A snapshot of Truth Social from February to September 2022, released by Patrick Gerard, Nicholas Botzer and Tim Weninger (University of Notre Dame) on Zenodo (7522646). 454,458 users and 845,060 posts ('truths'), with 4,002,115 follow edges, 506,276 replies and 10,508 quotes, plus hashtags, media and external URLs, as 13 tab-separated tables. Suitable for social network analysis, political communication and misinformation research on an alternative platform.
A twitter dataset of 957,956 tweet IDs from the #MeToo movement (Q3 2020), collected by Maiorana, Morales Henry, and Weintraub via Social Feed Manager. Each record contains tweet IDs only — full content requires hydration. Quarterly snapshot of #MeToo discourse enabling longitudinal analysis when combined with other quarterly collections. Suitable for social movement analysis, gender studies, harassment detection, discourse trends.
A twitter dataset of 690,281 tweet IDs from state health agencies, CDC, and FDA (2012–2022), collected by Mendez via academictwitteR (Harvard Dataverse, 2023). Each record contains tweet IDs, account state/agency labels — full content requires hydration. Longitudinal institutional public health Twitter archive spanning pre-pandemic baseline through COVID-19 response. Suitable for public health communication, pandemic response analysis, institutional messaging.
A Twitter dataset of 128,484 tweets with political leaning labels, collected by Chatelet via Hugging Face. Each record contains text, political label (liberal, conservative, neutral). 100K tweets labeled for political ideology with balanced classes. Suitable for political NLP, ideology classification, discourse analysis.
Synthetic, templated data: 75,000 short texts labelled with one of 6 harassment categories or non-toxic (10,714 or 10,715 each), with a 'platform' value (Facebook, Instagram, Reddit, news portal) and character offsets of the toxic span. The texts are generated from a small set of templates: the toxic rows use only 30 distinct toxic phrases, and every row ends in a '(Ref Node: #NNNNNN)' token. The platform values are assigned, not observed. Published on Mendeley Data. Suitable for testing span-tagging code, not for measuring real cyberbullying.
A Twitter dataset of 11,929 labeled financial tweets, collected by zeroshot via Twitter API. Each record contains tweet text, sentiment label (positive/negative/neutral), sentiment value (0/1/2), train/valid split. A labeled dataset of tweets about financial news, with sentiment annotations for stock market-related content. Suitable for sentiment analysis, financial NLP, stock market prediction, social media analytics, natural language processing.
A News Articles (multiple sources) dataset of 9,900 news articles (5,000 fake, 4,900 real), collected by Vishakh Dapat via Kaggle (2023). Each record contains full article text and binary veracity label (Fake or Real). The dataset is notable for its balanced binary-labeled news corpus with approximately equal proportions of fake and real articles, making it well-suited as a classification benchmark. Intended for research into fake news detection, text classification benchmarking, misinformation topic modeling, and NLP feature engineering.
A LinkedIn dataset of 3,600 profiles: 1,800 legitimate, 600 fake and 1,200 generated by ChatGPT (600 from legitimate profiles' statistics, 600 from fake ones'), released by Ayoobi, Shahriar and Mukherjee (ACM Hypertext 2023). Each record holds the profile sections visible before connecting: intro, about, experience, education, skills, languages, interests, licences and more, with counts per section. Suitable for fake and LLM-generated profile detection.
A Cross-platform meme corpus (4chan, Twitter, Weibo) dataset of 1,318 hate-speech labeled meme records from 3 platforms, collected by Mira AI Lab via cross-platform meme collection. Each record contains image filename, OCR-extracted image text, post caption text, hate speech label, category list (politics/race/religion/etc.), and human-readable annotation reason per record. M3-Hate is the hate-speech filtered subset of the M3 meme dataset, with 1,318 hateful memes across 4chan (1062), Twitter (132), and Weibo (124) — multimodal with both image OCR text and post captions. Suitable for hate speech detection, multimodal meme analysis, cross-platform content moderation, image-text classification, harmful content research. The package includes the 1,318 meme images (img/) alongside the JSON records.
Truth Social
Twitter (X)
Facebook
Instagram
Reddit
LinkedIn
4chan
Weibo