliverable. As such, it does not represent raw data, but rather files that already include certain analysis steps (like calculated degrees or other SNA-related measures), ready for analysis, visualisation and interpretation with R or any other network visualisation software (e.g., Gephi). The edges represent the follow relation and were retrieved using the Twitter API. All usernames except those of the two ego-accounts were anonymised by assigning a random number to each node. Due to the size of the network, we do not include any <code>.gexf </code>or <code>.gml </code>files in this upload, but rather resort to node and edge lists in the <code>.json</code> format Relatedness: The networks are the ego-networks for two related public accounts that are associated with citizen science (<a href="https://twitter.com/EuCitSci">@EuCitSci</a> & <a href="https://twitter.com/SciStarter">@SciStarter</a>). Content: In this Zenodo entry, two files can be found. <ul> <li><code>edges.json</code></li> </ul> Represents the edge list, with the columns: <table> <tbody> <tr> <td><code>source</code></td> <td><code>target</code></td> <td><code>wherefrom</code></td> <td><code>account</code></td> </tr> <tr> <td>EuCitSci</td> <td>23929</td> <td>friendslist</td> <td>EuCitSci</td> </tr> </tbody> </table> <code>Source</code> and <code>target</code> are the necessary columns for the network creation and in this example indicate that EuCitSci follows 23929. <code>wherefrom </code>indicates the origin of this relation in the crawling (i.e., whether it was retrieved using the friends or follower list) and <code>account </code>indicates the ego-account it belongs to. Thus, the edges can also be separated using this <code>account </code>attribute as they represent two distinct networks. <ul> <li><code>nodes.json</code></li> </ul> Represents the nodes in the networks. The following data fields are contained: <table> <tbody> <tr> <td><code>username</code></td> <td><code>CS</code></td> <td><code>followers_count</code></td> <td><code>friends_count</code></td> <td>...</td> </tr> <tr> <td>1116</td> <td>CS</td> <td>689</td> <td>514</td> <td>...</td> </tr> </tbody> </table> <table> <tbody> <tr> <td>...</td> <td><code>favourites_count</code></td> <td><code>listed_count</code></td> <td><code>statuses_count</code></td> <td>...</td> </tr> <tr> <td>...</td> <td>2601</td> <td>18</td> <td>2141</td> <td>...</td> </tr> </tbody> </table> <table> <tbody> <tr> <td>...</td> <td><code>degree</code></td> <td><code>in_degree</code></td> <td><code>out_degree</code></td> <td>...</td> </tr> <tr> <td>...</td> <td>21</td> <td>5</td> <td>16</td> <td>...</td> </tr> </tbody> </table> <table> <tbody> <tr> <td>...</td> <td><code>reciprocity</code></td> <td><code>account</code></td> </tr> <tr> <td>...</td> <td>0.48</td> <td>EuCitSci</td> </tr> </tbody> </table>   <code>Username </code>represents the numerical and anonymised username, <code>CS</code> the community-membership. The different counts (e.g., <code>followers_count</code>) indicate the number of followers the user had at the time of the retrieval by the Twitter API. <code>degree</code> refers to the degree in the network (similarly the <code>in-</code> and <code>out_degree</code>), while <code>reciprocity</code> refers to the number of mutual edges in respect to the total number of edges per node (see <a href="https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.reciprocity.reciprocity.html">here</a>). <code>Account</code> is similar as described above.   Grouping: The data is grouped according the ego-account it is associated to, as can be read above (i.e., the <code>account</code> attribute).

opencc-by-4.0Nov 2022View details →

zenodo48/100

VaxxHesitancy: A Dataset for Studying Hesitancy Towards COVID-19 Vaccination on Twitter

We create a publicly available dataset of over 3,100 COVID-19 vaccine-related tweets labeled as one of four stance categories: pro-vaxx, anti-vaxx, vaxx-hesitant, or irrelevant. *** Please use the V2 version. *** We split our dataset into two separate files: (1) VaccineHesitancy_train_v2.csv (Single + Double annotated) (2) VaccineHesitancy_test.csv (Double annotated) We present the details of this dataset here: VaxxHesitancy: A Dataset for Studying Hesitancy Towards COVID-19 Vaccination on Twitter (ICWSM 2023) Our Pre-trained model (GateNLP/covid-vaccine-twitter-bert) : https://huggingface.co/GateNLP/covid-vaccine-twitter-bert Paper: https://ojs.aaai.org/index.php/ICWSM/article/view/22213/21992   <pre>@inproceedings{mu2023vaxxhesitancy, title={VaxxHesitancy: A Dataset for Studying Hesitancy Towards COVID-19 Vaccination on Twitter}, author={Mu, Yida and Jin, Mali and Grimshaw, Charlie and Scarton, Carolina and Bontcheva, Kalina and Song, Xingyi}, booktitle={Proceedings of the International AAAI Conference on Web and Social Media}, volume={17}, pages={1052--1062}, year={2023} } </pre>

opencc-by-4.0Jan 2023View details →

zenodo44/100

Public Dataset for "Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior"

