Mushakkal: Detecting Arabic Clickbait Using CNN with Various Optimizers
نویسندگان
1 Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia.
2 Department of Information Technology and Communication, Security Forces Hospital, Dammam, Saudi Arabia.
3 Department of Information Technology, College of Computer, Qassim University, Saudi Arabia. Department of Computers and Control Engineering, Faculty of Engineering, Tanta University, Egypt.
4 Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia.
doi
10.22059/jitm.2024.99051چکیده
The term "clickbait" refers to content specifically designed to capture readers' attention, often through misleading headlines, leading to frustration among social media users. In this study, titled "Mushakkal," which translates to "variety" in Arabic, we utilized a Convolutional Neural Network (CNN)—a deep learning approach—to detect clickbait within an Arabic dataset. We compared three optimizers: RMSprop, Adam, and Adadelta, evaluating various parameter settings to determine the most effective combination for detecting clickbait in Arabic content. Our findings revealed that the CNN model performed best when both pre-processing and Word2Vec techniques were applied. The Adam optimizer outperformed the others, achieving a Macro-F1 score of 77%. The RMSprop optimizer closely followed, attaining a Macro-F1 score of 76%. In contrast, Adadelta proved to be the least effective for classifying Arabic text.
