{"url":"/dataset/isarcasm","name":"iSarcasm","full_name":"iSarcasm","description_markdown":"iSarcasm is a dataset of tweets, each labelled as either sarcastic or non_sarcastic. Each sarcastic tweet is further labelled for one of the following types of ironic speech:\r\n\r\n- sarcasm: tweets that contradict the state of affairs and are critical towards an addressee;\r\n- irony: tweets that contradict the state of affairs but are not obviously critical towards an addressee;\r\n- satire: tweets that appear to support an addressee, but contain underlying disagreement and mocking;\r\n- understatement: tweets that undermine the importance of the state of affairs they refer to;\r\n- overstatement: tweets that describe the state of affairs in obviously exaggerated terms;\r\n- rhetorical question: tweets that include a question whose invited inference (implicature) is obviously contradicting the state of affairs.\r\n\r\nFor each sarastic tweet, there's also:\r\n\r\n- an explanation, in English sentences, as to why it is sarcastic, and\r\n- a rephrase that conveys the same meaning non-sarcastically. Both have been provided by the author of the tweet.\r\n\r\niSarcasm contains 4,484 tweets, out of which 777 are labelled as sarcastic and 3,707 as non-sarcastic. You'll find two files, isarcasm_train.csv and isarcasm_test.csv, each containing 80% and 20% of the examples chosen at random, respectively. Each line in a file has the format tweet_id,sarcasm_label,sarcasm_type, where sarcasm_type are only defined for sarcastic tweets, as specified above.\r\n\r\nSource: [iSarcasm](https://github.com/silviu-oprea/iSarcasm)","description_withheld":null,"homepage":"https://github.com/silviu-oprea/iSarcasm","introduced_date":"2019-11-08","introduced_date_note":null,"introduced_by":{"paper":"/paper/isarcasm-a-dataset-of-intended-sarcasm","title":"iSarcasm: A Dataset of Intended Sarcasm","first_author":"Silviu Oprea","url":null},"license":null,"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Sentiment Analysis","url":"/task/sentiment-analysis","datasets_with_task":"/datasets/task/sentiment-analysis"},{"name":"Sarcasm Detection","url":"/task/sarcasm-detection","datasets_with_task":"/datasets/task/sarcasm-detection"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["iSarcasm"],"data_loaders":[{"repo":"https://github.com/silviu-oprea/isarcasm","url":"https://github.com/silviu-oprea/iSarcasm","frameworks":[]}],"num_papers_in_archive":17,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/sarcasm-detection-on-isarcasm","task":"Sarcasm Detection","dataset_variant":"iSarcasm","rows":1,"metrics":["F1-Score"],"first_row_in_archive_order":{"model":"RoBERTa + Mutation Data Augmentation","paper":"/paper/utnlp-at-semeval-2022-task-6-a-comparative","metrics":{"F1-Score":"0.414"},"code_links":[{"title":"amirabaskohi/semeval2022-task6-sarcasm-detection","url":"https://github.com/amirabaskohi/semeval2022-task6-sarcasm-detection"},{"title":"priyank96/dataset-pruning-sarcasm-detection","url":"https://github.com/priyank96/dataset-pruning-sarcasm-detection"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/utnlp-at-semeval-2022-task-6-a-comparative","title":"UTNLP at SemEval-2022 Task 6: A Comparative Analysis of Sarcasm Detection Using Generative-based and Mutation-based Data Augmentation","date":"2022-04-18","rows_on_this_dataset":1,"code_links":2,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}