The hurricane dorian 2019 event of HumAID: 7,613 tweets, each with its ID, its text and one of 9 humanitarian categories assigned by crowdsourcing workers (1,444 other relevant information, 1,369 caution and advice, 1,083 sympathy and support, 987 rescue volunteering or donation effort, 874 not humanitarian, 815 infrastructure and utility damage, 802 displaced people and evacuations, 179 requests or urgent needs, 60 injured or dead people), split by upstream into 5,329 train, 776 dev and 1,508 test tweets.
The california wildfires 2018 event of HumAID: 7,376 tweets, each with its ID, its text and one of 10 humanitarian categories assigned by crowdsourcing workers (1,946 injured or dead people, 1,415 rescue volunteering or donation effort, 1,318 not humanitarian, 1,038 other relevant information, 472 sympathy and support, 422 infrastructure and utility damage, 368 displaced people and evacuations, 179 missing or found people, 139 caution and advice, 79 requests or urgent needs), split by upstream into 5,163 train, 752 dev and 1,461 test tweets.
The hurricane maria 2017 event of HumAID: 7,278 tweets, each with its ID, its text and one of 9 humanitarian categories assigned by crowdsourcing workers (1,977 rescue volunteering or donation effort, 1,568 other relevant information, 1,427 infrastructure and utility damage, 711 requests or urgent needs, 672 sympathy and support, 302 injured or dead people, 270 not humanitarian, 220 caution and advice, 131 displaced people and evacuations), split by upstream into 5,094 train, 742 dev and 1,442 test tweets.
Actions of students on a MOOC platform, as published by SNAP for studies of temporal interaction networks: a directed, temporal network from students to course activities. Each of the 411,749 rows is one action of one of 7,047 students on one of 97 targets (course activities), with the number of seconds since the start of the data, a 4-dimensional feature vector per action (411,749 rows) and a label per action (411,749 rows) that is 1 for the 4,066 actions after which the student dropped out.
The hurricane florence 2018 event of HumAID: 6,264 tweets, each with its ID, its text and one of 9 humanitarian categories assigned by crowdsourcing workers (1,478 rescue volunteering or donation effort, 1,310 caution and advice, 1,060 not humanitarian, 637 displaced people and evacuations, 636 other relevant information, 472 sympathy and support, 320 infrastructure and utility damage, 297 injured or dead people, 54 requests or urgent needs), split by upstream into 4,384 train, 639 dev and 1,241 test tweets.
Tweets of the Twitter accounts of the Fortune 100 companies of 2020 in the weeks around the death of George Floyd, the data of Kevin McElwee's 'The Corporate Response to #BlackLivesMatter on Twitter': 140,279 tweets from 91 company accounts as returned by the Twitter API (the Twitter API returns at most an account's latest 3,200; 12 March 2009 to 27 July 2020), the list of the 100 companies with their Twitter handle, rank and sector, and manual labels for the 6,184 tweets sent between 25 May and 25 July 2020: whether each tweet addresses racial inequity (461 do) and, for those, whether it contains the words Black Lives Matter, mentions Juneteenth or mentions donations. A second file flags 461 tweets for BLM, Juneteenth, Money and Formal Statement.
5,171 short social media posts about NFL teams, written between 2024-11-23 and 2024-12-17, scraped with a Selenium web scraper by Cameron C. (Kaggle owner cammyc), each with the author's display name, the team the post was collected for and a sentiment label (neutral (2,235), negative (1,757), positive (1,161) among well-formed rows) predicted by a fine-tuned RoBERTa model, with its confidence and raw class probabilities.
