---
title: "DAICT: A Dialectal Arabic Irony Corpus — Abbes 2020"
description: "Arabic has a large presence on social media, yet no freely available corpus existed for detecting irony in it."
authors: ["Ines Abbes", "Wajdi Zaghouani", "Omaima El-Hardlo", "Faten Ashour"]
year: 2020
venue: "Proceedings of the Twelfth Language Resources and Evaluation Conference"
source: "https://aclanthology.org/2020.lrec-1.768/"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/abbes-2020-daict-dialectal-arabic-irony-corpus/"
---
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# DAICT: A Dialectal Arabic Irony Corpus Extracted from Twitter

**Authors:** Ines Abbes, [Wajdi Zaghouani](https://md.research.elamur.ai/free/authors/wajdi-zaghouani/), Omaima El-Hardlo, Faten Ashour · **Year:** 2020 · **Venue:** Proceedings of the Twelfth Language Resources and Evaluation Conference · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2020.lrec-1.768/) · [PDF](https://aclanthology.org/2020.lrec-1.768.pdf) · [OpenAlex](https://openalex.org/W3030258237)

**Citations:** 25 citations (openalex-snapshot · 2026-10-14) · [26](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Arabic has a large presence on social media, yet no freely available corpus existed for detecting irony in it. The only earlier resource, SOUKHRIA, was shared with a limited group of researchers and relied on automatic labels derived from hashtags. The authors build DAICT, an open-domain Twitter corpus that covers Modern Standard Arabic (MSA), regional dialects and mixed text. They compiled irony- and sarcasm-related hashtags in Arabic, English and French and harvested a large pool of Arabic tweets. They then kept only messages carrying the four most frequent hashtags and filtered out noisy or uninformative posts. Two linguists labeled the remaining tweets by hand, applying the polarity-inversion definition of irony from Van Hee et al. (2016), and a third annotator settled disputes. Hashtags turned out to be unreliable irony signals, so manual labeling produced both ironic and non-ironic examples. Hashtags were kept in the text as interpretive context. The resulting freely downloadable dataset enables work on dialect-aware Arabic irony detection and on how credible irony hashtags are.

## Key points
- Collection used 100 hand-picked hashtags and gathered about 1.47M Arabic tweets posted between 31 March 2012 and 1 March 2019.
- Filtering removed retweets, duplicates, mentions, URLs, images, tweets under 70 characters and tweets with more than four hashtags. This left 5,358 tweets.
- Two linguists annotated the full set, and their agreement rose to 0.92. Remaining conflicts went to a third annotator. Irony and sarcasm were not distinguished.
- A manual check of a random sample from SOUKHRIA found at least 8% of its tweets misclassified, compared with the roughly 3% error its authors had assumed.
- Irony hashtags are kept in DAICT, because removing them can make a tweet's intent impossible to judge. Knowledge of dialect and political context also proved essential for interpretation.

*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

26 papers of our library cite it (of the 140673 whose bibliographies we read); 5 of them have a page here, 21 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/)
- [Overview of the WANLP 2021 Shared Task on Sarcasm and Sentiment Detection in Arabic](https://md.research.elamur.ai/free/papers/farha-2021-overview-wanlp-2021-shared-task/)
- [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/)
- [Deep Multi-Task Model for Sarcasm Detection and Sentiment Analysis in Arabic Language](https://md.research.elamur.ai/free/papers/mahdaouy-2021-deep-multi-task-model-sarcasm/)

## Cite

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

```bibtex
@inproceedings{abbes2020daict,
  title = {{DAICT: A Dialectal Arabic Irony Corpus Extracted from Twitter}},
  author = {Abbes, Ines and Zaghouani, Wajdi and El-Hardlo, Omaima and Ashour, Faten},
  booktitle = {Proceedings of the Twelfth Language Resources and Evaluation Conference},
  year = {2020},
  url = {https://aclanthology.org/2020.lrec-1.768/}
}
```

- [← previous CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models](https://md.research.elamur.ai/free/papers/nangia-2020-crows-pairs-challenge-dataset-measuring/)
- [next → DART: Open-Domain Structured Data Record to Text Generation](https://md.research.elamur.ai/free/papers/nan-2020-dart-open-domain-structured-data/)
- [↑ back to papers of 2020](https://md.research.elamur.ai/free/papers/2020/page/2/)

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