An ensemble machine learning approach through effective feature extraction to classify fake news

By Saqib Hakak, Mamoun Alazab, Suleman Khan, Thippa Reddy Gadekallu, Praveen Kumar Reddy Maddikunta, Wazir Zada Khan. Future generations computer systems, 2021

341 citations recorded by the source corpus.

Abstract There are numerous channels available such as social media, blogs, websites etc., through which people can easily access the news. It is due to the availability of these platforms that the dissemination of the fake news has become easier. Anyone using these platforms can create and share the fake news content based on personal or professional motives. To address the issue of detecting fake news, numerous studies based on supervised and unsupervised learning methods have been proposed. However, all those studies do suffer from a certain limitation of poor accuracy. The reason for poor accuracy can be attributed due to several reasons such as poor selection of features, inefficient tuning of parameters, imbalanced datasets etc. In this article, we have proposed an ensemble classification model for detection of the fake news that has achieved a better accuracy compared to the state-of-the-art. The proposed model extracts important features from the fake news datasets and the extracted features are then classified using the ensemble model comprising of three popular machine learning models namely, Decision Tree, Random Forest and Extra Tree Classifier. We achieved a training accuracy of 99.8% and testing accuracy of 44.15% on ISOT dataset and training and testing accuracy of 100% on Liar dataset.

Subfields: Information Systems, Sociology and Political Science, Signal Processing

View Paper: https://www.semanticscholar.org/paper/dad6a02abbd2fa4e4cabdc231f8a4b1722be59f8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Sabrine Amri
2. Esma Aïmeur
3. Gilles Brassard

In Sociology and Political Science

1. Sabrine Amri
2. Esma Aïmeur
3. Gilles Brassard

Citations in the corpus

• Fake news, disinformation and misinformation in social media: a review (2023, background): “Some researchers define purely machine learning models (Del Vicario et al. 2019; Elhadad et al. 2019; Aswani et al. 2017; Hakak et al. 2021; Singh et al. 2021) in their fake news detection approaches.”