Using SNA Centrality Metrics to Detect Suspicious Social Media Users to Aid Law Enforcement Agencies in Kenya
Investigation of social media using social network theory is a new powerful tool that is expected to aid and ease law enforcement agencies in targeting cybercriminals in multi-faceted ways in this ever evolving digital landscape. It is against this backdrop that this study focused on identifying, investigating and detecting individuals based on selected users of Facebook and Twitter social media platforms. The objective of the study was to demonstrate how Social Network Analysis (SNA) can be employed as an investigate tool to mine, analyse data from selected online social media users and present digital forensic evidence to aid law enforcement in Kenya. Particularly, the study aimed at identifying high degree nodes in the network using network metrics. Social network analysis experimental research design was employed in this study. The sample size of the respondents was arrived at by employing snowball sampling procedure and particularly Yamane’s formula of calculating sample size. The respondents were guided to create pseudo-online parody accounts in various social media platforms, which were later used to carry out the online data mining from the selected respondents to aid in social network analysis. Data mining and analysis was done using NodeXL. Results were presented in form of social network centrality metrics or measures on egocentric networks. The outcome of this study gives a new insight and techniques that can help law enforcement agencies and related stakeholders to identify or detect important individuals and roles they play in a given network. The findings presented in this research principally demonstrates how law enforcement agencies can utilize this technique in identifying and tracking suspicious characters and ultimately help in maintaining law and order.