Dataset for the "Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior" paper, published in ICWSM 2018. The full text of the paper can be found <a href="https://arxiv.org/pdf/1802.00393.pdf">here</a>.   The dataset provided here includes an updated version of the original dataset, with ~100k tweets annotated using the CrowdFlower platform:  <ul> <li> hatespeech_id_label_PUBLIC_100K.csv: contains ~100K rows, where every row consists of a unique Tweet ID.  </li> <li> hatespeech_text_label_vote_RESTRICTED_100K.csv: contains ~100K rows, where every row consists of the tweet text, its label according to majority annotation and the number of majority annotators. Available only <a href="https://zenodo.org/record/3706866#.Xmkh6i97FQI">here</a>. </li> <li> retweets.csv: contains ~2K rows, where every row consists of the row number in the  hatespeech_text_label_vote_RESTRICTED_100K.csv file which is the first occurrence of a Tweet text followed by comma-separated row numbers of all other occurrences of the same Tweet text in the same file. There are ~8K other occurrences due to retweets. Available only <a href="https://zenodo.org/record/3706866#.Xmkh6i97FQI">here</a>. </li> </ul>   UPDATE: It has come to our understanding that a number of the tweets are not available anymore for download on Twitter. Therefore, we provide <a href="https://zenodo.org/record/3706866#.YYLG6S8RqjQ">here </a>the hatespeech_text_label_vote_RESTRICTED_100K file with the full ~100K tweet texts, their associated majority label, and the number of votes for the majority label. The tweets are shuffled so that there is no connection between tweet IDs and texts (in order to be in line with the T&C of Twitter).   Please cite the paper in any published work that uses any of these resources.  @inproceedings{founta2018large,      title={Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior},      author={Founta, Antigoni-Maria and Djouvas, Constantinos and Chatzakou, Despoina and Leontiadis, Ilias and Blackburn, Jeremy and Stringhini, Gianluca and Vakali, Athena and Sirivianos, Michael and Kourtellis, Nicolas},      booktitle={11th International Conference on Web and Social Media, ICWSM 2018},      year={2018},      organization={AAAI Press}  }  For any further questions contact a.m.founta at gmail dot com AND markos.charalambous at eecei dot cut dot ac dot cy

