Abstract
Victims of identity theft are growing as technology progresses. The increasing number of digital transactions (i.e., credit cards, online payment, banking) have become vulnerable to the cybercrime. Victims suffer from social and economic sabotage due to identity fraud. It is vital then to dig into available documents of the countries which have the most cases of identity theft as shown in Google Trends for the past five years. Hence, this work is anchored on web mining technique and utilizes the unsupervised machine learning with the application of Latent Dirichlet Allocation Algorithms for content analysis of online information related to identity theft. The five identified underlying themes were generated using the R-programming software for data analysis, and literature support for the discussion.
Published Version
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