Linguistic feature based learning model for fake news detection and classification

作者：

Highlights：

• Linguistic Features Based Fake News Detection and Classification approach is proposed

• Syntactic, Sentimental, Grammatical, and readability features are used as linguistic features.

• Explored the importance of deep learning model over Machine Learning Models for fake news detection and classification problem.

• Fake news detection deep learning results are presented with hyper parameter tuning to achieve improved performance.

摘要

•Linguistic Features Based Fake News Detection and Classification approach is proposed•Syntactic, Sentimental, Grammatical, and readability features are used as linguistic features.•Explored the importance of deep learning model over Machine Learning Models for fake news detection and classification problem.•Fake news detection deep learning results are presented with hyper parameter tuning to achieve improved performance.

论文关键词：Fake news,Syntactic,Readability,Neural network,Deep learning,Machine learning,LSTM

论文评审过程：Received 19 February 2020, Revised 19 August 2020, Accepted 25 October 2020, Available online 16 November 2020, Version of Record 24 December 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.114171