TG-MOISES, a corpus of 6,805,925 messages from 490 public Telegram channels associated with conspiracy theories, anti-vaccine movements and far-right or nationalist narratives, in English, Spanish, Turkish, Lithuanian, French, German and other languages, collected by the MOISES project team through the Telegram API and released on Zenodo. 3,273,829 of the messages carry cleaned text; for the rest the export holds only an ID, the channel and flags. 20,941 messages (from 464 channels) were annotated by hand on a five-dimension malicious-content taxonomy (Role, Tactic, Feature, Target and Vulnerability), giving 401,150 label rows; the package also has the per-channel message counts, language distributions and topic-model output (404 channels, 21,614 topics with keywords, a theme text and an embedding vector each). The data have no author or time field for almost every message: only 24,752 messages have a date, between 2020-11-28 and 2023-12-19.
Sentiment140 (Go, Bhayani and Huang, Stanford, 2009): 1,600,000 English tweets from April to June 2009 for training, labelled automatically from the emoticons they contain (800,000 negative, 800,000 positive), and 498 tweets labelled by hand for testing (177 negative, 139 neutral, 182 positive). Each tweet has its ID, post time, author name, the search query that found it and its text. The tweets come from 659,775 distinct accounts in the training file.
The GitHub Stargazers graph classification dataset of the Karate Club project: 12,725 small social networks of developers who starred popular machine-learning or web-development repositories on GitHub (up to August 2019). Nodes are users and links are follower relationships; each graph is the largest connected component of its repository group (at least 10 users) and has a binary label. This copy holds 2,985,781 links and 12,725 graph labels (6,808 labelled 1 and 5,917 labelled 0).
r/Fakeddit (Nakamura, Levy and Wang, LREC 2020): 1,063,106 Reddit posts from 22 subreddits, with the title, a cleaned title, the post time, score, comment count, upvote ratio, the address of the attached image and three labels at different levels of detail: a 2-way label (578,189 coded 0, 484,917 coded 1), a 3-way label and a 6-way label (codes 0 to 5). The posts are split into train (878,218), validation (92,444) and public test (92,444) files. 773,564 posts are marked as having an image. Images are not included, only their URLs.
DiPD (Disruptive Event Prediction Dataset): tweets from November 2021 (timestamps from 4 to 23 November 2021) about known disruptive events such as protests, riots and climate strikes (label 1) and tweets not about such events (label 0), each in a raw and a preprocessed version (event-preprocessed (94,855), event-raw (94,855), non-event-preprocessed (168,706), non-event-raw (168,706)), with the author's screen name, profile location, follower, retweet and tweet counts. By Sanskar Soni, Dev Mehta, Vinush Vishwanath, Aditi Seetha and Satyendra Singh Chouhan (arXiv 2111.15629).
CONVEX (Community Notes for Visual Misinformation on X): 152,266 pairs of a Community Note and the X post it is attached to (image-subset (66,135), video-subset (86,131); 131,334 distinct tweets), for posts with images or videos, with the post's reply, like, view and retweet counts, the note text and date, and a misinformation type (final labels: miscaptioned (104,915), edited (23,427), ai_generated (21,845), other (2,079)) from keyword rules, a vision-language model and voting. By Zacharias Chrysidis, Stefanos-Iordanis Papadopoulos and Symeon Papadopoulos for the CVPR 2026 workshop paper "The Synthetic Media Shift".
ReCOVery: 2,029 news articles about COVID-19 from 55 distinct publisher names (the README says the articles were collected from 22 reliable and 38 unreliable websites), published 2020-01-21 to 2020-05-25, with the full body text, URL, publisher, authors, head image, political bias and country of the source and a reliability label (reliable (1,364), unreliable (665)), and 140,820 IDs of tweets that shared 1,747 of the articles (tweet IDs only). By Xinyi Zhou, Apurva Mulay, Emilio Ferrara and Reza Zafarani for CIKM 2020.
128,484 tweets labelled for political leaning, published on Hugging Face by Châtelet AI as 'political-leaning-tweets-100k': neutral (114,477), liberal (9,992) and conservative (4,015). The labels are machine-generated by a large language model, according to the dataset card. The file has the tweet text and the label only: no IDs, dates, authors or language tags; 123,825 of the texts are distinct.
