G en E x: A Commonsense-aware Unified Generative Framework for Explainable Cyberbullying Detection (2023)

Abstract: With the rise of social media and online communication, the issue of cyberbullying has gained significant prominence. While extensive research is being conducted to develop more effective models for detecting cyberbullying in monolingual languages, a significant gap exists in understanding code-mixed languages and the need for explainability in this context. To address this gap, we have introduced a novel benchmark dataset named BullyExplain for explainable cyberbullying detection in code-mixed language. In this dataset, each post is meticulously annotated with four labels: bully, sentiment, target, and rationales, indicating the specific phrases responsible for identifying the post as a bully. Our current research presents an innovative unified generative framework, GenEx, which reimagines the multitask problem as a text-to-text generation task. Our proposed approach demonstrates its superiority across various evaluation metrics when applied to the BullyExplain dataset, surpassing other baseline models and current state-of-the-art approaches.

## introduction cyberaggression, which encompasses various types and forms of aggressive behavior conducted through information and communication technologies (ict), including cyberbullying and hate speech. in this study, we are working on cyberbullying detection. hate speech and cyberbullying are conceptualized differently. hate speech @xcite pertains to online assaults targeting collective identity, while cyberbullying @xcite is characterized by a repetitive, harmful intent and a power imbalance. according to research from the pew research center foot_1 , approximately 40% of social media users have encountered cyberbullying, resulting in emotional distress, anxiety, diminished self-esteem, momentary fear, and even suicidal ideation @xcite . in the past decade, the majority of cyberbullying detection research has concentrated on monolingual social media data, using conventional machine learning @xcite and deep learning @xcite models. however, these studies primarily sought to enhance detection performance without delving into the realm of explainability. as we enter the era of explainable artificial intelligence @xcite , the need for providing interpretations behind machine learning decisions has become paramount. furthermore, the prevalence of code-mixing @xcite , where multiple languages are interchanged within speech, is rapidly increasing. a comprehensive analysis of over 50 million tweets revealed that approximately 3.5% of them incorporated codemixing. therefore, addressing the intricacies of code-mixed languages should be a central concern at this juncture. numerous studies on multitasking @xcite have demonstrated the effectiveness of incorporating closely related auxiliary tasks alongside the main task to improve the performance of primary tasks (such as cyberbullying detection @xcite , complaint identification @xcite ). a common configuration for multitask models involves a shared encoder that consolidates data representations from diverse tasks, accompanied by multiple task-specific layers or heads linked to this central encoder. nonetheless, this approach is associated with certain limitations, including the risk of negative transfer @xcite , wherein multiple tasks, instead of aiding the learning process, begin to hinder it. additionally, concerns emerge regarding model capacity @xcite , as an overly expansive shared encoder can impede the effective transfer of information between various tasks. in an attempt to overcome the above-mentioned challenges, in this paper, we have developed an explainable cyberbullying dataset (bullyexplain) and a unified generative approach to solving four tasks simultaneously. task 1 cyberbullying detection (cd): given a text, detect whether it is bully or non-bully. task 2 sentiment analysis (sa) given a text, detect whether it is positive, negative, or neutral. task 3 target identification (ti) detect the type of targets that each bully post points to. task 4 rationales detection (rd): the task involves pinpointing text segments within the source text that substantiate a classification judgment. we introduce a commonsense-aware unified generative framework, genex, which excels in concurrently addressing all four tasks within a text-to-text generation environment, with a focus on prioritizing cd and rd as primary tasks, while treating ti and sa as secondary or auxiliary tasks. in summary, our contributions encompass two key aspects: (i) the achievement of explainable cyberbullying detection within code-mixed contexts and (ii) the innovative framing of the multi-task problem as a text-to-text generation endeavor. ## related works in this section, we have undertaken a review of cyberbullying detection research encompassing both monolingual and code-mixed data, as well as rationales. works on monolingual data: in the realm of monolingual data, @xcite conducted an investigation into cyberbullying detection, employing a dataset comprising 4500 youtube comments and various binary and multiclass classifiers. additionally, @xcite introduced a model that leverages diverse machine learning approaches and the psychological attributes of twitter users for cyberbullying detection. @xcite put forward an ensemble approach that amalgamates two deep learning models, employing character-level cnn and long-term recurrent convolutional networks (lrcn) for cyberbul-lying comment detection. cyberbert, a bertbased framework developed by @xcite , achieved state-of-the-art results across multiple datasets, including formspring, twitter, and wikipedia. works on code-mixed data: in the context of code-mixed data, a cyberbullying dataset featuring hindi-english code-mixed language was curated by @xcite . they further proposed a deep learning framework that combines bert and capsule network methodologies, achieving an accuracy of 79.28%. recent studies have delved into the impact of writing system changes (wscs) on the chinese language concerning affective and emotion analysis of social media text, as highlighted by @xcite . these studies have underscored the value of wscs in enhancing various analytical tasks. additionally, @xcite contributed to this field by assembling an aggression-annotated corpus featuring 21k facebook comments and 18k tweets in hindi-english code-mixed language. works on rationales: the realm of rationales was explored by @xcite , who introduced the concept of rationales, wherein human annotators underlined text sections that substantiated their tagging decisions. their findings demonstrated the utility of rationales in enhancing sentiment classification performance. furthermore, @xcite presented the hatexplain dataset, tailored for hate speech detection. @xcite introduced the task of detecting toxic post spans in english texts responsible for the toxicity, and later released the "tox-icspans" dataset. lastly, ravikiran and annamalai (2021) created the "dosa" dataset, featuring english-tamil code-mixed posts annotated with corresponding toxic spans. ## methodology in this section, we introduce genex model for explainable cyberbullying detection, which is illustrated in figure 1 . ## conclusion and future works this research tackles the challenge of cyberbullying detection in a code-mixed linguistic context while emphasizing the aspect of explainability. the contributions of this study can be summarized into two key components: (a) the creation of the explainable cyberbully detection (cd) dataset in a code-mixed language, featuring annotations for rationale/phrases used in decision-making alongside bully labels, sentiment labels, and target labels; (b) the introduction of a unified generative framework, (genex) founded on common-sense knowledge and reinforcement learning, which adeptly addresses four distinct tasks. by formulating the multitask problem as a text-to-text generation task and harnessing the capabilities of large pre-trained sequence-to-sequence models, our proposed model surpasses the state-of-the-art with an enhanced accuracy score of 2.47% for the cd task. future attempts will be made to extend explainable cyberbullying detection in a multimodal setting considering image and text modalities.