Phony News Detection using Machine Learning and Deep-Learning Techniques

Sonal Garg, D. Sharma
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引用次数: 9

Abstract

The proliferation of misleading news stories on social-media raised a big challenge due to its potential to create an adverse impact on human-being. Existing lexico-syntactic features are unable to detect counterfeit news. Most of the state of art algorithms used small datasets containing a limited number of the training dataset. In this paper, we evaluate our framework on the LIAR dataset by applying machine learning and advanced deep learning techniques. LIAR is a predominant dataset consist of 12,836 short news collected from different sources, including social media. The proposed framework uses POS (part of speech) tagging information and Glove Embedding. The result shows the superiority in terms of accuracy in comparison to the existing state of the art algorithm.
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利用机器学习和深度学习技术进行虚假新闻检测
社交媒体上误导性新闻的泛滥带来了一个巨大的挑战,因为它可能对人类产生不利影响。现有的词典句法特征无法检测假新闻。大多数最先进的算法使用包含有限数量的训练数据集的小数据集。在本文中,我们通过应用机器学习和先进的深度学习技术在LIAR数据集上评估我们的框架。LIAR是一个主要的数据集,由12836个从不同来源收集的短新闻组成,包括社交媒体。该框架采用词性标注信息和手套嵌入技术。结果表明,与现有算法相比,该算法在精度方面具有优势。
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