Misinformation & Fake News text dataset: 85,986 articles in three files (fake (43,642), true (34,975), russian-propaganda (7,369)), the articles of misinformation, fake news or propaganda (fake), articles from news outlets (true) and a subset on Russian propaganda, compiled on Kaggle by Steven Peutz. Only the article text is included.
HumAID: 76,484 tweets about 19 natural disasters of 2016 to 2019 (earthquakes, floods, hurricanes, cyclones and wildfires), each labeled with one of 10 humanitarian categories by crowdsourcing workers, as published by Firoj Alam, Umair Qazi, Muhammad Imran and Ferda Ofli of the Qatar Computing Research Institute. Each tweet is given with its ID, its text, the category and the split it belongs to (53,531 train, 7,793 dev, 15,160 test, as upstream provides them). The categories are: 21,278 rescue volunteering or donation effort, 12,144 other relevant information, 8,931 sympathy and support, 8,163 infrastructure and utility damage, 7,303 injured or dead people, 6,296 not humanitarian, 5,394 caution and advice, 3,999 displaced people and evacuations, 2,618 requests or urgent needs, 358 missing or found people. One table of tweets serves all 19 events; the column collection_member names the event, so a single event is a filter of the table.
The TextDetox multilingual toxicity dataset, compiled for the PAN 2024 and PAN 2025 TextDetox shared tasks: 71,374 short texts in 15 languages (English, Russian, Ukrainian, German, Spanish, Amharic, Chinese, Arabic, Hindi, Italian, French, Hebrew, Hinglish, Tatar and Japanese), each labelled toxic or non-toxic (35,507 toxic, 35,867 non-toxic). Each language is one file with a text column and a binary label column; there are no identifiers, dates or author information. The column collection_member names the language of each row. 70,286 of the texts are distinct across all languages.
OffensEval 2020 (SemEval-2020 Task 12), a multilingual collection of tweets labeled for offensive language: 62,748 tweets in five languages (Arabic 10,000, Danish 3,290, English 3,887, Greek 10,287, Turkish 35,284), each labeled OFF (offensive; 13,071 tweets) or NOT (not offensive; 49,677 tweets) for sub-task A of the shared task, with the shared task's training and test split. The datasets were collected and annotated separately for each language by the authors named in the shared task paper; the English training data is not part of the files (the card points to a separate download), so English has test tweets only. The column collection_member names the language, so one language is a filter of each table.
Sentiment labels for 60,823 tweets in four Nigerian languages, the training and validation parts of the NaijaSenti corpus released by HausaNLP: Hausa (21,703), Igbo (14,776), Nigerian Pidgin (7,489), Yoruba (16,855). Each row is a tweet ID and a label (negative 19,237, neutral 20,427, positive 21,159). Tweet texts are not distributed. In this copy all 60,823 tweet IDs are written in floating-point scientific notation (for example 1.3081401021499105e+18), so they have lost digits and cannot be used to retrieve the tweets; only 54,353 of the values are distinct.
CrisisLexT6: 60,082 English tweets about 6 crisis events of 2012 and 2013 (Hurricane Sandy, the Boston Marathon bombings, the Oklahoma tornado, the West Texas explosion, the Alberta floods and the Queensland floods), each labeled by crowdsourcing workers as on-topic (related to the crisis, 32,462 tweets) or off-topic (27,620 tweets), as published by Alexandra Olteanu, Carlos Castillo, Fernando Diaz and Sarah Vieweg. Between 9,992 and 10,033 tweets are labeled per event; the upstream README says each file was sampled half from a geo-based sample and half from a keyword-based sample, and that each label is the majority vote of at least three workers. One table of tweets serves all 6 events; the column collection_member names the event, so a single event is a filter of the table.
