Weather sentiment from CrowdFlower (Figure Eight): 1,000 weather-related tweets, each graded by 20 contributors for the emotion the author expresses specifically about the weather (271 Negative, 261 Neutral / author is just sharing information, 235 Tweet not related to weather condition, 231 Positive, 2 I can't tell), plus the follow-up job in which 10 contributors judged whether the crowd's grade was correct, for the same 1,000 tweets (answers: 992 Yes, 5 No, 3 I can't tell). Each tweet has an ID, its text, the emotion and the confidence upstream computed for it; the evaluation file's judgments are in their own table.
The English training data of the Task 2 (subjectivity in news articles) of the CLEF 2023 CheckThat! Lab: 830 sentences from news articles, each labelled subjective (SUBJ) or objective (OBJ). The label counts are OBJ (532), SUBJ (298). Each row has a sentence ID, the sentence, the label and a solved_conflict flag. The upstream data folder also holds other files of the task (development and test files); this package holds the training file only.
The English training data of the Task 2 (subjectivity in news articles) of the CLEF 2024 CheckThat! Lab: 830 sentences from news articles, each labelled subjective (SUBJ) or objective (OBJ). The label counts are OBJ (532), SUBJ (298). Each row has a sentence ID, the sentence, the label and a solved_conflict flag. 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 English training data of the Task 1 (subjectivity in news articles) of the CLEF 2025 CheckThat! Lab: 830 sentences from news articles, each labelled subjective (SUBJ) or objective (OBJ). The label counts are OBJ (532), SUBJ (298). Each row has a sentence ID, the sentence, the label and a solved_conflict flag. The upstream data folder also holds other files of the task (development and dev-test files); this package holds the training file only.
The Dutch training data of the Task 2 (subjectivity in news articles) of the CLEF 2023 CheckThat! Lab: 800 sentences from news articles, each labelled subjective (SUBJ) or objective (OBJ). The label counts are OBJ (489), SUBJ (311). 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 German training data of the Task 2 (subjectivity in news articles) of the CLEF 2023 CheckThat! Lab: 800 sentences from news articles, each labelled subjective (SUBJ) or objective (OBJ). The label counts are OBJ (492), SUBJ (308). 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 Turkish training data of the Task 2 (subjectivity in news articles) of the CLEF 2023 CheckThat! Lab: 800 sentences from news articles, each labelled subjective (SUBJ) or objective (OBJ). The label counts are OBJ (422), SUBJ (378). 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 German training data of the Task 2 (subjectivity in news articles) of the CLEF 2024 CheckThat! Lab: 800 sentences from news articles, each labelled subjective (SUBJ) or objective (OBJ). The label counts are OBJ (492), SUBJ (308). 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 German training data of the Task 1 (subjectivity in news articles) of the CLEF 2025 CheckThat! Lab: 800 sentences from news articles, each labelled subjective (SUBJ) or objective (OBJ). The label counts are OBJ (492), SUBJ (308). 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 maryland floods 2018 event of HumAID: 742 tweets, each with its ID, its text and one of 8 humanitarian categories assigned by crowdsourcing workers (140 missing or found people, 137 other relevant information, 110 sympathy and support, 79 infrastructure and utility damage, 77 not humanitarian, 73 rescue volunteering or donation effort, 70 caution and advice, 56 injured or dead people), split by upstream into 519 train, 75 dev and 148 test tweets.
The Bulgarian training data of the Task 2 (subjectivity in news articles) of the CLEF 2024 CheckThat! Lab: 729 sentences from news articles, each labelled subjective (SUBJ) or objective (OBJ). The label counts are OBJ (406), SUBJ (323). 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 Bulgarian training data of the Task 1 (subjectivity in news articles) of the CLEF 2025 CheckThat! Lab: 729 sentences from news articles, each labelled subjective (SUBJ) or objective (OBJ). The label counts are OBJ (406), SUBJ (323). 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 srilanka floods 2017 event of HumAID: 560 tweets, each with its ID, its text and one of 8 humanitarian categories assigned by crowdsourcing workers (319 rescue volunteering or donation effort, 56 other relevant information, 46 injured or dead people, 40 sympathy and support, 34 requests or urgent needs, 28 caution and advice, 20 not humanitarian, 17 infrastructure and utility damage), split by upstream into 392 train, 57 dev and 111 test tweets.
The Modern Standard Arabic training data of Task 2 (claim normalization) of the CLEF 2025 CheckThat! Lab: 540 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 Weibo part of M3: 529 memes of M3 (image, OCR text, post text, label and rationale), 124 labeled hate and 405 normal. Hate categories: violence (86), politics (16), international relations (14), gender (12), race (10), religion (4), health status (2), public health (1).
The X (Twitter) part of M3: 526 memes of M3 (image, OCR text, post text, label and rationale), 132 labeled hate and 394 normal. Hate categories: politics (81), violence (33), gender (16), religion (16), international relations (14), public health (6), race (5), health status (3).
The Arabic training data of Task 2 (claim normalization) of the CLEF 2025 CheckThat! Lab: 470 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 Punjabi training data of Task 2 (claim normalization) of the CLEF 2025 CheckThat! Lab: 445 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.
408 posts from the r/Bundesliga subreddit ("German Bundesliga: News & Highlights"), ordered by club (16 clubs), with the post's vote count, comment count, author name and age as shown by Reddit, collected for a Zenodo deposit by Pau Gómez Martínez and Ricardo Rodríguez Fernández (April 2023). The text column holds a short text per post that reads as the post's title.
The German training data of Task 2 (claim normalization) of the CLEF 2025 CheckThat! Lab: 386 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 Thai training data of Task 2 (claim normalization) of the CLEF 2025 CheckThat! Lab: 244 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 Polish training data of Task 2 (claim normalization) of the CLEF 2025 CheckThat! Lab: 163 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 Wikipedia hoax and non-hoax articles of Kumar, West and Leskovec (WWW 2016): 76 wikitext sources of hoax articles and 64 of non-hoax articles, plus 64 and 64 versions rendered as Wikipedia pages. Upstream describes 64 hoax articles that were patrolled, survived at least a month and had at least 100 page views a day, and 64 matching non-hoax articles created the same day. Each row is one file, with its path in the archive and its full text; the label (hoax or non-hoax) is in the annotations table.
The Marathi training data of Task 2 (claim normalization) of the CLEF 2025 CheckThat! Lab: 137 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 Tamil training data of Task 2 (claim normalization) of the CLEF 2025 CheckThat! Lab: 102 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.
Twitter (X)
Weibo
Reddit
Wikipedia