Social Network Analysis For Bullying Detection
K. Geethika, E. Sai Srikar, A. Venkatesh, G. Rakesh Reddy
Social Network Analysis is a data-driven approach that offers considerable promise to identify and mitigate bullying dynamics within social networks. The present study explores the application of SNA data-driven methods to investigate the prevalence and characteristics of bullying dynamics to validate and enhance intervention measures. According to the study results, the SNA method has a high potential for properly identifying the bullying dynamics, understanding their diversity, and intervening in a timely manner. The respondents’ data was collected from social networks consisting of online forums, social media sites, and messaging apps, and data was analyzed using multiple pre-processing methods. At the first stage of the research, the data was collected based on message, comment, like, and sharing analysis. Then it was pre- processed to remove unwanted fields using meta-tagging tools to correctly analyze and retrieve the information. After the necessary fields were extracted, SNA tools read this data and determine the network connection between users, and all controlled communities, cliques, and influential users were identified. Finally, collaboration with machine learning models has significantly improved the detection accuracy as it makes it possible to model bullying based on distinct interaction patterns and language patterns. More specifically, the supervised learning algorithms trained on annotated sets of data can classify specific interactions as instances and non- instances of bullying. Ethical considerations are an essential aspect of the research as well. They guarantee the responsible use of data, protect the privacy of users, and align the study with the ethical codes. Thus, there are consent procedures regarding data collection and takes to de-anonymize and aggregate the information.
Article Details
Unique Paper ID: 164486

Publication Volume & Issue: Volume 10, Issue 12

Page(s): 817 - 824
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