부산대학교 도서관

The advent of the Internet is a boon to society. However, many of its banes cannot be undermined, cyberbullying being one of them. The emotional state and sentiment of a person have a significant influence on the intended content. The current work is the first attempt in investigating the role of sentiment and emotion information for identifying cyberbullying in the Indian scenario. From Twitter, a benchmark Hind–English code-mixed corpus called BullySentEmo has been developed as there is no dataset available labeled with bully, sentiment, and emotion. Moreover, emoji information available with tweet texts can provide better understanding of user intention. The developed dataset consists of both modalities, tweet text, and emoji. In India, the majority of communication on different social media platforms is based on Hindi and English and language switching is a common practice in digital communication. An attention-based multimodal, adversarial multitasking framework is proposed for cyberbully detection (CBD) considering two auxiliary tasks: sentiment analysis (SA) and emotion recognition (ER). Experimental results indicate that compared to unimodal and single-task variants, the proposed framework improves the performance of the main task, i.e., CBD, by 3.59% and 2.56% in terms of accuracy and F1-Score, respectively. Furthermore, two different benchmark datasets (Twitter dataset and Aggression dataset) have been considered to show the robustness of our proposed model.