---
title: "The perfect solution for detecting sarcasm — Liebrecht 2013"
description: "Sarcasm can invert the polarity of a message, often by using positive or exaggerated wording to express a negative view, which undermines sentiment…"
authors: ["Christine Liebrecht", "Florian Kunneman", "Antal van den Bosch"]
year: 2013
venue: "Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis"
source: "https://aclanthology.org/W13-1605/"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/liebrecht-2013-perfect-solution-detecting-sarcasm-tweets/"
---
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# The perfect solution for detecting sarcasm in tweets #not

**Authors:** Christine Liebrecht, Florian Kunneman, [Antal van den Bosch](https://md.research.elamur.ai/free/en/authors/antal-van-den-bosch/) · **Year:** 2013 · **Venue:** Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/W13-1605/) · [PDF](https://aclanthology.org/W13-1605.pdf) · [OpenAlex](https://openalex.org/W2114661483)

**Citations:** 222 citations (openalex-snapshot · 2026-10-14) · [51](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Sarcasm can invert the polarity of a message, often by using positive or exaggerated wording to express a negative view, which undermines sentiment analysis. The authors build a sarcasm detector for Dutch Twitter. They use the author-supplied hashtag #sarcasme as a noisy label. They gathered 77,948 tagged tweets from a Netherlands e-Science Centre archive of Dutch tweets, and a manual check of a sample suggested about 85% are genuinely sarcastic. A machine learning classifier was trained on this data and evaluated on a realistic, heavily imbalanced test set: a full day of 3.3 million Dutch tweets. With the hashtag stripped, the classifier recovered three quarters of the explicitly tagged tweets. Among its highest-ranked untagged tweets, however, only about 30% were actually sarcastic, showing how difficult open-world detection is. A linguistic analysis found that sarcastic tweets are mostly literally positive and rely on intensifiers, exclamations or explicit markers. The authors argue that hashtags act as a written stand-in for the tone of voice and facial cues used in spoken sarcasm.

## Key points
- **Training data:** 77,948 Dutch tweets carrying #sarcasme. Annotation of a sample indicated roughly 85% are truly sarcastic.
- **Test set:** one day's stream of 3.3 million Dutch tweets, of which only 135 carried the hashtag. With the tag removed, the classifier found 101 of them (75%).
- **Untagged tweets:** precision in the top-250 ranked tweets was about 30%, with 76 tweets judged sarcastic.
- **Strongest predictors:** positive intensifiers (e.g., *geweldig*, *heerlijk*, *fantastisch*) and plain positive words, with relatively few negative words.
- **Breakdown of the 76 sarcastic tweets:**
  - 34.2% carried only an explicit marker and no hyperbole.
  - 59.2% contained intensifiers and/or exclamations.
  - Only 2.6% combined all three cue types.

*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/)

## Cited by

Cited by

51 papers of our library cite it (of the 140673 whose bibliographies we read); 30 of them have a page here, 21 not yet.