SenWave: tweets about COVID-19 labeled for ten sentiments, by Qiang Yang and colleagues. The package holds 10,000 English tweets and 9,999 Arabic tweets that annotators labeled with any of ten emotions (optimistic, thankful, empathetic, pessimistic, anxious, sad, annoyed, denial, official report, joking), and Spanish, French and Italian versions of the 10,000 English tweets that the authors made with Google Translate, each carrying the English labels. The Arabic tweets also carry one of four overall sentiment labels (neutral 4,269, negative 2,750, positive 1,562, joking 1,418). That is 49,999 labeled tweets in five languages. The tweets were collected from 1 March 2020 (per the upstream README). The upstream repository also lists a large number of unlabeled tweet IDs; they are not part of these files. The files have no usable tweet IDs and no users, times or other fields.
Data of the paper on Twitter accounts with GAN-generated profile pictures (OSoMe, Indiana University): the profile records of 1,353 accounts with 48,945 of their recent tweets (2021-10-01 to 2023-05-29), a list of 1,353 accounts in six groups (astroturf 1,066, elon_watch 246, sunday 18, blue_check 11, ind_acc 7, bart 5) with the file name of each account's profile picture, and a score for the profile pictures of 254,275 randomly sampled accounts. The profile pictures themselves (1,353 JPEG files) are in the original archive only.
ISOT Fake News dataset: 44,898 news articles (real (21,417), fake (23,481)), real ones crawled from Reuters.com and fake ones collected from unreliable websites flagged by PolitiFact and Wikipedia, each with title, text, subject and publication date, compiled by Hadeer Ahmed, Issa Traore and Sherif Saad (ISOT Research Lab, University of Victoria). The label is given by the file the article is in.
Messages from five public programming-help channels on Slack (Python, Clojure, Elm beginners, Elm general and Racket), grouped into conversations, with a label for 2,000 conversations saying whether it is archival-worthy, that is whether it contains information that could be useful to other users. The package holds 40,010 messages in 2,109 conversations; 2,000 of the conversations are labelled (1,310 archival-worthy, 690 not). According to the Zenodo record, user names are replaced with fake names and absolute timestamps with relative times in seconds.
The Turkish part of OffensEval 2020 (SemEval-2020 Task 12): 35,284 tweets labeled for offensive language, 6,847 OFF (offensive) and 28,437 NOT (not offensive) for sub-task A, 31,756 in the training file and 3,528 in the test file (labeled by the shared task's separate label file).
The Spanish data of Task 1 (check-worthiness) of the CLEF 2024 CheckThat! Lab: 29,990 Spanish texts, each labelled by whether it contains a claim worth fact-checking. The labels are Yes (4,341) and No (25,649). The data come in three files: train 19,990 (3,128 Yes, 16,862 No), dev 5,000 (704 Yes, 4,296 No), dev-test 5,000 (509 Yes, 4,491 No). Each row has a tweet ID, the tweet's URL, the tweet text and the label.
CrisisLexT26: tweets about 26 crisis events of 2012 and 2013 (earthquakes, floods, typhoons, wildfires, explosions, train and helicopter crashes, a building collapse, a nightclub fire, a meteor, an airport shooting and the Boston Marathon bombings), with the text of 27,933 tweets that crowdsourcing workers labeled by informativeness, information type and information source. Between 1,000 and 1,442 tweets are labeled per event. Each label is the majority vote of at least three workers. The package also holds the list of 286,096 tweet IDs that upstream collected for the 26 events, with their timestamps and a flag for whether each tweet fell within the period the authors analyzed, and one record per event with its location, category, start day and the keywords used to collect it. One table of tweets serves all 26 events; the column collection_member names the event, so a single event is a filter of the table.
Reddit-V, a virality prediction dataset of El-amrany, Brust, Lamsiyah and Bouvry (University of Luxembourg, RANLP 2025): 27,587 Reddit posts with an image, from 91 subreddits in seven category values (Humor 11,852, Creative 6,072, Reactions 2,687, Gaming 2,551, Educational 2,321, News 2,103 and one PersonalStories), posted between 2018-11-13 and 2024-12-06. Each post has its title, subreddit, author, time, upvotes, comments, image details, subreddit subscriber count, top comments, sentiment scores and a viral label (3,497 viral, 24,090 not viral). The package also holds test_reddit.csv (4,138 rows, with an extra image-file column) as a separate table; 4,133 of its post IDs are also in the main file.