opencc-by-4.0Feb 2020View details →

zenodo44/100

Digital Narratives of Covid-19: a Twitter Dataset

We are releasing a Twitter dataset connected to our project <a href="https://covid.dh.miami.edu/">Digital Narratives of Covid-19 </a>(DHCOVID) that -among other goals- aims to explore during one year (May 2020-2021) the narratives behind data about the coronavirus pandemic. In this first version, we deliver a Twitter dataset organized as follows: <ul> <li>Each folder corresponds to daily data (one folder for each day): YEAR-MONTH-DAY</li> <li>In every folder there are 9 different plain text files named with "dhcovid", followed by date (YEAR-MONTH-DAY), language ("en" for English, and "es" for Spanish), and region abbreviation ("fl", "ar", "mx", "co", "pe", "ec", "es"): <ol> <li>dhcovid_YEAR-MONTH-DAY_es_fl.txt: Dataset containing tweets geolocalized in South Florida. The geo-localization is tracked by tweet coordinates, by place, or by user information.</li> <li>dhcovid_YEAR-MONTH-DAY_en_fl.txt: We are gathering only tweets in English that refer to the area of Miami and South Florida. The reason behind this choice is that there are multiple projects harvesting English data, and, our project is particularly interested in this area because of our home institution (University of Miami) and because we aim to study public conversations from a bilingual (EN/ES) point of view.</li> <li>dhcovid_YEAR-MONTH-DAY_es_ar.txt: Dataset containing tweets from Argentina.</li> <li>dhcovid_YEAR-MONTH-DAY_es_mx.txt: Dataset containing tweets from Mexico.</li> <li>dhcovid_YEAR-MONTH-DAY_es_co.txt: Dataset containing tweets from Colombia.</li> <li>dhcovid_YEAR-MONTH-DAY_es_pe.txt: Dataset containing tweets from Perú.</li> <li>dhcovid_YEAR-MONTH-DAY_es_ec.txt: Dataset containing tweets from Ecuador.</li> <li>dhcovid_YEAR-MONTH-DAY_es_es.txt: Dataset containing tweets from Spain.</li> <li>dhcovid_YEAR-MONTH-DAY_es.txt: This dataset contains all tweets in Spanish, regardless of its geolocation.</li> </ol> </li> </ul> For English, we collect all tweets with the following keywords and hashtags: covid, coronavirus, pandemic, quarantine, stayathome, outbreak, lockdown, socialdistancing. For Spanish, we search for: covid, coronavirus, pandemia, quarentena, confinamiento, quedateencasa, desescalada, distanciamiento social. The corpus of tweets consists of a list of Tweet Ids; to obtain the original tweets, you can use "<a href="https://github.com/DocNow/hydrator">Twitter hydratator</a>" which takes the id and download for you all metadata in a csv file. We started collecting this Twitter dataset on April 24th, 2020 and we are adding daily data to our GitHub repository. There is a detected problem with file 2020-04-24/dhcovid_2020-04-24_es.txt, which we couldn't gather the data due to technical reasons. For more information about our project visit <a href="https://covid.dh.miami.edu/">https://covid.dh.miami.edu/</a> For more updated datasets and detailed criteria, check our GitHub Repository: <a href="https://github.com/dh-miami/narratives_covid19/">https://github.com/dh-miami/narratives_covid19/</a>

opencc-by-4.0May 2020View details →

zenodo44/100

#retweetthe8th: twitter dataset from the 2018 Referendum to repeal the 8th Amendment of the Constitution of Ireland

This dataset contains the tweet ids of 2,108,782 tweets related to the referendum to repeal the 8th Amendment of the Constitution of Ireland and replace it with the Thirty-sixth Amendment of the Constitution of Ireland on May 25, 2018.  They were collected between March 9th, 2018 and May 30th, 2018 from the Twitter filter stream API using Twarc (<a href="https://github.com/DocNow/twarc">https://github.com/DocNow/twarc</a>). The set of terms that were used for the search were: #repealthe8th, #together4yes, #8thref, #savethe8th, #hometovote, #togetherforyes, #repealedthe8th, #loveboth, #voteyes, #voteno, #lovebothvoteno, #repealtheeighth. Note that the terms changed during the course of data collection and that the searches were not exhaustive. The GET statuses/lookup method supports retrieving the complete tweet for a tweet id (known as hydrating). Twarc can also be used to hydrate tweets. Per Twitter’s Developer Policy, tweet ids may be publicly shared for academic purposes; tweets may not. There are several practical reasons to leave the retweets, as it allows researchers to trace important tweets and their dissemination.  However, for researchers that might be interested in NLP tasks for which retweets are not required, a set of 411,213 tweet ids without their retweets is also included in the zip file. Questions about this dataset can be sent to Emmet Ó Briain: <a href="mailto:emmet@quiddity.ie">emmet@quiddity.ie</a> . (24-05-20)

opencc-by-4.0May 2020View details →

zenodo44/100

CMU-MisCov19: A Novel Twitter Dataset for Characterizing COVID-19 Misinformation

From conspiracy theories to fake cures and fake treatments, COVID-19 has become a hot-bed for the spread of misinformation online. It is more important than ever to identify methods to debunk and correct false information online. Detection and characterization of misinformation requires an availability of annotated datasets. Most of the published COVID-19 Twitter datasets are generic, lack annotations or labels, employ automated annotations using transfer learning or semi-supervised methods, or are not specifically designed for misinformation. Annotated datasets are either only focused on "fake news", are small in size, or have less diversity in terms of classes. Here, we present a novel Twitter misinformation dataset called "CMU-MisCov19" with 4573 annotated tweets over 17 themes around the COVID-19 discourse. We also present our annotation codebook for the different COVID-19 themes on Twitter, along with their descriptions and examples, for the community to use for collecting further annotations. Further details related to the dataset, and our analysis based on this dataset can be found at <a href="https://arxiv.org/abs/2008.00791">https://arxiv.org/abs/2008.00791</a>. In adherence to the Twitter’s terms and conditions, we do not provide the full tweet JSONs but provide a ".csv" file with the tweet IDs so that the tweets can be rehydrated. We also provide the annotations, and the date of creation for each tweet for the reproduction of the results of our analyses. Note: If for any reason, you are not able to rehydrate all the tweets, reach out to Shahan Ali Memon at (shahan@nyu.edu). If you use this data, please cite our paper as follows:  "Shahan Ali Memon and Kathleen M. Carley. Characterizing COVID-19 Misinformation Communities Using a Novel Twitter Dataset, In Proceedings of The 5th International Workshop on Mining Actionable Insights from Social Networks (MAISoN 2020), co-located with CIKM, virtual event due to COVID-19, 2020."

