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

TL;DR

This paper introduces FASSILA, a new annotated corpus for Algerian dialect aimed at fake news detection and sentiment analysis, addressing resource scarcity and demonstrating promising ML classification results.

Contribution

The creation of the first large-scale, annotated Algerian dialect corpus for fake news detection and sentiment analysis, with a detailed annotation scheme and baseline classification experiments.

Findings

High inter-annotator agreement indicates reliable annotations.

Promising results with BERT-based models for FN detection and sentiment analysis.

The dataset is publicly available for future research.

Abstract

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.