# FASSILA: A Corpus for Algerian Dialect Fake News Detection and Sentiment Analysis

arXiv: https://arxiv.org/abs/2411.04604 · HTML: https://mlsanity.com/paper/2411.04604/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 31 references that are in the map.
- First posted on arXiv 7 Nov 2024.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)
- [ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic](https://mlsanity.com/paper/2101.01785/) (2020)
- [Efficient Few-Shot Learning Without Prompts](https://mlsanity.com/paper/2209.11055/) (2022)
- [A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages](https://mlsanity.com/paper/2006.06202/) (2020)
- [Wojood: Nested Arabic Named Entity Corpus and Recognition using BERT](https://mlsanity.com/paper/2205.09651/) (2022)
- [Stance Prediction and Claim Verification: An Arabic Perspective](https://mlsanity.com/paper/2005.10410/) (2020)
- [AraBART: a Pretrained Arabic Sequence-to-Sequence Model for Abstractive Summarization](https://mlsanity.com/paper/2203.10945/) (2022)
- [DziriBERT: a Pre-trained Language Model for the Algerian Dialect](https://mlsanity.com/paper/2109.12346/) (2021)
- [SentiALG: Automated Corpus Annotation for Algerian Sentiment Analysis](https://mlsanity.com/paper/1808.05079/) (2018)
- [SANA : Sentiment Analysis on Newspapers comments in Algeria](https://mlsanity.com/paper/2006.00459/) (2020)

## Built on by (most cited first, 2 of 2)

- [An End-to-End Hybrid Framework for Rumour Detection in Low-Resources Algerian Dialect](https://mlsanity.com/paper/2606.13411/) (2026)
- [FAD-SA-GRU: Enhancing Hate Speech Detection in Algerian Dialect Through Feature-Augmented Self-Attention GRU Networks](https://mlsanity.com/paper/2607.11279/) (2026)

## Abstract (arXiv)

In the context of low-resource languages, the Algerian dialect (AD) faces challenges due to the absence of annotated corpora, hindering its effective processing, notably in Machine Learning (ML) applications reliant on corpora for training and assessment. This study outlines the development process of a specialized corpus for Fake News (FN) detection and sentiment analysis (SA) in AD called FASSILA. This corpus comprises 10,087 sentences, encompassing over 19,497 unique words in AD, and addresses the significant lack of linguistic resources in the language and covers seven distinct domains. We propose an annotation scheme for FN detection and SA, detailing the data collection, cleaning, and labelling process. Remarkable Inter-Annotator Agreement indicates that the annotation scheme produces consistent annotations of high quality. Subsequent classification experiments using BERT-based models and ML models are presented, demonstrate promising results and highlight avenues for further research. The dataset is made freely available on GitHub (https://github.com/amincoding/FASSILA) to facilitate future advancements in the field.