26,985 social-media posts in English (13,609) and Malay (13,376) labelled for binary hate-speech detection (12,343 hate, 14,642 non-hate), compiled by Jun Chen Tan and Lee-Yeng Ong of Multimedia University from five public datasets: HateXplain (13,609 posts), Toxicity-Small (5,252), Snapshot (4,449), HateM (3,584) and Supervised-Twitter (91). Each row is the cleaned text, its label, its source dataset and its language. The files carry no IDs, dates or user names.
24,783 English tweets collected with a lexicon of hate-speech terms from hatebase.org and labelled by CrowdFlower workers as hate speech (1,430), offensive language (19,190) or neither (4,163), the data of Davidson, Warmsley, Macy and Weber, 'Automated Hate Speech Detection and the Problem of Offensive Language' (ICWSM 2017). Each tweet has the number of workers who coded it (22,807 were coded by three, the most by any is 9), how many of them chose each of the three labels, and the label chosen by the majority. The file holds no tweet IDs, dates or user names.
23,992 YouTube comments with a sentiment label (Negative 7,783, Neutral 7,755, Positive 8,454), a dominant emotional tone (2,372 distinct values), and lists of positive and negative aspects the comment mentions, published on Hugging Face by AnandforU as 'YouTube Comment Insights (Clean)'. The data are split into train (19,992 comments), validation (2,000) and test (2,000). 10,227 comments have at least one listed positive aspect and 11,340 at least one negative aspect. The comments carry no IDs, dates, video references or author information.
The English data of Task 1 (check-worthiness) of the CLEF 2024 CheckThat! Lab: 23,851 sentences from political debates, each labelled by whether it contains a claim worth fact-checking. The labels are Yes (5,759) and No (18,092). The data come in three files: train 22,501 (5,413 Yes, 17,088 No), dev 1,032 (238 Yes, 794 No), dev-test 318 (108 Yes, 210 No). Each row has a sentence ID, the sentence and the label.
The English data of Subtask 1B (check-worthiness, text only) of Task 1 of the CLEF 2023 CheckThat! Lab: 23,533 sentences from political debates, each labelled by whether it contains a claim worth fact-checking. The labels are Yes (5,651) and No (17,882). The data come in three files: train 16,876 (4,058 Yes, 12,818 No), dev 5,625 (1,355 Yes, 4,270 No), dev-test 1,032 (238 Yes, 794 No). Each row has a sentence ID, the sentence and the label.
Fact-checked news articles from the FakeNewsNet repository (Shu et al.): 23,196 articles, with their URL, title and the IDs of the tweets that shared them. 1,056 come from PolitiFact (432 labelled fake, 624 real) and 22,140 from GossipCop (5,323 fake, 16,817 real). The tweet ID lists hold 2,063,442 IDs in total; 1,501 articles have an empty list. The files contain no article text and no tweet content.
Menéame Media Bias Interaction Dataset: 14,995 news stories from the Spanish social news site Menéame, submitted between 6 December 2005 and 30 December 2021 (UTC), each with its text, outlet, tags, score and an automatic bias label and probability from a DistilBERT classifier, plus 36 features derived from the comments on the story. The package also holds karma statistics for 183,822 stories, 20,000 sampled comments with sentiment and emotion scores, user-level bias exposure for 45,392 users and 519,769 user-outlet comment counts.
The Spanish data of Subtask 1B (check-worthiness, text only) of Task 1 of the CLEF 2023 CheckThat! Lab: 14,990 Spanish tweets, each labelled by whether it contains a claim worth fact-checking. The labels are Yes (3,217) and No (11,773). The data come in three files: train 7,490 (2,208 Yes, 5,282 No), dev 2,500 (305 Yes, 2,195 No), dev-test 5,000 (704 Yes, 4,296 No). Each row has a tweet ID, the tweet's URL, the tweet text and the label. The upstream zip also holds the tweet objects of 14,064 tweets, kept in a separate table.