opencc-by-4.0Sep 2020View details →

zenodo44/100

MAVIS Twitter dataset: A collection of tweets and sentiment analysis in Spanish about vaccines and diseases during the period 2015-2018

MAVIS dataset comprises a full knowledge base regarding Twitter messages published in Spanish during the period 2015-2018, in the context of sentiment analysis of specific vaccines and their related diseases. Such diseases and vaccines are summarized as follows: <ul> <li>Invasive meningococcal disease (“EMI” in Spanish): Bexsero, Trumenba, Nimenrix</li> <li>Invasive pneumococcal disease (“ENI” in Spanish)</li> <li>Influenza</li> <li>Hepatitis</li> <li>Rotavirus: Rotarix, Rotateq</li> <li>Measles (“Sarampión” in Spanish) and MMR (“Triple vírica” in Spanish)</li> <li>Sepsis</li> <li>Whooping cough (“Tosferina” in Spanish)</li> <li>Chickenpox (“Varicela” in Spanish): Varivax, Varilrix; and Shingles (“Zoster” in Spanish)</li> <li>Human papillomavirus infection (“VPH” in Spanish): Cervarix, Gardasil</li> </ul> Tweets have been manually classified as having a negative or non-negative sentiment by 5 experts. Moreover, an automatic classification has been performed by 3 different tools: IBM Watson (now Watson Tone Analyzer, <a href="https://www.ibm.com/watson/services/tone-analyzer/">https://www.ibm.com/watson/services/tone-analyzer/</a>), Google Cloud Natural Language (<a href="https://cloud.google.com/natural-language">https://cloud.google.com/natural-language</a>), and Meaning Cloud (<a href="https://www.meaningcloud.com/">https://www.meaningcloud.com/</a>). IBM Watson and Google Cloud Natural Language returned a numerical sentiment score ranging from -1 to 1, while Meaning Cloud returned a categorical variable with the values ‘P+’, ‘P’, ‘NEU’, ‘N’ and ‘N+’, which were converted to 1, 2, 3, 4 and 5 respectively. With these variables (IBM Watson, Google Cloud Natural Language, and Meaning Cloud annotations and the experts’ classification as the target label), a machine learning metamodel was developed. Tweets were also annotated with the sentiment output given by this classifier.    The provided data includes intrinsic tweets information, intrinsic information regarding the users that posted the tweets, the keywords mentioned in each tweet, and the annotations that the experts, the tools, and the model gave to each tweet. Funding: This dataset was obtained with funding from MSD, Spain under MAVIS Study (VEAP ID: 7789). Current studies using this dataset at the moment of the publication: <ul> <li>Rodríguez-González et al., “Creating a metamodel based on machine learning to identify the sentiment of vaccine and disease-related messages in Twitter: the MAVIS study” in 2020 IEEE 33st International Symposium on Computer-Based Medical Systems (CBMS), Jul. 2020, p. 6. DOI: 10.1109/CBMS49503.2020.00053</li> <li>Rodríguez-González et al., "Identifying Polarity in Tweets from an Imbalanced Dataset about Diseases and Vaccines Using a Meta-Model Based on Machine Learning Techniques" in Applied Sciences, 2020, 10. DOI: 10.3390/app10249019</li> </ul>

opencc-by-4.0Dec 2020View details →

zenodo44/100

#IndonesiaHumanRightsSOS Twitter Hashtag Tweets Dataset

Dataset ini merupakan hasil dari scraping pada media sosial twitter dengan menggunakan aplikasi twint yang ditujukan pada hashtag #IndonesiaHumanRightsSOS. Scraping data dilakukan untuk cuitan yang dibuat dari tanggal 18 Desember 2020 10:59 AM s/d 19 Desember 2020 23:18 PM. Pada dataset mengandung 106.903 Row data dengan informasi terkait: User ID, Username, Twitter Name,Tweets, dsb. Selain itu dilampirkan juga contoh data yang telah dianalisis berupa wordcloud,username cloud, 100 most used word & most active username. - This dataset is the result of scraping on social media twitter using the twint application aimed at the hashtag #IndonesiaHumanRightsSOS. Data scraping is done for tweets made from December 18 2020 10:59 AM to December 19 2020 23:18 PM. The dataset contains 106,903 rows of data with related information: User ID, Username, Twitter Name, Tweets, etc. Also there is an example of the data that has been analyzed in the form of wordcloud, username cloud, 100 most used words & most active username.

