Mitigating Clickbait: An Approach to Spoiler Generation Using Multitask Learning (2023)

Abstract: With the increasing number of users on social media platforms, the detection and categorization of abusive comments have become crucial, necessitating effective strategies to mitigate their impact on online discussions. However, the intricate and diverse nature of lowresource Indic languages presents a challenge in developing reliable detection methodologies. This research focuses on the task of classifying YouTube comments written in Tamil language into various categories. To achieve this, our research conducted experiments utilizing various multi-lingual transformer-based models along with data augmentation approaches involving back translation approaches and other pre-processing techniques. Our work provides valuable insights into the effectiveness of various preprocessing methods for this classification task. Our experiments showed that the Multilingual Representations for Indian Languages (MURIL) transformer model, coupled with round-trip translation and lexical replacement, yielded the most promising results, showcasing a significant improvement of over 15 units in macro F1-score compared to existing baselines. This contribution adds to the ongoing research to mitigate the adverse impact of abusive content on online platforms, emphasizing the utilization of diverse preprocessing strategies and state-of-the-art language models.

## introduction in today's fast-paced digital world, 'clickbait'-enticing but often misleading headlines designed to generate clicks have become a common trend. @xcite . while these headlines draw users in, they often fail to deliver their promises, leading to user dissatisfaction (d. @xcite . consequently, 'clickbait spoiling'-providing succinct, truthful summaries (or 'spoilers') of clickbait content-has emerged as a powerful strategy to counter this trend @xcite . however, detecting, categorizing, and generating these spoilers involves sophisticated natural language processing (nlp) techniques @xcite . furthermore, the type of required spoiler can vary from a short phrase to an extended passage or multiple snippets from the document, adding another layer of complexity to the problem @xcite . examples illustrating each of these types of spoilers are presented in figure 1 . @xcite has proposed a two-step methodology to tackle clickbait. although, hagen et al. approach is a crucial contribution, it has enough room for improvement. our research aims to extend their work by exploring ways to identify passage and multi-spoilers and enhance the user experience by providing accurate spoilers to clickbait content, mitigating the negative impacts of such misleading headlines @xcite . contribution: in this study, we introduce a simple and novel methodology to solve the clickbait spoiling task using multi-task learning(mtl) framework,a method that enables the simultaneous learning of multiple related tasks to improve performance across them as outlined by liu et al. @xcite . this allows a more comprehensive representation of the problem space, potentially enhancing its ability to generate accurate and diverse spoilers from different aspects of the same content. specifically, we show that using spoiler generation in mtl setting can increase the overall generation quality. furthermore, we show fine-tuning longt5 model @xcite has displayed superior performance, achieving 60% higher bleu-4 over our multi-task approach. ## our approach we address the challenge of mitigating clickbait by a two-step process. the first task is to classify the spoilers into phrases, passages, or multi, followed by the second task of generating contextually accurate and informative spoilers through a multi-task learning framework. ## conclusion and future work in this work, we have notably advanced the field of clickbait spoiling, particularly excelling in scenarios involving reduced context. our research lays a solid groundwork for finding the optimal parameters for context reduction and innovative context generation techniques, ensuring better performance in spoiler generation tasks. furthermore, the study underscores the superiority of longt5based models over their bert-based counterparts in generating extended spoilers. the sensitivity of our mtl model's performance to the alpha hyperparameter, initially a limitation, opens a direction for developing adaptive strategies to fine-tune the balance between primary and auxiliary tasks, optimizing the model's efficiency. the future also holds the potential for rigorous statistical analysis to strengthen the validity of the performance gains attributed to multitask learning.