Twitter US Airline Sentiment, a CrowdFlower job on the problems of each major U.S. airline: 14,640 tweets about 6 airlines (United 3,822, US Airways 2,913, American 2,759, Southwest 2,420, Delta 2,222, Virgin America 504); the publisher says the tweets were scraped in February 2015. Contributors first classified each tweet as positive, negative or neutral (9,178 negative, 3,099 neutral, 2,363 positive) and then gave a reason for the negative ones (9,178 tweets have a reason, in 10 categories). Each row has the crowd job's unit ID, the sentiment and the reason with the confidence of each, the airline, the author's screen name, the tweet text, its retweet count and time, and the author's free-text location and time zone; 1,019 tweets carry coordinates. The package also holds the copy of the tweets from the folder named kaggle-v2 (Tweets.csv, 14,640 rows) as its own table.
First GOP debate sentiment analysis: 13,871 tweets about the Republican presidential primary debate in Ohio in August 2015, as labeled by Figure Eight (CrowdFlower) contributors, who judged whether each tweet was relevant, which candidate it mentioned, which subject it concerned and its sentiment. The publisher says the non-relevant messages were removed, and all 13,871 rows are marked relevant. Sentiment: 8,493 Negative, 3,142 Neutral, 2,236 Positive. Candidate: 11 values plus 96 empty. Each row has the four labels with their confidence values, the author's screen name, the tweet text, retweet count, time, free-text location and time zone; 21 tweets carry coordinates. The package also holds the copy of the data from the folder named kaggle-v2 (Sentiment.csv, 13,871 rows) as its own table.
LIAR: 12,836 short statements by public figures, each labelled by PolitiFact's truthfulness scale (half-true (2,638), false (2,511), mostly-true (2,466), barely-true (2,108), true (2,063), pants-fire (1,050)), with the subject, speaker, speaker's job, state and party, counts of the speaker's earlier statements on each rating and the context, in the benchmark's split into train, valid and test (train (10,269), valid (1,284), test (1,283)). 3,318 distinct speakers. By William Yang Wang (UC Santa Barbara) for ACL 2017, "Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection.
11,931 English tweets about finance and markets, collected with the Twitter API and annotated with the sentiment bearish, bullish or neutral (Neutral (7,744), Bullish (2,398), Bearish (1,789)), in the upstream split into train and validation (validation (2,388), train (9,543)). Published on Hugging Face by the account zeroshot as "twitter-financial-news-sentiment".
The English training data of Task 2 (claim normalization) of the CLEF 2025 CheckThat! Lab: 11,374 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.
Disasters on Social Media from CrowdFlower (Figure Eight): 10,876 tweets that the publisher says were culled with searches for words such as 'ablaze', 'quarantine' and 'pandemonium', each judged by contributors on whether it refers to a disaster event (as opposed to a joke, a film review or something non-disastrous). Judgments: 6,187 Not Relevant, 4,673 Relevant, 16 Can't Decide. Each row has the crowd job's unit ID, the judgment and its confidence, the search keyword (221 distinct), the author's free-text location, the tweet text, and two number columns named tweetid and userid. The package also holds two files from the folder named kaggle-nlp-getting-started as their own tables: train.csv (7,613 rows with a 0/1 target) and test.csv (3,263 rows without one).
The Greek part of OffensEval 2020 (SemEval-2020 Task 12): 10,287 tweets labeled for offensive language, 2,728 OFF (offensive) and 7,559 NOT (not offensive) for sub-task A, 8,743 in the training file and 1,544 in the test file (labeled by the shared task's separate label file).
The Queensland floods event of CrisisLexT6: 10,033 English tweets, each with its ID, its text and a label from crowdsourcing workers, 5,414 on-topic (related to the crisis) and 4,619 off-topic. The CrisisLex data page dates the event from 2013-01-27, for 6 days.
The Alberta floods event of CrisisLexT6: 10,031 English tweets, each with its ID, its text and a label from crowdsourcing workers, 5,189 on-topic (related to the crisis) and 4,842 off-topic. The CrisisLex data page dates the event from 2013-06-21, for 11 days.
