--- title: "The Role of Conversation Context for Sarcasm — Ghosh 2017" description: "Most sarcasm detectors judge a message on its own, but a speaker's ironic intent often becomes clear only from what came before." authors: ["Debanjan Ghosh", "Alexander Richard Fabbri", "Smaranda Muresan"] year: 2017 venue: "Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue" source: "https://aclanthology.org/W17-5523/" doi: "10.18653/v1/w17-5523" arxiv: "1707.06226" 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/ghosh-2017-role-conversation-context-sarcasm-detection/" --- [Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/ghosh-2017-role-conversation-context-sarcasm-detection/) [home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2017](https://md.research.elamur.ai/free/papers/2017/page/3/) / The Role of Conversation Context for Sarcasm Detection in Online Interactions # The Role of Conversation Context for Sarcasm Detection in Online Interactions **Authors:** [Debanjan Ghosh](https://md.research.elamur.ai/free/authors/debanjan-ghosh/), Alexander Richard Fabbri, [Smaranda Muresan](https://md.research.elamur.ai/free/authors/smaranda-muresan/) · **Year:** 2017 · **Venue:** Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/W17-5523/) · [PDF](https://aclanthology.org/W17-5523.pdf) · [DOI 10.18653/v1/w17-5523](https://doi.org/10.18653/v1/w17-5523) · [arXiv 1707.06226](https://arxiv.org/abs/1707.06226) · [OpenAlex](https://openalex.org/W2737498144) **Citations:** 74 citations (openalex-snapshot · 2026-10-14) · [25](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary Most sarcasm detectors judge a message on its own, but a speaker's ironic intent often becomes clear only from what came before. This paper asks two questions. First, does modeling the preceding turn improve sarcasm classification? Second, can we find which part of that context provoked the sarcastic reply? The authors use two sources: - discussion-forum replies from the Sarcasm Corpus V2, labeled by crowd workers; - Twitter conversations, labeled by the authors' own hashtags such as #sarcasm. They compare classification on the reply alone with classification on context plus reply. The baseline is an SVM with n-gram, lexicon and sarcasm-marker features, set against several LSTM variants. The conditional LSTM and LSTMs with sentence-level attention over context and reply beat an LSTM that reads only the reply. The authors also compare attention weights with crowd workers' judgments. The models tend to focus on context sentences that are semantically coherent or incongruous with the reply, and on explicit markers such as emoticons. Earlier Twitter studies had found little gain from conversation context, so this result shows a clearer benefit. Data and code are publicly released. ## Key points - **Forum data:** drawn from a portion of the Internet Argument Corpus known as the Sarcasm Corpus V2. Of its 9,400 posts, only 4,692 (balanced between classes) are publicly available. - **Twitter data:** over 200K tweets were collected, and only about 13% were replies to another tweet. The final set has 25,991 context–reply instances (12,215 sarcastic, 13,776 non-sarcastic), and 30% have more than one tweet of context. - **Baseline (SVM bl):** a linear-kernel libSVM model using BoW n-grams, LIWC, MPQA and Opinion Lexicon sentiment features, and sarcasm indicators from Burgers et al. (2012). - **Best models:** the conditional LSTM (Rocktäschel et al., 2015) and attention LSTMs at sentence level. Sentence-level attention was chosen for interpretability and because the forum corpus is fairly small. - **Attention analysis:** compared against Turkers' selections of triggering sentences. The authors caution that attention-based interpretations should be treated carefully. *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 25 papers of our library cite it (of the 140673 whose bibliographies we read); 12 of them have a page here, 13 not yet. - [Detecting Sarcasm in Conversation Context Using Transformer-Based Models](https://md.research.elamur.ai/free/papers/avvaru-2020-detecting-sarcasm-conversation-context-transformer/) - [Affective and Contextual Embedding for Sarcasm Detection](https://md.research.elamur.ai/free/papers/babanejad-2020-affective-contextual-embedding-sarcasm-detection/) - [Context-Aware Sarcasm Detection Using BERT](https://md.research.elamur.ai/free/papers/baruah-2020-context-aware-sarcasm-detection-bert/) - [R^3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge](https://md.research.elamur.ai/free/papers/chakrabarty-2020-r-textasciicircum3-reverse-retrieve-rank/) - [Transformer-based Context-aware Sarcasm Detection in Conversation Threads from Social Media](https://md.research.elamur.ai/free/papers/dong-2020-transformer-based-context-aware-sarcasm/) - [Sarcasm Analysis Using Conversation Context](https://md.research.elamur.ai/free/papers/ghosh-2018-sarcasm-analysis-conversation-context/) - [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/) - [Neural Sarcasm Detection using Conversation Context](https://md.research.elamur.ai/free/papers/jaiswal-2020-neural-sarcasm-detection-conversation-context/) - [Multi-source Semantic Graph-based Multimodal Sarcasm Explanation Generation](https://md.research.elamur.ai/free/papers/jing-2023-multi-source-semantic-graph-based/) - [Comparative Study of Multilingual Idioms and Similes in Large Language Models](https://md.research.elamur.ai/free/papers/khoshtab-2025-comparative-study-multilingual-idioms-similes/) - [When did you become so smart, oh wise one?! Sarcasm Explanation in Multi-modal Multi-party Dialogues](https://md.research.elamur.ai/free/papers/kumar-2022-when-did-you-become-so/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{ghosh2017role, title = {{The Role of Conversation Context for Sarcasm Detection in Online Interactions}}, author = {Ghosh, Debanjan and Fabbri, Alexander Richard and Muresan, Smaranda}, booktitle = {Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue}, year = {2017}, doi = {10.18653/v1/w17-5523}, eprint = {1707.06226}, archivePrefix = {arXiv}, url = {https://doi.org/10.18653/v1/w17-5523} } ``` - [← previous The Pragmatics of Indirect Commands in Collaborative Discourse](https://md.research.elamur.ai/free/papers/lamm-2017-pragmatics-indirect-commands-collaborative-discourse/) - [next → The University of Edinburgh's Neural MT Systems for WMT17](https://md.research.elamur.ai/free/papers/sennrich-2017-university-edinburgh-s-neural-mt/) - [↑ back to papers of 2017](https://md.research.elamur.ai/free/papers/2017/page/3/) ## References 2 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Salvatore Attardo. 2000. Irony markers and functions: Towards a goal-oriented theory of irony and its processing. Rask 12(1):3–20. [find](https://scholar.google.com/scholar?q=Salvatore+Attardo%2E+2000%2E+Irony+markers+and+functions%3A+Towards+a+goal%2Doriented+theory+of+irony+and+its+processing%2E+Rask+12%281%29%3A3%E2%80%9320%2E) 2. David Bamman and Noah A Smith. 2015. Contextualized sarcasm detection on twitter. In Ninth International AAAI Conference on Web and Social Media. [find](https://scholar.google.com/scholar?q=David+Bamman+and+Noah+A+Smith%2E+2015%2E+Contextualized+sarcasm+detection+on+twitter%2E+In+Ninth+International+AAAI+Conference+on+Web+and+Social+Media%2E)