Exploring Bystanders' Roles in Labeled Cyberbullying Threads on Twitter: A preliminary analysis
Haifa Saleh Alfurayj, Syaheerah Lebai Lutfi · 2023
This study presents findings from an anal-ysis of a newly developed corpus, CYBY23, focused on cyberbullying, aiming to comprehensively examine labeled cyberbullying threads on the social media platform Twitter, with a specific emphasis on the role of bystanders. Previous corpora used for automatic cyberbullying detection have primarily focused on the main posts, disregarding the threaded responses. Con-sequently, these studies have overlooked valuable infor-mation regarding the involvement of bystanders, which is crucial for enhancing the accuracy of cyberbullying detection. This study addresses this gap by incorpo-rating bystander roles within the corpus, resulting in significant impact on annotators' perception and classification of cyberbullying instances. The findings suggest promising prospects for improved automated cyberbullying detection. Notably, the most frequently observed bystander roles align with the content of the main post. Surprisingly, impartial bystanders are most prevalent in cyberbullying threads characterized by high levels of aggression. This article provides a detailed analysis of the annotation process and examines the influence of bystanders roles in greater depth.