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

Saqib Hakak, Mamoun Alazab, Suleman Khan, Thippa Reddy Gadekallu, Praveen Kumar Reddy Maddikunta, Wazir Zada Khan

    Research output: Contribution to journalArticlepeer-review


    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 fake news has become easier. Anyone using these platforms can create and share 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 the poor selection of features, inefficient tuning of parameters, imbalanced datasetsred, 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 and testing accuracy of 99.8% and 44.15% respectively on the ISOT dataset. For the Liar dataset, we achieved the training and testing accuracy of 100%.

    Original languageEnglish
    Pages (from-to)47-58
    Number of pages12
    JournalFuture Generation Computer Systems
    Publication statusPublished - Apr 2021


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