The rBrexit dataset of the paper 'Brexit Means Brexit: Selection Bias, Echo Chambers, and Entrenched Opinion on Reddit' (Rizoiu, Khuu, Law and Largeron): 809,713 entries of the r/Brexit subreddit (53,058 submissions and 756,655 comments, from 53,244 submissions' threads, by 33,831 author names) with the Reddit score, the numbered period of 27 Brexit events they fall in and a stance towards Brexit predicted by a BERT model, but no text; 5,024 submissions with their text and a stance label that Amazon Mechanical Turk workers gave by majority (anti-Brexit 887, neither 3,912, pro-Brexit 225); each user's polarity score in each period (70,042 rows); and the user interaction network of each of the 27 periods (361,723 links in total).
2015 New Year's resolutions from CrowdFlower: 5,011 tweets, a Twitter sentiment analysis of users' 2015 New Year's resolutions that, per the publisher, contains demographic and geographical data of the users and resolution categorizations. Each tweet has a resolution topic (115 distinct values) and one of 10 resolution categories (1,781 Personal Growth, 1,010 Humor, 840 Health & Fitness, 467 Recreation & Leisure, 351 Family/Friends/Relationships, 176 Finance, 126 Career, 89 Education/Training, 87 Time Management/Organization, 84 Philanthropic). Each row also has the tweet text, the author's screen name and a gender value (2,518 male, 2,493 female), the retweet count, time, free-text location, state, region and time zone; 227 tweets carry coordinates.
The Amharic part of the TextDetox multilingual toxicity dataset, compiled for the PAN 2024 and PAN 2025 TextDetox shared tasks: 5,000 short texts, each labelled toxic or non-toxic (2,500 toxic, 2,500 non-toxic). For Amharic the dataset card names the Amharic Hate Speech dataset as the source. The file has two columns, the text and the binary label, and no identifiers, dates or author information. 5,000 of the texts are distinct; the rest occur more than once.
The Arabic part of the TextDetox multilingual toxicity dataset, compiled for the PAN 2024 and PAN 2025 TextDetox shared tasks: 5,000 short texts, each labelled toxic or non-toxic (2,500 toxic, 2,500 non-toxic). For Arabic the dataset card names OSACT4 as the source. The file has two columns, the text and the binary label, and no identifiers, dates or author information. 4,991 of the texts are distinct; the rest occur more than once.
The German part of the TextDetox multilingual toxicity dataset, compiled for the PAN 2024 and PAN 2025 TextDetox shared tasks: 5,000 short texts, each labelled toxic or non-toxic (2,500 toxic, 2,500 non-toxic). For German the dataset card names the DeTox dataset and GermEval 2018/2021 as the source. The file has two columns, the text and the binary label, and no identifiers, dates or author information. 4,585 of the texts are distinct; the rest occur more than once.
The English part of the TextDetox multilingual toxicity dataset, compiled for the PAN 2024 and PAN 2025 TextDetox shared tasks: 5,000 short texts, each labelled toxic or non-toxic (2,500 toxic, 2,500 non-toxic). For English the dataset card names Jigsaw and the Unitary AI Toxicity Dataset as the source. The file has two columns, the text and the binary label, and no identifiers, dates or author information. 4,900 of the texts are distinct; the rest occur more than once.
The Spanish part of the TextDetox multilingual toxicity dataset, compiled for the PAN 2024 and PAN 2025 TextDetox shared tasks: 5,000 short texts, each labelled toxic or non-toxic (2,500 toxic, 2,500 non-toxic). For Spanish the dataset card names the CLANDESTINO Spanish toxic language dataset as the source. The file has two columns, the text and the binary label, and no identifiers, dates or author information. 5,000 of the texts are distinct; the rest occur more than once.
The French part of the TextDetox multilingual toxicity dataset, compiled for the PAN 2024 and PAN 2025 TextDetox shared tasks: 5,000 short texts, each labelled toxic or non-toxic (2,500 toxic, 2,500 non-toxic). The dataset card lists no source for French. The file has two columns, the text and the binary label, and no identifiers, dates or author information. 4,774 of the texts are distinct; the rest occur more than once.
