---
title: "CASCADE: Contextual Sarcasm Detection in — Hazarika 2018"
description: "Sarcasm detection has mostly relied on surface signals in the text, such as interjections, punctuation or sentiment contrasts."
authors: ["Devamanyu Hazarika", "Soujanya Poria", "Sruthi Gorantla", "Erik Cambria", "Roger Zimmermann", "Rada Mihalcea"]
year: 2018
venue: "Proceedings of the 27th International Conference on Computational Linguistics"
source: "https://aclanthology.org/C18-1156/"
arxiv: "1805.06413"
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/en/papers/hazarika-2018-cascade-contextual-sarcasm-detection-online/"
---
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# CASCADE: Contextual Sarcasm Detection in Online Discussion Forums

**Authors:** [Devamanyu Hazarika](https://md.research.elamur.ai/free/en/authors/devamanyu-hazarika/), [Soujanya Poria](https://md.research.elamur.ai/free/en/authors/soujanya-poria/), Sruthi Gorantla, [Erik Cambria](https://md.research.elamur.ai/free/en/authors/erik-cambria/), [Roger Zimmermann](https://md.research.elamur.ai/free/en/authors/roger-zimmermann/), [Rada Mihalcea](https://md.research.elamur.ai/free/en/authors/rada-mihalcea/) · **Year:** 2018 · **Venue:** Proceedings of the 27th International Conference on Computational Linguistics · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/C18-1156/) · [PDF](https://aclanthology.org/C18-1156.pdf) · [arXiv 1805.06413](https://arxiv.org/abs/1805.06413) · [OpenAlex](https://openalex.org/W2803424915)

**Citations:** 38 citations (openalex-snapshot · 2026-10-14) · [51](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Sarcasm detection has mostly relied on surface signals in the text, such as interjections, punctuation or sentiment contrasts. Much sarcasm in online forums carries no such markers and makes sense only with knowledge of the speaker and the surrounding conversation. The authors present CASCADE, a hybrid classifier for Reddit comments that combines three representations. The first is a CNN encoding of the comment itself. The second is a user embedding built from each author's posting history. The third is a discourse vector describing the subreddit where the comment appears. For the user embedding, ParagraphVector captures writing style, a personality-oriented CNN captures character traits, and canonical correlation analysis (CCA) merges the two views. The discourse vector comes from document-level modeling of all comments in a forum. On a large Reddit corpus, tested in balanced, imbalanced and politics-only (Pol) settings, CASCADE beats strong baselines, including the earlier best system, CUE-CNN. The work shows that modeling both the user and the thread is key to recognizing sarcasm that depends on context.

## Key points
- With the content CNN alone, accuracy is roughly 10 points below the full model. Adding user embeddings brings the biggest jump, about 8–12 points in accuracy and F1.
- The full CASCADE model scores Acc/F1 of 0.78/0.77 on Main balanced, 0.79/0.86 on Main imbalanced and 0.74/0.75 on Pol.
- Discourse features matter most on the Pol subset, where they add about 3 points of F1.
- Replacing CCA with plain concatenation of the user features clearly lowers performance. Dropping the personality features brings results down to roughly CUE-CNN's level.
- A t-SNE plot of the user embeddings shows sarcastic users spread more widely than non-sarcastic ones (variance 10.92 vs 5.20). Errors cluster around users with few past posts and around very long context comments.

*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/en/shelves/linguistics/)

Topics: [Discourse analysis](https://md.research.elamur.ai/free/en/topics/discourse-analysis/)

## Cited by

Cited by

51 papers of our library cite it (of the 140673 whose bibliographies we read); 16 of them have a page here, 35 not yet.

- [Affective and Contextual Embedding for Sarcasm Detection](https://md.research.elamur.ai/free/en/papers/babanejad-2020-affective-contextual-embedding-sarcasm-detection/)
- [Context-Aware Sarcasm Detection Using BERT](https://md.research.elamur.ai/free/en/papers/baruah-2020-context-aware-sarcasm-detection-bert/)
- [Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)](https://md.research.elamur.ai/free/en/papers/castro-2019-towards-multimodal-sarcasm-detection-obviously/)
- [“When Numbers Matter!”: Detecting Sarcasm in Numerical Portions of Text](https://md.research.elamur.ai/free/en/papers/dubey-2019-when-numbers-matter-detecting-sarcasm/)
- [Sarcasm Detection is Way Too Easy! An Empirical Comparison of Human and Machine Sarcasm Detection](https://md.research.elamur.ai/free/en/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/en/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/en/papers/ghosh-2020-interpreting-verbal-irony-linguistic-strategies/)
- [A Report on the 2020 Sarcasm Detection Shared Task](https://md.research.elamur.ai/free/en/papers/ghosh-2020-report-2020-sarcasm-detection-shared/)
- [Representing Social Media Users for Sarcasm Detection](https://md.research.elamur.ai/free/en/papers/kolchinski-2018-representing-social-media-users-sarcasm/)
- [A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict](https://md.research.elamur.ai/free/en/papers/liu-2022-dual-channel-framework-sarcasm-recognition/)
- [Prompt Tuning with Contradictory Intentions for Sarcasm Recognition](https://md.research.elamur.ai/free/en/papers/liu-2023-prompt-tuning-contradictory-intentions-sarcasm/)
- [Exploring Author Context for Detecting Intended vs Perceived Sarcasm](https://md.research.elamur.ai/free/en/papers/oprea-2019-exploring-author-context-detecting-intended/)
- [iSarcasm: A Dataset of Intended Sarcasm](https://md.research.elamur.ai/free/en/papers/oprea-2020-isarcasm-dataset-intended-sarcasm/)
- [Chandler: An Explainable Sarcastic Response Generator](https://md.research.elamur.ai/free/en/papers/oprea-2021-chandler-explainable-sarcastic-response-generator/)
- [A deep-learning framework to detect sarcasm targets](https://md.research.elamur.ai/free/en/papers/patro-2019-deep-learning-framework-detect-sarcasm/)
- [A Transformer-based approach to Irony and Sarcasm detection](https://md.research.elamur.ai/free/en/papers/potamias-2019-transformer-based-approach-irony-sarcasm/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{hazarika2018cascade,
  title = {{CASCADE: Contextual Sarcasm Detection in Online Discussion Forums}},
  author = {Hazarika, Devamanyu and Poria, Soujanya and Gorantla, Sruthi and Cambria, Erik and Zimmermann, Roger and Mihalcea, Rada},
  booktitle = {Proceedings of the 27th International Conference on Computational Linguistics},
  year = {2018},
  eprint = {1805.06413},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/1805.06413}
}
```

- [← previous CARER: Contextualized Affect Representations for Emotion Recognition](https://md.research.elamur.ai/free/en/papers/saravia-2018-carer-contextualized-affect-representations-emotion/)
- [next → Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation](https://md.research.elamur.ai/free/en/papers/poliak-2018-collecting-diverse-natural-language-inference/)
- [↑ back to papers of 2018](https://md.research.elamur.ai/free/en/papers/2018/)

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