Enhancing the Identification of Cyberbullying through Participant Roles (2020)

Abstract: Cyberbullying is a prevalent social problem that inflicts detrimental consequences to the health and safety of victims such as psychological distress, anti-social behaviour, and suicide. The automation of cyberbullying detection is a recent but widely researched problem, with current research having a strong focus on a binary classification of bullying versus non-bullying. This paper proposes a novel approach to enhancing cyberbullying detection through role modeling. We utilise a dataset from ASKfm to perform multi-class classification to detect participant roles (e.g. victim, harasser). Our preliminary results demonstrate promising performance including 0.83 and 0.76 of F1-score for cyberbullying and role classification respectively, outperforming baselines.

## introduction the surge of internet and social media has led to the unprecedented social crisis of cyberbullying, particularly among adolescents. it can lead to various damaging consequences on the health and safety of victims, such as feelings of isolation, depression, and suicide. cyberbullying is the repetitive use of aggressive language among peers, with the intention to harm others through digital media @xcite . despite the illegality of harassing others, most social media platforms are susceptible to cyberbullying due to the openness and anonymisation of platforms. research conducted by @xcite indicates that cyberbullying victimisation rates have approximately doubled between the years 2007 and 2019. adolescents, minorities (e.g. refugees, lgbtqi) and women are among common targets of cyberbullying. the sheer amount of cyberbullying-related incidents vastly exceeds the capacity of manual detection and demands the need to develop technology to effectively and automatically detect this. the development of automated models to detect cyberbullying is a widely researched problem in recent years, with current research focusing on classifying posts as bullying or non-bullying @xcite @xcite . one of the fundamental gaps in current research is that all texts from all users are treated equally without differentiating who has authored bullying and who has been targeted.these models provide a temporary solution by filtering offensive contents. bullies often find novel ways to bypass technology such as incorporating implicit and subtle forms of language (e.g. sarcasm) and pseudo profiles. identifying the roles of authors and targets introduces a novel approach to enable more information-rich models and to foster precise detection. a small number of recent studies focus on cyberbullying-related 'participant roles' (e.g. bully, victim, bystander) (see figure 1 ) @xcite @xcite . motivated by this idea, our work focuses on two tasks, 1) detecting cyberbullying as a binary classification problem, and 2) detecting participant roles as a multi-class classification problem. we build upon previous role identification research and the amica dataset proposed by @xcite . ## related works in addition to modeling bullying and non-bullying content as a binary classification task @xcite @xcite , several research studies focus on participant role identification @xcite @xcite within the cyberbullying context. @xcite defined 8 roles -bully, victim, bystander, assistant, defender, reporter, accuser and reinforcer, based on the theoretical framework of @xcite . the majority of previous studies addressing role identification incorporate user-(e.g., age, gender, location) and social networkbased features (e.g., number of followers, network centrality). although these features have demonstrated a tendency to increase classification performance @xcite , relying on user and network features is logistically challenging in real-world application due to the creation of pseudo profiles and ethical restrictions imposed by platforms. alternatively, lexical and semantic features (e.g., subjectivity lexicons, character n-grams, topic models, profanity word lists, and named entities) of participants' posts are considered in few research studies @xcite . our research aims to automatically identify cyberbullying and roles are based on supervised learning mechanisms that utilizes pretrained language models and advanced contextual embedding techniques. therefore, such mechanisms will mitigate the need for rule-based approaches and will also minimize the requirement for creating task-specific feature extraction mechanisms. ## model description this study focuses on two tasks 1) detecting cyberbullying as a binary classification problem, and 2) detecting cyberbullying-related participant roles as a multi-class classification problem. ## methods our research is guided by two tasks, which focus on evaluating the performance of models that could classify whether a given post is, 1. cyberbullying-related or not, and 2. if cyberbullying-related, predicting the role of the user who authored that post. ## results and discussion evaluation metric. to evaluate our models and compare the performance with baselines, we use metrics similar to van hee et al. (2018): 1) f1-score: the harmonic mean of precision and recall and 2) error rate: 1-recall of the class. baseline. we use the best system of @xcite as our baseline to compare our models. this baseline used feature combinations such as subjectivity lexicons, character n-grams, term lists, and topic models. ## conclusions this paper proposes an approach to classify cyberbullying and associated roles (e.g., harasser, victim) as a novel contribution to enhance automated cyberbullying detection. cyberbullying is a growing social problem that inflicts detrimental impacts on online users. the identification of roles is a valuable contribution to future research as it can prompt closer monitoring of bullies and implicitly help victims through potential prevention. currently, our approaches to identifying cyberbullying related roles focus only on individual posts on a forum. in our future work, we aim to expand this further by considering an entire discussion and the discourse relationships between the posts within the considered discussion. this will enable us to get a better understanding of the roles played by different users in a discussion. moreover, we intend to integrate cyberbullying and role classification as a single model and optimise performance further to provide an effective solution to the cyberbullying problem.