Approaches in Fake News Detection : An Evaluation of Natural Language Processing and Machine Learning Techniques on the Reddit Social Network

M. Shariff, Brian Thoms, Jason T. Isaacs, Vida Vakilian
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Abstract

Classifier algorithms are a subfield of data mining and play an integral role in finding patterns and relationships within large datasets. In recent years, fake news detection has become a popular area of data mining for several important reasons, including its negative impact on decision-making and its virality within social networks. In the past, traditional fake news detection has relied primarily on information context, while modern approaches rely on auxiliary information to classify content. Modelling with machine learning and natural language processing can aid in distinguishing between fake and real news. In this research, we mine data from Reddit, the popular online discussion forum and social news aggregator, and measure machine learning classifiers in order to evaluate each algorithm’s accuracy in detecting fake news using only a minimal subset of data.
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假新闻检测的方法:对Reddit社交网络上的自然语言处理和机器学习技术的评估
分类器算法是数据挖掘的一个子领域,在寻找大型数据集中的模式和关系方面发挥着不可或缺的作用。近年来,假新闻检测已经成为数据挖掘的一个热门领域,原因有几个,包括它对决策的负面影响,以及它在社交网络中的病毒式传播。过去,传统的假新闻检测主要依靠信息语境,而现代方法则依靠辅助信息对内容进行分类。利用机器学习和自然语言处理进行建模可以帮助区分假新闻和真新闻。在这项研究中,我们从Reddit(一个流行的在线讨论论坛和社会新闻聚合器)中挖掘数据,并测量机器学习分类器,以便仅使用最小的数据子集来评估每种算法在检测假新闻方面的准确性。
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