Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

Fine-Tuning Transformers and LLMs for Fake News Detection in Algerian Dialect

Record type:

paper
Creator:
MerDjiRaiMoh
Publisher:
IEEE
Host:

Visit

doi.org

Tasks

text classification

Languages

Arabic, Algerian Spoken

Licenses

https://doi.org/10.15223/policy-029https://doi.org/10.15223/policy-037

Similar

FASSILA: A Corpus for Algerian Dialect Fake News Detection and Sentiment AnalysisDetection of Arabic and Algerian Fake NewsHausa Fake News Detection Using Lightweight Transformer Models with Adaptive Fine-Tuning in Low-Resource SettingsDziriFake: A Dataset and Comparative Benchmark of Large Language Models for Fake News Detection In Algerian DialectFine-Tuning LLMs for Low-Resource Dialect Translation: The Case of LebaneseFine-tuning Multilingual Transformers for Hausa-English Sentiment Analysis

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

In the context of low-resource languages, the Algerian dialect (AD) faces challenges due to the abse

Detection of Arabic and Algerian Fake News

Abstract In an era characterised by the rapid dissemination of information throu

Hausa Fake News Detection Using Lightweight Transformer Models with Adaptive Fine-Tuning in Low-Resource Settings

The rapid growth of digital communication technologies and online news platforms has enhanced global

DziriFake: A Dataset and Comparative Benchmark of Large Language Models for Fake News Detection In Algerian Dialect

Fine-Tuning LLMs for Low-Resource Dialect Translation: The Case of Lebanese

This paper examines the effectiveness of Large Language Models (LLMs) in translating the low-resourc

Fine-tuning Multilingual Transformers for Hausa-English Sentiment Analysis