--- title: "Exploring the Impact of Pragmatic Phenomena on — Karoui 2017" description: "Irony detectors for Twitter mostly rely on cues inside the message, but much irony depends on knowledge the reader brings from outside." authors: ["Jihen Karoui", "Farah Benamara", "Véronique Moriceau", "Viviana Patti", "Cristina Bosco", "Nathalie Aussenac-Gilles"] year: 2017 venue: "Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers" source: "https://aclanthology.org/E17-1025/" licence: "CC BY 4.0 (ACL Anthology)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/papers/karoui-2017-exploring-impact-pragmatic-phenomena-irony/" --- [Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/karoui-2017-exploring-impact-pragmatic-phenomena-irony/) [home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2017](https://md.research.elamur.ai/free/papers/2017/page/2/) / Exploring the Impact of Pragmatic Phenomena on Irony Detection in Tweets: A Multilingual Corpus Study # Exploring the Impact of Pragmatic Phenomena on Irony Detection in Tweets: A Multilingual Corpus Study **Authors:** [Jihen Karoui](https://md.research.elamur.ai/free/authors/jihen-karoui/), [Farah Benamara](https://md.research.elamur.ai/free/authors/farah-benamara/), [Véronique Moriceau](https://md.research.elamur.ai/free/authors/veronique-moriceau/), [Viviana Patti](https://md.research.elamur.ai/free/authors/viviana-patti/), [Cristina Bosco](https://md.research.elamur.ai/free/authors/cristina-bosco/), [Nathalie Aussenac-Gilles](https://md.research.elamur.ai/free/authors/nathalie-aussenac-gilles/) · **Year:** 2017 · **Venue:** Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/E17-1025/) · [PDF](https://aclanthology.org/E17-1025.pdf) · [OpenAlex](https://openalex.org/W2606882412) **Citations:** 41 citations (openalex-snapshot · 2026-10-14) · [28](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary Irony detectors for Twitter mostly rely on cues inside the message, but much irony depends on knowledge the reader brings from outside. This paper asks four questions. Do the irony categories that linguists described for literary texts also appear in tweets? Do they carry surface markers? How do categories and markers co-occur? Do French, English and Italian favour different categories? The authors propose a three-layer annotation scheme. The first layer records how irony is triggered: explicitly, through a contradiction between two lexicalized propositions, or implicitly, through a clash with an unstated pragmatic context. The second layer records the irony category, and the third records the linguistic markers present. They apply the scheme to new French and English tweet collections on nine media topics and to existing Italian data, then analyse the results qualitatively and quantitatively. The main finding is that implicitly triggered irony, which needs external knowledge, is the hardest case for future systems. The released corpus is a fine-grained multilingual resource and has already supported irony detection shared tasks. ## Key points - **Corpora:** - French (F): 2,073 ironic vs. 16,179 non-ironic tweets. - English (E): 5,173 ironic vs. 6,116 non-ironic tweets. - Italian (I): 806 ironic tweets from Sentipolc plus 2,273 from TW-SPINO, with 5,642 non-ironic Sentipolc tweets. - The corpus is freely available at github.com/IronyAndTweets. - **Data collection:** French and English tweets were gathered through the Twitter API using keywords for nine topics, such as politics, sport and the Arab Spring. Ironic examples were identified by hashtags like #irony and #sarcasm. Italian needed existing annotated data because Italian users rarely use stable irony hashtags. - **Scheme layers:** - Irony activation (EXPLICIT vs. IMPLICIT). - Irony categories adapted from the literature, such as analogy, hyperbole/exaggeration, euphemism, rhetorical question, oxymoron, false assertion and context shift. - Markers, such as negation, punctuation, intensifiers, URLs and named entities. - **Pilot study:** The literary categories were first checked on a pilot sample of 150 ironic tweets. - **Shared tasks:** The French data was used for DEFT@TALN2017 and the Italian data for SENTIPOLC@Evalita (2014 and 2016). In the 2016 edition, 13 teams submitted 57 runs. Irony detection proved difficult, partly because the data is unbalanced: only about 1 in 7 training tweets is ironic. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/) ## Cited by Cited by 28 papers of our library cite it (of the 140673 whose bibliographies we read); 9 of them have a page here, 19 not yet. - [DAICT: A Dialectal Arabic Irony Corpus Extracted from Twitter](https://md.research.elamur.ai/free/papers/abbes-2020-daict-dialectal-arabic-irony-corpus/) - [Application and Analysis of a Multi-layered Scheme for Irony on the Italian Twitter Corpus TWITTIRÒ](https://md.research.elamur.ai/free/papers/cignarella-2018-application-analysis-multi-layered-scheme/) - [EPIC: Multi-Perspective Annotation of a Corpus of Irony](https://md.research.elamur.ai/free/papers/frenda-2023-epic-multi-perspective-annotation-corpus/) - [Interpreting Verbal Irony: Linguistic Strategies and the Connection to the Type of Semantic Incongruity](https://md.research.elamur.ai/free/papers/ghosh-2020-interpreting-verbal-irony-linguistic-strategies/) - [We Usually Don't Like Going to the Dentist: Using Common Sense to Detect Irony on Twitter](https://md.research.elamur.ai/free/papers/hee-2018-we-usually-don-t-like/) - [Irony Detection in Hebrew Documents: A Novel Dataset and an Evaluation of Neural Classification Methods](https://md.research.elamur.ai/free/papers/prag-2025-shmidman-irony-detection-hebrew-documents-novel/) - [Modeling Cross-Cultural Pragmatic Inference with Codenames Duet](https://md.research.elamur.ai/free/papers/shaikh-2023-modeling-cross-cultural-pragmatic-inference/) - [Cross-Cultural Similarity Features for Cross-Lingual Transfer Learning of Pragmatically Motivated Tasks](https://md.research.elamur.ai/free/papers/sun-2021-cross-cultural-similarity-features-cross/) - [A Survey in Automatic Irony Processing: Linguistic, Cognitive, and Multi-X Perspectives](https://md.research.elamur.ai/free/papers/zeng-2022-survey-automatic-irony-processing-linguistic/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{karoui2017exploring, title = {{Exploring the Impact of Pragmatic Phenomena on Irony Detection in Tweets: A Multilingual Corpus Study}}, author = {Karoui, Jihen and Benamara, Farah and Moriceau, Véronique and Patti, Viviana and Bosco, Cristina and Aussenac-Gilles, Nathalie}, booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers}, year = {2017}, doi = {10.18653/v1/e17-1025}, url = {https://doi.org/10.18653/v1/e17-1025} } ``` - [← previous Exploring Neural Text Simplification Models](https://md.research.elamur.ai/free/papers/nisioi-2017-exploring-neural-text-simplification-models/) - [next → FiLM: Visual Reasoning with a General Conditioning Layer](https://md.research.elamur.ai/free/papers/perez-2017-film-visual-reasoning-general-conditioning/) - [↑ back to papers of 2017](https://md.research.elamur.ai/free/papers/2017/page/2/) ## References 5 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. David Bamman and Noah A. Smith. 2015. Contextualized sarcasm detection on Twitter. In Proceedings of the International Conference on Web and Social Media, ICWSM 2015, pages 574–577. [find](https://scholar.google.com/scholar?q=David+Bamman+and+Noah+A%2E+Smith%2E+2015%2E+Contextualized+sarcasm+detection+on+Twitter%2E+In+Proceedings+of+the+International+Conference+on+Web+and+Social+Media%2C+ICWSM+2015%2C+pages+574%E2%80%93577%2E) 2. Katharina Barbe. 1995. Irony in context, volume 34. John Benjamins Publishing. [find](https://scholar.google.com/scholar?q=Katharina+Barbe%2E+1995%2E+Irony+in+context%2C+volume+34%2E+John+Benjamins+Publishing%2E) 3. Francesco Barbieri and Horacio Saggion. 2014. Modelling Irony in Twitter: Feature Analysis and Evaluation. In Proceedings of Language Resources and Evaluation Conference (LREC), pages 4258–4264. [find](https://scholar.google.com/scholar?q=Francesco+Barbieri+and+Horacio+Saggion%2E+2014%2E+Modelling+Irony+in+Twitter%3A+Feature+Analysis+and+Evaluation%2E+In+Proceedings+of+Language+Resources+and+Evaluation+Conference+%28LREC%29%2C+pages+4258%E2%80%934264%2E) [our card](https://md.research.elamur.ai/free/papers/barbieri-2014-modelling-irony-twitter-feature-analysis/) 4. Francesco Barbieri, Valerio Basile, Danilo Croce, Malvina Nissim, Nicole Novielli, and Viviana Patti. 2016. Overview of the Evalita 2016 SENTIment POLarity Classification Task. In Proceedings of Third Italian Conference on Computational Linguistics (CLiC-it 2016) & Fifth Evaluation Campaign of Natur… [find](https://scholar.google.com/scholar?q=Francesco+Barbieri%2C+Valerio+Basile%2C+Danilo+Croce%2C+Malvina+Nissim%2C+Nicole+Novielli%2C+and+Viviana+Patti%2E+2016%2E+Overview+of+the+Evalita+2016+SENTIment+POLarity+Classification+Task%2E+In+Proceedings+of+Third) 5. Final Workshop (EVALITA 2016), Napoli, Italy, December 5-7, 2016., volume 1749 of CEUR Workshop Proceedings. CEUR-WS.org. [find](https://scholar.google.com/scholar?q=Final+Workshop+%28EVALITA+2016%29%2C+Napoli%2C+Italy%2C+December+5%2D7%2C+2016%2E%2C+volume+1749+of+CEUR+Workshop+Proceedings%2E+CEUR%2DWS%2Eorg%2E)