The Boston Marathon bombings event of CrisisLexT6: 10,012 English tweets, each with its ID, its text and a label from crowdsourcing workers, 5,648 on-topic (related to the crisis) and 4,364 off-topic. The CrisisLex data page dates the event from 2013-04-15, for 5 days.
The Hurricane Sandy event of CrisisLexT6: 10,008 English tweets, each with its ID, its text and a label from crowdsourcing workers, 6,138 on-topic (related to the crisis) and 3,870 off-topic. The CrisisLex data page dates the event from 2012-10-28, for 3 days.
The West Texas explosion event of CrisisLexT6: 10,006 English tweets, each with its ID, its text and a label from crowdsourcing workers, 5,246 on-topic (related to the crisis) and 4,760 off-topic. The CrisisLex data page dates the event from 2013-04-17, for 11 days.
The Arabic part of OffensEval 2020 (SemEval-2020 Task 12): 10,000 tweets labeled for offensive language, 1,991 OFF (offensive) and 8,009 NOT (not offensive) for sub-task A, 8,000 in the training file and 2,000 in the test file (labeled by the shared task's separate label file).
The Oklahoma tornado event of CrisisLexT6: 9,992 English tweets, each with its ID, its text and a label from crowdsourcing workers, 4,827 on-topic (related to the crisis) and 5,165 off-topic. The CrisisLex data page dates the event from 2013-05-20, for 11 days.
Fake News Detection: 9,900 news articles labelled Fake or Real (Fake (5,000), Real (4,900)), each as one text field, published on Kaggle by Vishakh Patel. 9,900 of the 9,900 texts are identical to the title, a space and the text of an article of the ISOT Fake News dataset, which the Kaggle page does not name as the source.
The hurricane irma 2017 event of HumAID: 9,399 tweets, each with its ID, its text and one of 9 humanitarian categories assigned by crowdsourcing workers (2,358 other relevant information, 1,881 infrastructure and utility damage, 1,590 rescue volunteering or donation effort, 894 injured or dead people, 755 displaced people and evacuations, 615 not humanitarian, 613 caution and advice, 567 sympathy and support, 126 requests or urgent needs), split by upstream into 6,579 train, 958 dev and 1,862 test tweets.
TweetBLM: 9,165 tweets related to Black Lives Matter, each with a hate-speech label (counts of the labels: 0 (6,081), 1 (3,084)), its hashtags, the client it was posted from and three fields about the author's account, by Raj Ratn Pranesh, Sumit Kumar and Ambesh Shekhar (Birla Institute of Technology, Mesra), 2020. The file has no tweet IDs, user names or tweet times.
The hurricane harvey 2017 event of HumAID: 9,112 tweets, each with its ID, its text and one of 9 humanitarian categories assigned by crowdsourcing workers (2,823 rescue volunteering or donation effort, 1,767 other relevant information, 1,217 infrastructure and utility damage, 698 injured or dead people, 688 displaced people and evacuations, 635 sympathy and support, 541 caution and advice, 410 not humanitarian, 333 requests or urgent needs), split by upstream into 6,378 train, 929 dev and 1,805 test tweets.
The Arabic data of Task 1 (check-worthiness) of the CLEF 2024 CheckThat! Lab: 8,926 Arabic texts, each labelled by whether it contains a claim worth fact-checking. The labels are Yes (3,031) and No (5,895). The data come in three files: train 7,333 (2,243 Yes, 5,090 No), dev 1,093 (411 Yes, 682 No), dev-test 500 (377 Yes, 123 No). Each row has a tweet ID, the tweet's URL, the tweet text and the label.
The kerala floods 2018 event of HumAID: 7,984 tweets, each with its ID, its text and one of 9 humanitarian categories assigned by crowdsourcing workers (4,294 rescue volunteering or donation effort, 955 other relevant information, 835 sympathy and support, 590 requests or urgent needs, 456 not humanitarian, 363 injured or dead people, 296 infrastructure and utility damage, 139 caution and advice, 56 displaced people and evacuations), split by upstream into 5,588 train, 814 dev and 1,582 test tweets.
Telegram
Twitter (X)
GitHub
Reddit
Slack
Instagram
YouTube
Menéame