Bystanders Unveiled: Introducing a Comprehensive Cyberbullying Corpus with Bystander Information
Haifa Saleh Alfurayj, Ng Sui Yee, Syaheerah Lebai Lutfi · 2023
This paper introduces a new cyberbullying dataset, CYBY23, that includes Twitter threads containing both the main posts and the replies from bystanders. The dataset is organized based on conversation ID and consists of 112 threads, totaling around 639 tweets. The unique aspect of this dataset is the inclusion of labels for bystanders' roles, which provides a comprehensive understanding of the bullying incident and helps identify the level of aggressiveness in cyberbullying. This type of information is not available in existing datasets that only label isolated tweets. By incorporating bystanders' roles, annotators gain a deeper understanding of real-world scenarios, leading to improved machine learning performance and better classification of cyberbullying. The dataset is freely available, promoting collaboration among researchers, ensuring result reliability, and enabling the reuse of Twitter datasets. It also offers a cost-effective way for non-technical researchers to leverage Twitter data in their scientific investigations.