# sarcasm detection and quantification in arabic tweets

arXiv: https://arxiv.org/abs/2108.01425 · HTML: https://mlsanity.com/paper/2108.01425/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 3 Aug 2021.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [NADI 2021: The Second Nuanced Arabic Dialect Identification Shared Task](https://mlsanity.com/paper/2103.08466/) (2021)
- [Automatic Sarcasm Detection: A Survey](https://mlsanity.com/paper/1602.03426/) (2016)
- [Arabic natural language processing: An overview](https://mlsanity.com/paper/1903.02784/) (2019)
- [Multi-Dialect Arabic BERT for Country-Level Dialect Identification](https://mlsanity.com/paper/2007.05612/) (2020)
- [Deep Multi-Task Model for Sarcasm Detection and Sentiment Analysis in Arabic Language](https://mlsanity.com/paper/2106.12488/) (2021)
- [Irony Detection in a Multilingual Context](https://mlsanity.com/paper/2002.02427/) (2020)
- [BERT Fine-tuning For Arabic Text Summarization](https://mlsanity.com/paper/2004.14135/) (2020)

## Built on by (most cited first, 1 of 1)

- [A comprehensive survey of contemporary Arabic sentiment analysis: Methods, Challenges, and Future Directions](https://mlsanity.com/paper/2502.03827/) (2025)

## Abstract (arXiv)

The role of predicting sarcasm in the text is known as automatic sarcasm detection. Given the prevalence and challenges of sarcasm in sentiment-bearing text, this is a critical phase in most sentiment analysis tasks. With the increasing popularity and usage of different social media platforms among users around the world, people are using sarcasm more and more in their day-to-day conversations, social media posts and tweets, and it is considered as a way for people to express their sentiment about some certain topics or issues. As a result of the increasing popularity, researchers started to focus their research endeavors on detecting sarcasm from a text in different languages especially the English language. However, the task of sarcasm detection is a challenging task due to the nature of sarcastic texts; which can be relative and significantly differs from one person to another depending on the topic, region, the user's mentality and other factors. In addition to these challenges, sarcasm detection in the Arabic language has its own challenges due to the complexity of the Arabic language, such as being morphologically rich, with many dialects that significantly vary between each other, while also being lowly resourced. In recent years, only few research attempts started tackling the task of sarcasm detection in Arabic, including creating and collecting corpora, organizing workshops and establishing baseline models. This paper intends to create a new humanly annotated Arabic corpus for sarcasm detection collected from tweets, and implementing a new approach for sarcasm detection and quantification in Arabic tweets. The annotation technique followed in this paper is unique in sarcasm detection and the proposed approach tackles the problem as a regression problem instead of classification; i.e., the model attempts to predict the level of sarcasm instead of binary classification.