A deep learning framework for clickbait spoiler generation and type identification

Clickbait pertains to attention-grabbing or misleading content that sacrifices accuracy for clicks. This marketing tactic is widely used to drive online traffic, but it can lead to misinformation, frustration, and a diminished user experience. Consequently, the timely identification and countering of clickbait posts is crucial. One way to counter clickbait posts is to spoil them by creating short messages that reveal their true content. This research generates short texts called clickbait spoiler for clickbait headlines. We have fine-tuned the Generative Pretrained Transformer 2 (GPT-2) medium model with the clickbait dataset to generate spoilers for them. Since these spoilers vary from one word to multiple paragraphs, we also determine the type of spoilers. For spoiler type identification a sentence encoder Bidirectional Encoder Representations from Transformers (BERT) is used to generate embeddings of each sentence, followed by classification by Support Vector Machine (SVM). The spoiler generation by GPT-2 yielded a Bilingual Evaluation Understudy (BLEU) score of 0.58 outperforming the previous state-of-the-art models. The spoiler identification model achieved a precision of 0.83, recall of 0.82, F1-Score of 0.80, MCC Score of 0.63, and accuracy of 0.83 surpassing previous state-of-the-art models.