An improved multiple features and machine learning-based approach for detecting clickbait news on social networks

Al-Sarem, M, Saeed, F, Al-Mekhlafi, ZG, Mohammed, BA, Hadwan, M, Al-Hadhrami, T ORCID: https://orcid.org/0000-0001-7441-604X, Alshammari, MT, Alreshidi, A and Alshammari, TS, 2021. An improved multiple features and machine learning-based approach for detecting clickbait news on social networks. Applied Sciences, 11 (20): 9487. ISSN 2076-3417

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Abstract

The widespread usage of social media has led to the increasing popularity of online advertisements, which have been accompanied by a disturbing spread of clickbait headlines. Clickbait dissatisfies users because the article content does not match their expectation. Detecting clickbait posts in online social networks is an important task to fight this issue. Clickbait posts use phrases that are mainly posted to attract a user’s attention in order to click onto a specific fake link/website. That means clickbait headlines utilize misleading titles, which could carry hidden important information from the target website. It is very difficult to recognize these clickbait headlines manually. Therefore, there is a need for an intelligent method to detect clickbait and fake advertisements on social networks. Several machine learning methods have been applied for this detection purpose. However, the obtained performance (accuracy) only reached 87% and still needs to be improved. In addition, most of the existing studies were conducted on English headlines and contents. Few studies focused specifically on detecting clickbait headlines in Arabic. Therefore, this study constructed the first Arabic clickbait headline news dataset and presents an improved multiple feature-based approach for detecting clickbait news on social networks in Arabic language. The proposed approach includes three main phases: data collection, data preparation, and machine learning model training and testing phases. The collected dataset included 54,893 Arabic news items from Twitter (after pre-processing). Among these news items, 23,981 were clickbait news (43.69%) and 30,912 were legitimate news (56.31%). This dataset was pre-processed and then the most important features were selected using the ANOVA F-test. Several machine learning (ML) methods were then applied with hyper-parameter tuning methods to ensure finding the optimal settings. Finally, the ML models were evaluated, and the overall performance is reported in this paper. The experimental results show that the Support Vector Machine (SVM) with the top 10% of ANOVA F-test features (user-based features (UFs) and content-based features (CFs)) obtained the best performance and achieved 92.16% of detection accuracy.

Item Type:	Journal article
Publication Title:	Applied Sciences
Creators:	Al-Sarem, M., Saeed, F., Al-Mekhlafi, Z.G., Mohammed, B.A., Hadwan, M., Al-Hadhrami, T., Alshammari, M.T., Alreshidi, A. and Alshammari, T.S.
Publisher:	MDPI AG
Date:	13 October 2021
Volume:	11
Number:	20
ISSN:	2076-3417
Identifiers:	Number Type 10.3390/app11209487 DOI 1479338 Other
Divisions:	Schools > School of Science and Technology
Record created by:	Jeremy Silvester
Date Added:	14 Oct 2021 15:35
Last Modified:	14 Oct 2021 15:35
URI:	https://irep.ntu.ac.uk/id/eprint/44450

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