An Improved Multiple Features and Machine Learning-Based Approach for Detecting Clickbait News on Social Networks

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.

Arabic Sentiment Analysis of Users’ Opinions of Governmental Mobile Applications

“…After pre-processing, the text must be converted into an understandable form. Hybrid and multiple features selection are trends in the literature of sentiment analysis research as in [53,54], In this research, the following methods were explored for feature selection and extraction.…”

An Improved Sentiment Classification Approach for Measuring User Satisfaction toward Governmental Services’ Mobile Apps Using Machine Learning Methods with Feature Engineering and SMOTE Technique

“…As each annotator assigned a sentimental score separately, we followed the same guideline that was used in [32,33].…”

“…They might contain unnecessary data, which can negatively affect the performance of ML classifiers. This makes preprocessing of the data a very important step for improving the performance of the ML models and reducing the training time [32,34,35]. In addition, the size of the featured set can be reduced from 50% to 30%, as stated by the authors of [36].…”

Detection of Web Cross-Site Scripting (XSS) Attacks

“…The existing software for detecting XSS vulnerabilities, such as the Online Web Security Scanner, search for these vulnerabilities only in the open parts of websites that do not require access authorisation [17][18][19][20][21]. Such a drawback is significant considering that XSS vulnerabilities may be located in an unsearchable part of a web resource.…”