Mushakkal: Detecting Arabic Clickbait Using CNN with Various Optimizers

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 Word۲Vec techniques were applied. The Adam optimizer outperformed the others, achieving a Macro-F۱ score of ۷۷%. The RMSprop optimizer closely followed, attaining a Macro-F۱ score of ۷۶%. In contrast, Adadelta proved to be the least effective for classifying Arabic text.

کلیدواژه ها:

Clickbait Detection ، Arabic Dataset ، Arabic Clickbait Detection ، Deep learning ، Optimizers ، CNN