Work

PTUK-HULAT at ArAIEval Shared Task Fine-tuned Distilbert to Predict Disinformative Tweets

Year: 2023

Type: conference-paper

Abstract: Disinformation involves the dissemination of incomplete, inaccurate, or misleading information; it has the objective, goal, or purpose of deliberately or intentionally lying to others about the truth.The spread of disinformative information on social media has serious implications, and it causes concern among internet users in different aspects.Automatic classification models are required to detect disinformative posts on social media, especially on Twitter.In this article, DistilBERT multilingual model was fine-tuned to classify tweets either as dis-informative or not dis-informative in Subtask 2A of the ArAIEval shared task.The system outperformed the baseline and achieved F1 micro 87% and F1 macro 80%.Our system ranked 11 compared with all participants.

Source: Arabic Natural Language Processing Conference (ArabicNLP)

DOI: 10.18653/v1/2023.arabicnlp-1.50

Authors: Areej Jaber, Paloma Martinez

Institutions: Palestine Technical University - Kadoorie, Universidad Carlos III de Madrid

Language: English

Topic: Misinformation and Its Impacts

Subfield: Sociology and Political Science

Field: Social Sciences

Domain: Social Sciences

Open Access status: hybrid

Funders: Comunidad de Madrid, Palestine Technical University Kadoorie, Universidad Carlos III de Madrid

Award: G311042921