The Hindi part of the TextDetox multilingual toxicity dataset, compiled for the PAN 2024 and PAN 2025 TextDetox shared tasks: 5,000 short texts, each labelled toxic or non-toxic (2,500 toxic, 2,500 non-toxic). For Hindi the dataset card names the Hostility Detection Dataset in Hindi and the HASOC 2019 track overview as the source. The file has two columns, the text and the binary label, and no identifiers, dates or author information. 4,963 of the texts are distinct; the rest occur more than once.
The Italian part of the TextDetox multilingual toxicity dataset, compiled for the PAN 2024 and PAN 2025 TextDetox shared tasks: 5,000 short texts, each labelled toxic or non-toxic (2,500 toxic, 2,500 non-toxic). For Italian the dataset card names AMI, HODI 2023 and the Jigsaw Multilingual Toxic Comment competition as the source. The file has two columns, the text and the binary label, and no identifiers, dates or author information. 4,975 of the texts are distinct; the rest occur more than once.
The Japanese part of the TextDetox multilingual toxicity dataset, compiled for the PAN 2024 and PAN 2025 TextDetox shared tasks: 5,000 short texts, each labelled toxic or non-toxic (2,500 toxic, 2,500 non-toxic). For Japanese the dataset card names posts from the 2channel-derived open2ch forum, filtered with the Perspective API as the source. The file has two columns, the text and the binary label, and no identifiers, dates or author information. 4,908 of the texts are distinct; the rest occur more than once.
The Russian part of the TextDetox multilingual toxicity dataset, compiled for the PAN 2024 and PAN 2025 TextDetox shared tasks: 5,000 short texts, each labelled toxic or non-toxic (2,500 toxic, 2,500 non-toxic). For Russian the dataset card names two Kaggle datasets, "Russian Language Toxic Comments" and "Toxic Russian Comments" as the source. The file has two columns, the text and the binary label, and no identifiers, dates or author information. 4,978 of the texts are distinct; the rest occur more than once.
The Tatar part of the TextDetox multilingual toxicity dataset, compiled for the PAN 2024 and PAN 2025 TextDetox shared tasks: 5,000 short texts, each labelled toxic or non-toxic (2,500 toxic, 2,500 non-toxic). For Tatar the dataset card names the textdetox authors' own data as the source. The file has two columns, the text and the binary label, and no identifiers, dates or author information. 5,000 of the texts are distinct; the rest occur more than once.
The Ukrainian part of the TextDetox multilingual toxicity dataset, compiled for the PAN 2024 and PAN 2025 TextDetox shared tasks: 5,000 short texts, each labelled toxic or non-toxic (2,500 toxic, 2,500 non-toxic). For Ukrainian the dataset card names the dataset of Dementieva, Khylenko, Babakov and Groh (WOAH 2024), hosted on Hugging Face as the source. The file has two columns, the text and the binary label, and no identifiers, dates or author information. 4,999 of the texts are distinct; the rest occur more than once.
The Chinese part of the TextDetox multilingual toxicity dataset, compiled for the PAN 2024 and PAN 2025 TextDetox shared tasks: 5,000 short texts, each labelled toxic or non-toxic (2,500 toxic, 2,500 non-toxic). The dataset card lists no source for Chinese. The file has two columns, the text and the binary label, and no identifiers, dates or author information. 5,000 of the texts are distinct; the rest occur more than once.
The Hinglish (Hindi written in Latin script) part of the TextDetox multilingual toxicity dataset, compiled for the PAN 2024 and PAN 2025 TextDetox shared tasks: 4,363 short texts, each labelled toxic or non-toxic (2,200 toxic, 2,163 non-toxic). The dataset card lists no source for Hinglish (Hindi written in Latin script). The file has two columns, the text and the binary label, and no identifiers, dates or author information. 4,237 of the texts are distinct; the rest occur more than once.
