ChSO-DNFNet: Spam detection in Twitter using feature fusion and optimized Deep Neuro Fuzzy Network. (January 2023)

• Record Type: Journal Article
• Authors: Thomas, Merly; Meshram, B.B.
• Is Part Of: Advances in engineering software. Volume 175(2023)
• Publication Date: 2023-01
• Subjects: Twitter -- Social network -- Spam detection -- Deep learning -- Box-Cox transformation; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2022.103333
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: Spam tweets cause number of problems to user, so that automatic spam detection method is required to detect spam tweets. Chimp Sailfish Optimization-based Deep Neuro Fuzzy Network (ChSO-based DNFN) is developed to filter the spam information. The proposed method performs well under high dimensional data in real platform environment using deep learning classifier. Abstract: The usage of social networking platforms for interaction and meeting has grown significantly. However, as social networks continue to expand, Twitter has emerged as a vital social media for real life and has become a major source of spam. To address the aforementioned problems, spam identification on social media becomes an increasingly crucial task. The features that are present in the high dimension data of social networks cannot be used effectively by the existing approaches. In order to filter the spam information in social media, a Chimp Sailfish Optimization-based Deep Neuro Fuzzy Network (ChSO-based DNFN) is proposed. The proposed method effectively performs well under high dimensional data in real platform environment using deep learning classifier. It is more robust and generates optimal result and also reduces the computational complexity problems. Additionally, the proposed approach demonstrated improved performance in terms of metrics like precision, recall, and F-measure, which were measured using a 5k continuous dataset and yielded values of 0.894, 0.903, and 0.898, respectively.