Eating Garlic Prevents COVID-19 Infection: Detecting Misinformation on the Arabic Content of Twitter

Autor:	Alqurashi, Sarah, Hamoui, Btool, Alashaikh, Abdulaziz, Alhindi, Ahmad, Alanazi, Eisa
Rok vydání:	2021
Předmět:	Computer Science - Information Retrieval Computer Science - Computers and Society Computer Science - Machine Learning Computer Science - Social and Information Networks
Druh dokumentu:	Working Paper
Popis:	The rapid growth of social media content during the current pandemic provides useful tools for disseminating information which has also become a root for misinformation. Therefore, there is an urgent need for fact-checking and effective techniques for detecting misinformation in social media. In this work, we study the misinformation in the Arabic content of Twitter. We construct a large Arabic dataset related to COVID-19 misinformation and gold-annotate the tweets into two categories: misinformation or not. Then, we apply eight different traditional and deep machine learning models, with different features including word embeddings and word frequency. The word embedding models (\textsc{FastText} and word2vec) exploit more than two million Arabic tweets related to COVID-19. Experiments show that optimizing the area under the curve (AUC) improves the models' performance and the Extreme Gradient Boosting (XGBoost) presents the highest accuracy in detecting COVID-19 misinformation online. Comment: 18 pages, 4 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2101.05626 Zobrazit plný text záznamu View this record from Arxiv