Content Analysis of Online Documents on Identity Theft Using Latent Dirichlet Allocation Algorithm

Devine Grace D. Funcion


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 to dig 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 theme was generated using the R-programming software for data analysis,and literature supports discussion.


Cybercrime; identity theft; latent dirichlet allocation algorithm; unsupervised machine learning


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