# Let-Mi: An Arabic Levantine Twitter Dataset for Misogynistic Language

arXiv: https://arxiv.org/abs/2103.10195 · HTML: https://mlsanity.com/paper/2103.10195/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 30 references that are in the map.
- First posted on arXiv 18 Mar 2021.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)
- [All-in-one: Multi-task Learning for Rumour Verification](https://mlsanity.com/paper/1806.03713/) (2018)
- [Sentiment and Sarcasm Classification with Multitask Learning](https://mlsanity.com/paper/1901.08014/) (2019)
- [Multi-Task Bidirectional Transformer Representations for Irony Detection](https://mlsanity.com/paper/1909.03526/) (2019)

## Built on by (most cited first, 15 of 15)

- [SWSR: A Chinese Dataset and Lexicon for Online Sexism Detection](https://mlsanity.com/paper/2108.03070/) (2021)
- [From Languages to Geographies: Towards Evaluating Cultural Bias in Hate Speech Datasets](https://mlsanity.com/paper/2404.17874/) (2024)
- [Dataset for Identification of Homophobia and Transophobia in Multilingual YouTube Comments](https://mlsanity.com/paper/2109.00227/) (2021)
- [Multilingual and Explainable Text Detoxification with Parallel Corpora](https://mlsanity.com/paper/2412.11691/) (2024)
- [Toxic language detection: a systematic review of Arabic datasets](https://mlsanity.com/paper/2312.07228/) (2023)
- [Hate speech detection in algerian dialect using deep learning](https://mlsanity.com/paper/2309.11611/) (2023)
- [MasonTigers@LT-EDI-2024: An Ensemble Approach Towards Detecting Homophobia and Transphobia in Social Media Comments](https://mlsanity.com/paper/2401.14681/) (2024)
- [Deep Multi-Task Models for Misogyny Identification and Categorization on Arabic Social Media](https://mlsanity.com/paper/2206.08407/) (2022)
- [Content-Localization based System for Analyzing Sentiment and Hate Behaviors in Low-Resource Dialectal Arabic: English to Levantine and Gulf](https://mlsanity.com/paper/2312.03727/) (2023)
- [Content-Localization based Neural Machine Translation for Informal Dialectal Arabic: Spanish/French to Levantine/Gulf Arabic](https://mlsanity.com/paper/2312.06926/) (2023)
- [Mpox Narrative on Instagram: A Labeled Multilingual Dataset of Instagram Posts on Mpox for Sentiment, Hate Speech, and Anxiety Analysis](https://mlsanity.com/paper/2409.05292/) (2024)
- [GLARE: Google Apps Arabic Reviews Dataset](https://mlsanity.com/paper/2412.15259/) (2024)
- [Web(er) of Hate: A Survey on How Hate Speech Is Typed](https://mlsanity.com/paper/2506.16190/) (2025)
- [Beyond Binary Classification: Detecting Fine-Grained Sexism in Social Media Videos](https://mlsanity.com/paper/2602.15757/) (2026)
- [AraDetox: A Multi-Dialect Arabic Detoxification Dataset](https://mlsanity.com/paper/2608.22894/) (2026)

## Abstract (arXiv)

Online misogyny has become an increasing worry for Arab women who experience gender-based online abuse on a daily basis. Misogyny automatic detection systems can assist in the prohibition of anti-women Arabic toxic content. Developing such systems is hindered by the lack of the Arabic misogyny benchmark datasets. In this paper, we introduce an Arabic Levantine Twitter dataset for Misogynistic language (LeT-Mi) to be the first benchmark dataset for Arabic misogyny. We further provide a detailed review of the dataset creation and annotation phases. The consistency of the annotations for the proposed dataset was emphasized through inter-rater agreement evaluation measures. Moreover, Let-Mi was used as an evaluation dataset through binary/multi-/target classification tasks conducted by several state-of-the-art machine learning systems along with Multi-Task Learning (MTL) configuration. The obtained results indicated that the performances achieved by the used systems are consistent with state-of-the-art results for languages other than Arabic, while employing MTL improved the performance of the misogyny/target classification tasks.