基于阿拉伯语文本分析的假新闻分类模型的开发:案例研究

Hanen Himdi, F. Assiri
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引用次数: 1

摘要

WhatsApp和Line等聊天信息平台带来的沟通便利,以及它们在社会中越来越多的使用和无处不在,促使假新闻的提供者创造并呈现出合法的新闻。尽管不同的组织继续努力解决这个问题,但许多开发的解决方案依赖于验证相关的元数据,而这些元数据并不总是可用的。有几项研究试图通过分析文本内容来识别假新闻;然而,缺乏对阿拉伯语来源的研究。我们的工作填补了这一空白;我们提出了一个基于文本分析对阿拉伯假新闻进行分类的机器学习模型。本文展示了我们的方法在社交媒体应用中对阿拉伯语文本进行分类的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of Classification Model based on Arabic Textual Analysis to Detect Fake News: Case Studies
The ease of communication that has been made possible by chat messaging platforms, such as WhatsApp and Line, and their increased use and ubiquity in society, have motivated purveyors of fake news to create and present their news as legitimate. Though different organisations continued their efforts to resolve this problem, many of the developed solutions rely on verifying the associated metadata, which are not always available. Several studies have attempted to identify fake news by analysing textual content; however, there is a lack of studies on Arabic language sources. Our work filled this gap; we proposed a machine learning model that classifies Arabic fake news based on textual analysis. This paper shows the feasibility of our approach to classify Arabic text in social media applications.
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