- [Modelling Irony in Twitter: Feature Analysis and Evaluation](https://md.research.elamur.ai/free/en/papers/barbieri-2014-modelling-irony-twitter-feature-analysis/)
- [Modelling Irony in Twitter](https://md.research.elamur.ai/free/en/papers/barbieri-2014-modelling-irony-twitter/)
- [Modelling Sarcasm in Twitter, a Novel Approach](https://md.research.elamur.ai/free/en/papers/barbieri-2014-modelling-sarcasm-twitter-novel-approach/)
- [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/)
- [Linguistic Features in Automatic Sarcasm Detection](https://md.research.elamur.ai/free/en/papers/ducret-2020-linguistic-features-automatic-sarcasm-detection/)
- [Sarcastic or Not: Word Embeddings to Predict the Literal or Sarcastic Meaning of Words](https://md.research.elamur.ai/free/en/papers/ghosh-2015-sarcastic-or-not-word-embeddings/)
- [Fracking Sarcasm using Neural Network](https://md.research.elamur.ai/free/en/papers/ghosh-2016-fracking-sarcasm-neural-network/)
- [Magnets for Sarcasm: Making Sarcasm Detection Timely, Contextual and Very Personal](https://md.research.elamur.ai/free/en/papers/ghosh-2017-magnets-sarcasm-making-sarcasm-detection/)
- [Sarcasm Analysis Using Conversation Context](https://md.research.elamur.ai/free/en/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/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/)
- [We Usually Don't Like Going to the Dentist: Using Common Sense to Detect Irony on Twitter](https://md.research.elamur.ai/free/en/papers/hee-2018-we-usually-don-t-like/)
- [Harnessing Context Incongruity for Sarcasm Detection](https://md.research.elamur.ai/free/en/papers/joshi-2015-harnessing-context-incongruity-sarcasm-detection/)
- [Are Word Embedding-based Features Useful for Sarcasm Detection?](https://md.research.elamur.ai/free/en/papers/joshi-2016-are-word-embedding-based-features/)
- [Automatic Sarcasm Detection: A Survey](https://md.research.elamur.ai/free/en/papers/joshi-2016-automatic-sarcasm-detection-survey/)
- [Towards a Contextual Pragmatic Model to Detect Irony in Tweets](https://md.research.elamur.ai/free/en/papers/karoui-2015-contextual-pragmatic-model-detect-irony/)
- [Who cares about Sarcastic Tweets? Investigating the Impact of Sarcasm on Sentiment Analysis.](https://md.research.elamur.ai/free/en/papers/maynard-2014-who-cares-about-sarcastic-tweets/)
- [Harnessing Cognitive Features for Sarcasm Detection](https://md.research.elamur.ai/free/en/papers/mishra-2016-harnessing-cognitive-features-sarcasm-detection/)
- [Learning Cognitive Features from Gaze Data for Sentiment and Sarcasm Classification using Convolutional Neural Network](https://md.research.elamur.ai/free/en/papers/mishra-2017-learning-cognitive-features-gaze-data/)
- [iSarcasm: A Dataset of Intended Sarcasm](https://md.research.elamur.ai/free/en/papers/oprea-2020-isarcasm-dataset-intended-sarcasm/)
- [Are you serious?: Rhetorical Questions and Sarcasm in Social Media Dialog](https://md.research.elamur.ai/free/en/papers/oraby-2017-are-you-serious-rhetorical-questions/)
- [A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks](https://md.research.elamur.ai/free/en/papers/poria-2016-deeper-look-into-sarcastic-tweets/)
- [Sarcasm as Contrast between a Positive Sentiment and Negative Situation](https://md.research.elamur.ai/free/en/papers/prag-2013-riloff-sarcasm-as-contrast-between-positive/)
- [SemEval-2014 Task 9: Sentiment Analysis in Twitter](https://md.research.elamur.ai/free/en/papers/rosenthal-2014-semeval-2014-task-9-sentiment/)
- [SemEval-2015 Task 10: Sentiment Analysis in Twitter](https://md.research.elamur.ai/free/en/papers/rosenthal-2015-semeval-2015-task-10-sentiment/)
- [Reactive Supervision: A New Method for Collecting Sarcasm Data](https://md.research.elamur.ai/free/en/papers/shmueli-2020-reactive-supervision-new-method-collecting/)
- [A Novel Hierarchical BERT Architecture for Sarcasm Detection](https://md.research.elamur.ai/free/en/papers/srivastava-2020-novel-hierarchical-bert-architecture-sarcasm/)
- [Chinese Irony Corpus Construction and Ironic Structure Analysis](https://md.research.elamur.ai/free/en/papers/tang-2014-chinese-irony-corpus-construction-ironic/)
- [Building a Bridge: A Method for Image-Text Sarcasm Detection Without Pretraining on Image-Text Data](https://md.research.elamur.ai/free/en/papers/wang-2020-building-bridge-method-image-text/)

## Cite

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

```bibtex
@inproceedings{liebrecht2013perfect,
  title = {{The perfect solution for detecting sarcasm in tweets \#not}},
  author = {Liebrecht, Christine and Kunneman, Florian and Bosch, Antal van den},
  booktitle = {Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis},
  year = {2013},
  url = {https://aclanthology.org/W13-1605/}
}
```

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