The cyclone idai 2019 event of HumAID: 3,933 tweets, each with its ID, its text and one of 10 humanitarian categories assigned by crowdsourcing workers (1,869 rescue volunteering or donation effort, 482 sympathy and support, 433 injured or dead people, 407 other relevant information, 354 infrastructure and utility damage, 143 requests or urgent needs, 89 caution and advice, 80 not humanitarian, 57 displaced people and evacuations, 19 missing or found people), split by upstream into 2,753 train, 401 dev and 779 test tweets.
The English part of OffensEval 2020 (SemEval-2020 Task 12): 3,887 tweets labeled for offensive language, 1,080 OFF (offensive) and 2,807 NOT (not offensive) for sub-task A, 0 in the training file and 3,887 in the test file (labeled by the shared task's separate label file). The English training data is not part of the files.
The Spanish training data of Task 2 (claim normalization) of the CLEF 2025 CheckThat! Lab: 3,458 social media posts, each paired with a normalized claim, a short simplified statement of the claim the post makes. Each row has the post and its normalized claim. The task asks systems to generate the normalized claim from the noisy post. The upstream repository also holds development and test files for this task; this package holds the training file only.
The #petpeeve dataset of William Yang Wang and Diyi Yang (EMNLP 2015): 3,375 tweets that use the hashtag #petpeeve, each assigned one of 60 categories of annoying behaviour (the most frequent are others, 585, and appearance, 463). The data come as a training file with 2,025 tweets and a test file with 1,350. Each row is the category and the tweet text; the files carry no tweet IDs, dates or user names.
The Danish part of OffensEval 2020 (SemEval-2020 Task 12): 3,290 tweets labeled for offensive language, 425 OFF (offensive) and 2,865 NOT (not offensive) for sub-task A, 2,961 in the training file and 329 in the test file (labeled by the shared task's separate label file).
M3 (Multi-platform, Multi-lingual, and Multimodal Meme), a benchmark for multimodal hate speech detection by Rui Xing, Qi Chai, Jie Ma, Jing Tao, Pinghui Wang, Shuming Zhang, Xinping Wang and Hao Wang: 2,455 memes, each an image with the text of the social media post it was shared in, collected from X (Twitter, 526 memes), 4chan (1,400) and Weibo (529). Each meme has the text read from its image (OCR), the post text, a label hate (1,318) or normal (1,137), the hate categories it falls in, and a short rationale. The hate categories are politics (348), race (334), violence (297), international relations (180), gender (172), religion (108), health status (106), public health (85); the 1,137 normal memes have the category none. One table of posts serves all three platforms; the column collection_member names the platform, so a single platform is a filter of the table.
The Arabic training data of the Task 1 (subjectivity in news articles) of the CLEF 2025 CheckThat! Lab: 2,446 sentences from news articles, each labelled subjective (SUBJ) or objective (OBJ). The label counts are OBJ (1,391), SUBJ (1,055). Each row has a sentence ID, the sentence and the label. The upstream data folder also holds other files of the task (development and dev-test files); this package holds the training file only.
The canada wildfires 2016 event of HumAID: 2,242 tweets, each with its ID, its text and one of 8 humanitarian categories assigned by crowdsourcing workers (934 rescue volunteering or donation effort, 380 displaced people and evacuations, 311 other relevant information, 251 infrastructure and utility damage, 161 sympathy and support, 106 caution and advice, 79 not humanitarian, 20 requests or urgent needs), split by upstream into 1,569 train, 228 dev and 445 test tweets.
The kaikoura earthquake 2016 event of HumAID: 2,195 tweets, each with its ID, its text and one of 9 humanitarian categories assigned by crowdsourcing workers (493 caution and advice, 432 sympathy and support, 312 infrastructure and utility damage, 311 other relevant information, 224 not humanitarian, 207 rescue volunteering or donation effort, 105 injured or dead people, 87 displaced people and evacuations, 24 requests or urgent needs), split by upstream into 1,536 train, 224 dev and 435 test tweets.
