Which machine learning paradigm for fake news detection?

Dimitrios Katsaros, G. Stavropoulos, Dimitrios Papakostas
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引用次数: 39

Abstract

Fake news detection/classification is gradually becoming of paramount importance to out society in order to avoid the so-called reality vertigo, and protect in particular the less educated persons. Various machine learning techniques have been proposed to address this issue. This article presents a comprehensive performance evaluation of eight machine learning algorithms for fake news detection/classification. CCS CONCEPTS • General and reference → Evaluation; • Human-centered computing → Collaborative and social computing design and evaluation methods; Social network analysis.
哪种机器学习范式用于假新闻检测?
为了避免所谓的现实眩晕,特别是保护受教育程度较低的人,假新闻的检测/分类逐渐成为我们社会的头等大事。已经提出了各种机器学习技术来解决这个问题。本文介绍了用于假新闻检测/分类的八种机器学习算法的综合性能评估。CCS概念•一般和参考→评估;•以人为中心的计算→协同和社会计算设计和评估方法;社会网络分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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