Building of safer urban hubs: Insights from a comparative study on cyber telecom scams and early warning design

Abstract

As digital technologies and smart city development continue to grow, the threats of cybercrime and scams have become increasingly salient for city managers, businesses, and citizens worldwide. With a less effective data privacy protection, the number of new types of scams that precisely target victims is increasing. We collected 6,871 crawl fraud cases from news reports and local websites between October 2018 and December 2021 in Mainland China and Hong Kong. We generated 2,747 messages from news and open-source message datasets on GitHub. Based on these novel datasets, we conducted a comparative analysis of cyber telecom scams between Mainland China and Hong Kong and identified victim profiles using adata technology and target group index analysis. Furthermore, we developed a message-classifier scam alert model using data mining and machine learning algorithms. Our study provides valuable insights and essential implications on how data analytics can support future antifraud initiatives and help cities build safer urban hubs.