Enhancing Clickbait Detection Through Deep Learning and Language-specific Analysis in English and Kurdish

In the rapidly evolving landscape of online content, the prevalence of clickbait poses a significant challenge for users seeking reliable and informative material. Clickbait, characterized by sensationalized headlines designed to attract attention and drive user engagement, has become a pervasive issue in various languages and cultural contexts. As digital platforms continue to host a vast array of content, the need for robust clickbait detection mechanisms becomes paramount to ensure a trustworthy online experience. This study aims to evaluate the performance of deep neural networks in clickbait detection for both English and Kurdish languages. To address clickbait in Kurdish, we collected 10,000 news articles from various Kurdish platforms, complemented by a dataset of 32,000 English headlines curated by Chakraborty. Utilizing Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BILSTM), Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and a hybrid CNN BILSTM model, we evaluated clickbait detection techniques. Findings underscore the importance of understanding language-specific traits and cultural norms in spotting clickbait across linguistic boundaries. The Bidirectional Long Short- Term Memory algorithm proved most effective in English, boasting a 99.23% accuracy rate, 95.33% precision, 94.33% recall rate, and a 95% F1 score. In Kurdish, the Gated Recurrent Unit algorithm excelled with a 93.93% accuracy rate, 93.13% precision, 95.17% recall rate, and a 94.13% F1 score. This study extends the application of recurrent neural network and deep learning methods in clickbait detection, showcasing their potential in analyzing textual data with nuanced semantic features, contributing valuable insights to the broader field of natural language processing.