---
title: "Computational Sarcasm Analysis on Social Media: A Systematic"
description: "The authors define sarcasm and its subtypes, separate it from irony, and catalogue datasets, which they group as short text… 165 references."
authors: ["Faria Binte Kader", "Nafisa Hossain Nujat", "Tasmia Binte Sogir", "Mohsinul Kabir", "Hasan Mahmud", "Kamrul Hasan"]
year: 2022
venue: "arXiv:2209.06170"
source: "https://arxiv.org/abs/2209.06170"
doi: "10.48550/arxiv.2209.06170"
arxiv: "2209.06170"
licence: "arXiv non-exclusive distribution licence (read only, no redistribution)"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/kader-2022-computational-sarcasm-analysis-social-media/"
---
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# Computational Sarcasm Analysis on Social Media: A Systematic Review

**Authors:** Faria Binte Kader, Nafisa Hossain Nujat, Tasmia Binte Sogir, Mohsinul Kabir, Hasan Mahmud and Kamrul Hasan · **Year:** 2022 · **Venue:** arXiv:2209.06170 · **Licence:** [arXiv non-exclusive distribution licence (read only, no redistribution)](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2209.06170) · [PDF](https://arxiv.org/pdf/2209.06170) · [DOI 10.48550/arxiv.2209.06170](https://doi.org/10.48550/arxiv.2209.06170) · [arXiv 2209.06170](https://arxiv.org/abs/2209.06170) · [OpenAlex](https://openalex.org/W4295884227)

**Citations:** 4 citations (OpenAlex, 2026-10-14)

## Summary
This survey maps research on automatically recognising sarcasm in English social media text, concentrating on work from 2016 onward. That cutoff picks up where an earlier broad review by Joshi et al. ended. Papers qualified only if they used social media data, included an English dataset, and contained text, either alone or within a multimodal setup. The authors define sarcasm and its subtypes, separate it from irony, and catalogue datasets, which they group as short text, long text or image and which come mostly from Twitter, with some from Reddit and Amazon. They also cover feature types and extraction methods, compare reported performance, and describe shared tasks. Their main observation is a move away from pattern, rule-based and statistical methods toward deep learning and transformers. Alongside this, they see more use of context such as conversation history and author information, and growing multimodal work. Remaining obstacles include informal platform language, the variable length of useful context, missing common-sense knowledge, and very little real-time detection. The tables are meant to help newcomers get oriented.

## Key points
- The Riloff et al. Twitter corpus, reused widely, has 3,200 tweets: 742 sarcastic and 2,458 non-sarcastic. Datasets from Khodak et al. and Cai et al. also serve as common benchmarks.
- The SemEval-2018 irony task gathered tweets through hashtags such as #irony, #sarcasm and #not. On that data, Dimovska et al. got their best irony result with a linear SVM using a hashing vectorizer over word n-grams.
- Studies disagree on how much conversational context helps. Avvaru et al. found the 5–7 most recent sentences worked best, while Jaiswal et al. preferred the last 3 utterances.
- Jamil et al. trained a CNN-LSTM on News Headlines (built from TheOnion and HuffPost) together with Twitter data. They validated it on News Headlines, Reddit and Sarcasm V2.
- Bharti et al. ran sarcasm detection on 1.4 million live tweets with Hadoop (Flume, Hive, HDFS, MapReduce), using three methods: PBLGA, IWS and PSWAP. Runtime fell from 11,609 s to 4,147 s, roughly 66% less.

## Interesting ideas
- Ivanko et al. model sarcasm as six slots: speaker, listener, context, utterance, the literal proposition, and the intended proposition, which makes the gap between what is said and what is meant explicit.
- Oprea et al. argue that sarcasm the author intended (distant labels from hashtags) and sarcasm readers perceive (manual labels) are separate phenomena, since annotators may miss what the author meant.
- Cheap, transparent cues such as a positive sentiment word paired with an antonym (PSWAP) or an utterance opening with an interjection (IWS) can flag sarcasm at scale.
- The survey reports that without common-sense knowledge, models tend to latch onto surface lexical cues instead of grasping why a statement is sarcastic.
- The best amount of preceding context differs between studies (3 versus 5–7 utterances), so the context window is a parameter to tune rather than a fixed constant.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (frames): The irony component of prag could produce this six-slot structure, giving each detected irony a readable pair of literal and intended propositions instead of a bare label.
- **I2** (learn): Silver irony data should record where each label came from, and author-signalled labels should be evaluated apart from human-perceived ones rather than merged.
- **I3** (frames): Such cues could become explainable rules inside prag that propose an irony reading, which deeper frame-level checks then confirm or reject.
- **I4** (world): Mova could flag likely irony when the literal proposition contradicts the state of the text's world or compiled knowledge, so the incongruity itself becomes the evidence.
- **I5** (learn): Evaluation could sweep the number of prior utterances given to the irony step and keep a context-free baseline as a negative control, showing how much context actually helps.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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- [Islamic University of Technology](https://md.research.elamur.ai/free/en/universities/islamic-university-of-technology/) — University, Dhaka, Bangladesh · 5 works · 511 citations

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🧭 Topics (2): [English](https://md.research.elamur.ai/free/en/topics/english/) · 1,373 works · [😊 Sentiment & emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) · 608 works

## Cite

Cite as: Faria Binte Kader, Nafisa Hossain Nujat, Tasmia Binte Sogir, Mohsinul Kabir, Hasan Mahmud and Kamrul Hasan. 2022. Computational Sarcasm Analysis on Social Media: A Systematic Review. arXiv:2209.06170.

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

```bibtex
@misc{kader2022computational,
  title = {{Computational Sarcasm Analysis on Social Media: A Systematic Review}},
  author = {Faria Binte Kader, Nafisa Hossain Nujat, Tasmia Binte Sogir, Mohsinul Kabir, Hasan Mahmud and Kamrul Hasan},
  year = {2022},
  doi = {10.48550/arxiv.2209.06170},
  eprint = {2209.06170},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2209.06170}
}
```

- [← previous Comparing Open Arabic Named Entity Recognition Tools](https://md.research.elamur.ai/free/en/papers/aldumaykhi-2022-comparing-open-arabic-named-entity/)
- [next → GetSmartMSEC at SemEval-2022 Task 6: Sarcasm Detection using Contextual Word Embedding with Gaussian model for Irony Type Identification](https://md.research.elamur.ai/free/en/papers/krishnan-2022-getsmartmsec-semeval-2022-task-6/)
- [↑ back to papers of 2022](https://md.research.elamur.ai/free/en/papers/2022/page/6/)

## References

165 references, 73 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 24 October 2026*