CHECKED, a Chinese COVID-19 fake news dataset from the microblog platform Weibo (Yang, Zhou and Zafarani, Social Network Analysis and Mining 2021): 2,104 microblogs posted between 2019-12-31 and 2020-08-30, 344 labelled fake (each with the expert analysis that identified it as false) and 1,760 real, with the 1,185,701 comments and 1,868,174 reposts (forwards) they received, including their text, date and hashed author. Microblog IDs and user IDs are replaced by 32-character hashes and user names are not released.
The puebla mexico earthquake 2017 event of HumAID: 2,015 tweets, each with its ID, its text and one of 8 humanitarian categories assigned by crowdsourcing workers (984 rescue volunteering or donation effort, 367 sympathy and support, 254 injured or dead people, 167 infrastructure and utility damage, 109 other relevant information, 61 requests or urgent needs, 38 not humanitarian, 35 caution and advice), split by upstream into 1,410 train, 205 dev and 400 test tweets.
The Hebrew part of the TextDetox multilingual toxicity dataset, compiled for the PAN 2024 and PAN 2025 TextDetox shared tasks: 2,011 short texts, each labelled toxic or non-toxic (807 toxic, 1,204 non-toxic). For Hebrew the dataset card names the Hebrew Offensive Language Dataset as the source. The file has two columns, the text and the binary label, and no identifiers, dates or author information. 1,977 of the texts are distinct; the rest occur more than once.
The pakistan earthquake 2019 event of HumAID: 1,958 tweets, each with its ID, its text and one of 8 humanitarian categories assigned by crowdsourcing workers (823 sympathy and support, 401 injured or dead people, 213 not humanitarian, 154 other relevant information, 152 rescue volunteering or donation effort, 125 infrastructure and utility damage, 71 caution and advice, 19 requests or urgent needs), split by upstream into 1,370 train, 199 dev and 389 test tweets.
The Dutch data of Task 1 (check-worthiness) of the CLEF 2024 CheckThat! Lab: 1,913 Dutch texts, each labelled by whether it contains a claim worth fact-checking. The labels are Yes (823) and No (1,090). The data come in three files: train 995 (405 Yes, 590 No), dev 252 (102 Yes, 150 No), dev-test 666 (316 Yes, 350 No). Each row has a tweet ID, the tweet's URL, the tweet text and the label.
The midwestern us floods 2019 event of HumAID: 1,880 tweets, each with its ID, its text and one of 7 humanitarian categories assigned by crowdsourcing workers (788 rescue volunteering or donation effort, 389 not humanitarian, 273 other relevant information, 165 sympathy and support, 140 infrastructure and utility damage, 79 caution and advice, 46 requests or urgent needs), split by upstream into 1,316 train, 191 dev and 373 test tweets.
The Portuguese training data of Task 2 (claim normalization) of the CLEF 2025 CheckThat! Lab: 1,735 social media posts, each paired with a normalized claim, a short simplified statement of the claim the post makes. Each row has the post and its normalized claim. The task asks systems to generate the normalized claim from the noisy post. The upstream repository also holds development and test files for this task; this package holds the training file only.
The hurricane matthew 2016 event of HumAID: 1,654 tweets, each with its ID, its text and one of 9 humanitarian categories assigned by crowdsourcing workers (395 sympathy and support, 328 other relevant information, 326 rescue volunteering or donation effort, 224 injured or dead people, 178 infrastructure and utility damage, 76 not humanitarian, 53 requests or urgent needs, 38 displaced people and evacuations, 36 caution and advice), split by upstream into 1,157 train, 168 dev and 329 test tweets.
The Italian training data of the Task 2 (subjectivity in news articles) of the CLEF 2023 CheckThat! Lab: 1,613 sentences from news articles, each labelled subjective (SUBJ) or objective (OBJ). The label counts are OBJ (1,231), SUBJ (382). Each row has a sentence ID, the sentence and the label. The upstream data folder also holds other files of the task (development and test files); this package holds the training file only.
