Intelligent gravitational search random forest algorithm for fake news detection

Rathika Natarajan (), Abolfazl Mehbodniya (), Kantilal Pitambar Rane (), Sonika Jindal, Mohammed Faez Hasan (), Luis Vives () and Abhishek Bhatt ()
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Rathika Natarajan: Department of Electronics and Communication Engineering, Jaya Institute of Technology, Thiruthani, Thiruvallur, Tamilnadu, India
Abolfazl Mehbodniya: Department of Electronics and Communications Engineering, Kuwait College of Science and Technology, Doha Area, 7th Ring Road, Kuwait
Kantilal Pitambar Rane: Department of Electronics and Telecom Engineering, KCE Society’s College of Engineering and Information Technology, Jalgaon, Maharashtra 425001, India
Sonika Jindal: Department of Computer Science and Engineering, Shaheed Bhagat Singh State University, Firozpur, Punjab 152001, India
Mohammed Faez Hasan: Department of Finance and Banking Sciences, Kerbala University, Karbala 56001, Iraq
Luis Vives: Department of Computer Science, Peruvian University of Applied Sciences, Lima 15023, Peru
Abhishek Bhatt: Department of Electronics and Telecommunication, College of Engineering Pune, Maharashtra 411005, India

International Journal of Modern Physics C (IJMPC), 2022, vol. 33, issue 06, 1-19

Abstract: Online social media has made the process of disseminating news so quick that people have shifted their way of accessing news from traditional journalism and press to online social media sources. The rapid rotation of news on social media makes it challenging to evaluate its reliability. Fake news not only erodes public trust but also subverts their opinions. An intelligent automated system is required to detect fake news as there is a tenuous difference between fake and real news. This paper proposes an intelligent gravitational search random forest (IGSRF) algorithm to be employed to detect fake news. The IGSRF algorithm amalgamates the Intelligent Gravitational Search Algorithm (IGSA) and the Random Forest (RF) algorithm. The IGSA is an improved intelligent variant of the classical gravitational search algorithm (GSA) that adds information about the best and worst gravitational mass agents in order to retain the exploitation ability of agents at later iterations and thus avoid the trapping of the classical GSA in local optimum. In the proposed IGSRF algorithm, all the intelligent mass agents determine the solution by generating decision trees (DT) with a random subset of attributes following the hypothesis of random forest. The mass agents generate the collection of solutions from solution space using random proportional rules. The comprehensive prediction to decide the class of news (fake or real) is determined by all the agents following the attributes of random forest. The performance of the proposed algorithm is determined for the FakeNewsNet dataset, which has sub-categories of BuzzFeed and PolitiFact news categories. To analyze the effectiveness of the proposed algorithm, the results are also evaluated with decision tree and random forest algorithms. The proposed IGSRF algorithm has attained superlative results compared to the DT, RF and state-of-the-art techniques.

Keywords: Gravitational search algorithm; fake news; random forest; decision tree; meta-heuristic; fake news detection (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S012918312250084X

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