opencc-by-4.0Dec 2020View details →

zenodo44/100

Antisemitism on Twitter: A Dataset for Machine Learning and Text Analytics

<h1>Dataset from the Institute for the Study of Contemporary Antisemitism (ISCA) at Indiana University:  </h1>   <div> <div> The Social Media & Hate research lab at the Institute for the Study of Contemporary Antisemitism compiled this dataset using an annotation portal (Jikeli, Soemer, and Karali 2024), which was used to label tweets as either antisemitic or non-antisemitic, among other labels. Note that annotation was done on live data, including images and context, such as threads. All data was annotated by two experts, and all discrepancies were discussed (Jikeli et al. 2023).  </div> </div> Content: This dataset contains 11311 tweets covering a wide range of topics common in conversations about Jews, Israel, and antisemitism between January 2019 and April 2023. The dataset consists of random samples of relevant keywords during this time period. 1,953 tweets (17%) are antisemitic according to the IHRA definition of antisemitism.    <div> The distribution of tweets by year is as follows: 1499 (13%) from 2019, 3712 (33%) from 2020, 2591 (23%) from 2021, 2644 from 2022 (23%) and 865 (8%) from 2023. 6365 (56%) contain the keyword "Jews," 4134 (37%) include "Israel," 529 (5%) feature the derogatory term "ZioNazi*," and 283 (3%) use the slur "K---s." Some tweets may contain multiple keywords.   </div> <div> 725 out of the 6365 tweets with the keyword "Jews" (11%) and 664 out of the 4134 tweets with the keyword "Israel" (16%) were classified as antisemitic. 97 out of the 283 tweets using the antisemitic slur "K---s" (34%) are antisemitic. Interestingly, many tweets featuring the slur "K---s" actually call out its use. In contrast, the majority of tweets using the derogatory term "ZioNazi*" are antisemitic, with 467 out of 529 (88%) being classified as such.   </div>   File Description:  <div> <div> The dataset is provided in a csv file format, with each row representing a single message, including replies, quotes, and retweets. The file contains the following columns:   </div> <div>    ‘ID’: Represents the tweet ID.   </div> <div> ‘Username’: Represents the username that posted the tweet.    </div> <div> ‘Text’: Represents the full text of the tweet (not pre-processed).  </div> <div> ‘CreateDate’: Represents the date on which the tweet was created.    </div> <div> ‘Biased’: Represents the label given by our annotations as to whether the tweet is antisemitic or not.  </div> <div> ‘Keyword’: Represents the keyword that was used in the query. The keyword can be in the text, including hashtags, mentioned users, or the username itself.    </div> </div>   Licences  Data is published under the terms of the "Creative Commons Attribution 4.0 International" licence (https://creativecommons.org/licenses/by/4.0)    Acknowledgements  We are grateful for the support of Indiana University’s Observatory on Social Media (OSoMe) (Davis et al. 2016) and the contributions and annotations of all team members in our Social Media & Hate Research Lab at Indiana University’s Institute for the Study of Contemporary Antisemitism, especially Grace Bland, Elisha S. Breton, Kathryn Cooper, Robin Forstenhäusler, Sophie von Máriássy, Mabel Poindexter, Jenna Solomon, Clara Schilling, and Victor Tschiskale.  This work used Jetstream2 at Indiana University through allocation HUM200003 from the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program, which is supported by National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296.

