--- title: "Exploring Author Context for Detecting Intended — Oprea 2019" description: "The paper asks whether a tweet author's posting history helps decide if that tweet is sarcastic, and whether the answer depends on how the sarcasm labels…" authors: ["Silviu Oprea", "Walid Magdy"] year: 2019 venue: "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics" source: "https://aclanthology.org/P19-1275/" doi: "10.18653/v1/p19-1275" arxiv: "1910.11932" 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/oprea-2019-exploring-author-context-detecting-intended/" --- [Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/oprea-2019-exploring-author-context-detecting-intended/) [home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2019](https://md.research.elamur.ai/free/papers/2019/page/2/) / Exploring Author Context for Detecting Intended vs Perceived Sarcasm # Exploring Author Context for Detecting Intended vs Perceived Sarcasm **Authors:** [Silviu Oprea](https://md.research.elamur.ai/free/authors/silviu-oprea/), [Walid Magdy](https://md.research.elamur.ai/free/authors/walid-magdy/) · **Year:** 2019 · **Venue:** Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/P19-1275/) · [PDF](https://aclanthology.org/P19-1275.pdf) · [DOI 10.18653/v1/p19-1275](https://doi.org/10.18653/v1/p19-1275) · [arXiv 1910.11932](https://arxiv.org/abs/1910.11932) · [OpenAlex](https://openalex.org/W2948894916) **Citations:** 17 citations (openalex-snapshot · 2026-10-14) · [30](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary The paper asks whether a tweet author's posting history helps decide if that tweet is sarcastic, and whether the answer depends on how the sarcasm labels were produced. The authors represent each user as an embedding built from tweets posted before the target tweet. They use four embedding models: CASCADE, a recency-weighted variant they propose (W-CASCADE), an encoder-decoder model (ED) and a SUMMARY model. These embeddings are used either alone ("exclusive" models) or concatenated with text features from the SIARN baseline ("inclusive" models). Experiments cover two Twitter corpora: Riloff, annotated by humans, and Ptacek, labelled through sarcasm hashtags. On Ptacek, user history alone outperforms the text-only state of the art, and combined models set a new best score. On Riloff, history-only models perform poorly and the gains from adding history are marginal. The authors argue that hashtag labels capture sarcasm the author intends, while manual labels capture sarcasm readers perceive. They conclude that detection research should treat these as distinct phenomena. ## Key points - Only part of each dataset could be re-collected with user timelines: 701 of 3,200 Riloff tweets and 27,177 of 50,000 Ptacek tweets. Data splits were stratified by user, so no user's embedding appears in more than one split. - On Ptacek, EX-W-CASCADE uses no tweet content yet reaches F1 0.922, versus 0.863 for SIARN. IN-W-CASCADE gives the best result at 0.934. - On Riloff, the best model is IN-ED with F1 0.739, versus 0.711 for SIARN. Exclusive models stay at or below 0.546. - Hashtags and manual labels disagree often in Riloff: 217 of the 509 tweets annotated as non-sarcastic contain a sarcasm tag. - The authors re-labelled Riloff by hashtags to create #Riloff. On #Riloff, exclusive models reach up to 0.827 F1, which supports the intended-vs-perceived distinction. *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 30 papers of our library cite it (of the 140673 whose bibliographies we read); 10 of them have a page here, 20 not yet. - [From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset](https://md.research.elamur.ai/free/papers/farha-2020-arabic-sentiment-analysis-sarcasm-detection/) - [Sarcasm Detection is Way Too Easy! An Empirical Comparison of Human and Machine Sarcasm Detection](https://md.research.elamur.ai/free/papers/farha-2022-sarcasm-detection-is-way-too/) - [SemEval-2022 Task 6: iSarcasmEval, Intended Sarcasm Detection in English and Arabic](https://md.research.elamur.ai/free/papers/farha-2022-semeval-2022-task-6-isarcasmeval/) - [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/) - [A Report on the 2020 Sarcasm Detection Shared Task](https://md.research.elamur.ai/free/papers/ghosh-2020-report-2020-sarcasm-detection-shared/) - [Generalizable Sarcasm Detection is Just Around the Corner, of Course!](https://md.research.elamur.ai/free/papers/jang-2024-generalizable-sarcasm-detection-is-just/) - [iSarcasm: A Dataset of Intended Sarcasm](https://md.research.elamur.ai/free/papers/oprea-2020-isarcasm-dataset-intended-sarcasm/) - [Chandler: An Explainable Sarcastic Response Generator](https://md.research.elamur.ai/free/papers/oprea-2021-chandler-explainable-sarcastic-response-generator/) - [Reactive Supervision: A New Method for Collecting Sarcasm Data](https://md.research.elamur.ai/free/papers/shmueli-2020-reactive-supervision-new-method-collecting/) - [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{oprea2019exploring, title = {{Exploring Author Context for Detecting Intended vs Perceived Sarcasm}}, author = {Oprea, Silviu and Magdy, Walid}, booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics}, year = {2019}, doi = {10.18653/v1/p19-1275}, eprint = {1910.11932}, archivePrefix = {arXiv}, url = {https://doi.org/10.18653/v1/p19-1275} } ``` - [← previous Explain Yourself! Leveraging Language Models for Commonsense Reasoning](https://md.research.elamur.ai/free/papers/rajani-2019-explain-yourself-leveraging-language-models/) - [next → Facebook FAIR's WMT19 News Translation Task Submission](https://md.research.elamur.ai/free/papers/ng-2019-facebook-fair-s-wmt19-news/) - [↑ back to papers of 2019](https://md.research.elamur.ai/free/papers/2019/page/2/) ## References 26 references, 9 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Amir, S.; Wallace, B. C.; Lyu, H.; Carvalho, P.; and Silva, M. J. 2016. Modelling context with user embeddings for sarcasm detection in social media. In CoNLL, 167–177. ACL. 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