The Italian training data of the Task 2 (subjectivity in news articles) of the CLEF 2024 CheckThat! Lab: 1,613 sentences from news articles, each labelled subjective (SUBJ) or objective (OBJ). The label counts are OBJ (1,231), SUBJ (382). Each row has a sentence ID, the sentence and the label. The upstream data folder also holds other files of the task (development, dev-test and test files); this package holds the training file only.
The Italian training data of the Task 1 (subjectivity in news articles) of the CLEF 2025 CheckThat! Lab: 1,613 sentences from news articles, each labelled subjective (SUBJ) or objective (OBJ). The label counts are OBJ (1,231), SUBJ (382). Each row has a sentence ID, the sentence and the label. The upstream data folder also holds other files of the task (development and dev-test files); this package holds the training file only.
The ecuador earthquake 2016 event of HumAID: 1,563 tweets, each with its ID, its text and one of 8 humanitarian categories assigned by crowdsourcing workers (555 injured or dead people, 394 rescue volunteering or donation effort, 319 sympathy and support, 91 requests or urgent needs, 81 other relevant information, 70 infrastructure and utility damage, 30 caution and advice, 23 not humanitarian), split by upstream into 1,094 train, 159 dev and 310 test tweets.
The greece wildfires 2018 event of HumAID: 1,515 tweets, each with its ID, its text and one of 9 humanitarian categories assigned by crowdsourcing workers (495 injured or dead people, 356 rescue volunteering or donation effort, 322 sympathy and support, 159 other relevant information, 74 not humanitarian, 38 infrastructure and utility damage, 26 caution and advice, 25 requests or urgent needs, 20 missing or found people), split by upstream into 1,060 train, 154 dev and 301 test tweets.
The Russian meteor event of CrisisLexT26 (Chelyabinsk, Russia): 1,442 tweets about it, each labeled by crowdsourcing workers for informativeness, information type and information source, and the list of 8,392 tweet IDs that the CrisisLex authors collected for the event, posted between 14 February 2013 and 7 March 2013, with a flag for the 8,365 that fall within the period they analyzed. For informativeness, the workers judged 642 related and informative, 491 related but not informative, 271 not related and 38 not applicable of the labeled tweets. The record of the event gives its category (Natural, Others, Meteorite), its location and the keywords used to collect the tweets.
The Costa Rica earthquake event of CrisisLexT26 (Costa Rica, Costa Rica): 1,412 tweets about it, each labeled by crowdsourcing workers for informativeness, information type and information source, and the list of 2,369 tweet IDs that the CrisisLex authors collected for the event, posted between 4 September 2012 and 21 September 2012, with a flag for the 2,193 that fall within the period they analyzed. For informativeness, the workers judged 742 related and informative, 167 related but not informative, 399 not related and 104 not applicable of the labeled tweets. The record of the event gives its category (Natural, Geophysical, Earthquake), its location and the keywords used to collect the tweets.
The 4chan part of M3: 1,400 memes of M3 (image, OCR text, post text, label and rationale), 1,062 labeled hate and 338 normal. Hate categories: race (319), politics (251), violence (178), international relations (152), gender (144), health status (101), religion (88), public health (78).
The hateful memes of M3: 1,318 memes of M3 (image, OCR text, post text, label and rationale), 1,318 labeled hate and 0 normal. Hate categories: politics (348), race (334), violence (297), international relations (180), gender (172), religion (108), health status (106), public health (85).
The Savar building collapse event of CrisisLexT26 (Savar, Bangladesh): 1,250 tweets about it, each labeled by crowdsourcing workers for informativeness, information type and information source, and the list of 4,126 tweet IDs that the CrisisLex authors collected for the event, posted between 23 April 2013 and 1 June 2013, with a flag for the 4,070 that fall within the period they analyzed. For informativeness, the workers judged 836 related and informative, 75 related but not informative, 305 not related and 34 not applicable of the labeled tweets. The record of the event gives its category (Human-induced, Unintentional, Collapse), its location and the keywords used to collect the tweets.
Twitter (X)
Reddit
4chan
Weibo