opencc-by-4.0Apr 2023View details →

zenodo44/100

TWikiL - Twitter Wikipedia Link Dataset

The Twitter Wikipedia Link (TWikiL) dataset contains all Tweets posted on Twitter that contain a Wikipedia URL. The data was collected via Twitters academic research access and spans 15 years of Tweets from March 2006 to January 2021. TWikiL comes in two versions: TWikiL_raw is a list of Tweet IDs in CSV format. TWikiL_curated is an SQLite database, which is a curated version of TWikiL containing only links to Wikipedia articles. The curated version has been augmented with the language edition that the URL in the Tweet links to, the Wikidata identifier and a Wikipedia topic category.  TWikiL raw contains 44,945,098 Tweet IDs TWikiL curated contains 35,252,782 URLs/Wikidata concepts with 34,543,612 unique Tweets and 474,577 Tweets linking to multiple Wikipedia articles.

opencc-by-4.0Jan 2022View details →

zenodo44/100

A Multilingual Dataset of COVID-19 Vaccination Attitudes on Twitter

This dataset consists of the IDs of 2,198,090 tweets collected from Western Europe, of which 17,934 are annotated with labels indicating the originators' affective vaccination stances, including Positive (PO), Negative (NG), Positive but dissatisfaction (PD), Neutral (NE) and Off-topic (OT). all_tweets.txt contains all the ids of the collected tweets, annotated_tweets.txt contains the ids of the annotated tweets and the categories they are annotated to.

opencc-by-4.0Jan 2022View details →

zenodo44/100

Twitter Account Dataset - 10,000 accounts tested through the Botometer API

This dataset contains 10,420 accounts extracted from Twitter from tweets containing the word "vaccine". We verified each one through the Botometer API. Includes: <ul> <li>Account ID</li> <li>Echo Chamber Score (0-5)</li> <li>Fake Follower Score (0-5)</li> <li>Financial Score (0-5)</li> <li>Self Declared Score (0-5)</li> <li>Spammer Score (0-5)</li> <li>Other Score (0-5)</li> <li>Overall Score (0-5)</li> </ul> Those appearing with an 'X' correspond to deleted, modified or private accounts.

opencc-by-4.0Jan 2022View details →

zenodo44/100

Public Dataset for "Did State-sponsored Trolls Shape the 2016 US Presidential Election Discourse? Quantifying Influence on Twitter"

Dataset for the "Did State-sponsored Trolls Shape the 2016 US Presidential Election Discourse? Quantifying Influence on Twitter" paper.  The full text of the paper can be found <a href="https://zenodo.org/record/4699959#.YngKatNBy3K">here</a>. 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" by Nikos Salamanos, Michael J. Jensen, Costas Iordanou and Michael Sirivianos We have split the tweets into separate .zip files based on the date listed in their timestamps. The crawling took place from September 21 to November 7, 2016 (47 days; we did not collect data on 02/10/2016). 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. Please cite the paper in any published work that uses any of these resources.  @misc{nikos_salamanos_2021_4699959,   author       = {Nikos Salamanos and                   Michael J. Jensen and                   Costas Iordanou and                   Michael Sirivianos},   title        = {{Did State-sponsored Trolls Shape the 2016 US                     Presidential Election Discourse? Quantifying                    Influence on Twitter}},   month        = apr,   year         = 2021,   publisher    = {Zenodo},   version      = 3,   doi          = {10.5281/zenodo.4699959},   url          = {https://doi.org/10.5281/zenodo.4699959} }

opencc-by-4.0May 2022View details →

zenodo44/100

Dataset: Characterizing Anti-Asian Rhetoric During The COVID-19 Pandemic: A Sentiment Analysis Case Study on Twitter

This is the dataset, trained model, and software companion for the paper titled: Characterizing Anti-Asian Rhetoric During The COVID-19 Pandemic: A Sentiment Analysis Case Study on Twitter accepted for the Workshop on Data for the Wellbeing of Most Vulnerable of the ICWSM 2022 conference. The COVID-19 pandemic has shown a measurable increase in the usage of sinophobic comments or terms on online social media platforms. In the United States, Asian Americans have been primarily targeted by violence and hate speech stemming from negative sentiments about the origins of the novel SARS-CoV-2 virus. While most published research focuses on extracting these sentiments from social media data, it does not connect the specific news events during the pandemic with changes in negative sentiment on social media platforms. In this work we combine and enhance publicly available resources with our own manually annotated set of tweets to create machine learning classification models to characterize the sinophobic behavior. We then applied our classifier to a pre-filtered longitudinal dataset spanning two years of pandemic related tweets and overlay our findings with relevant news events.

opencc-by-4.0May 2022View details →

zenodo44/100

MMoveT15: A Twitter Dataset for Extracting and Analysing Migration-Movement Data of the European Migration Crisis 2015

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

opencc-by-4.0Jan 2019View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record