A Survey of Relevant Text Mining Technology

TL;DR

This survey reviews current text mining techniques for social media and cybercriminal analysis, highlighting challenges like data variability, noise, and deception, and discusses future research directions.

Contribution

It provides a comprehensive overview of existing methods and identifies gaps in addressing domain-specific challenges in social media text mining.

Findings

Current methods address some challenges but lack robustness against noise and deception.

Identifies key areas needing further research, such as adversarial behaviour detection.

Highlights the importance of domain-specific adaptations in text mining.

Abstract

Recent advances in text mining and natural language processing technology have enabled researchers to detect an authors identity or demographic characteristics, such as age and gender, in several text genres by automatically analysing the variation of linguistic characteristics. However, applying such techniques in the wild, i.e., in both cybercriminal and regular online social media, differs from more general applications in that its defining characteristics are both domain and process dependent. This gives rise to a number of challenges of which contemporary research has only scratched the surface. More specifically, a text mining approach applied on social media communications typically has no control over the dataset size, the number of available communications will vary across users. Hence, the system has to be robust towards limited data availability. Additionally, the quality of the data cannot be guaranteed. As a result, the approach needs to be tolerant to a certain degree of linguistic noise (for example, abbreviations, non-standard language use, spelling variations and errors). Finally, in the context of cybercriminal fora, it has to be robust towards deceptive or adversarial behaviour, i.e. offenders who attempt to hide their criminal intentions (obfuscation) or who assume a false digital persona (imitation), potentially using coded language. In this work we present a comprehensive survey that discusses the problems that have already been addressed in current literature and review potential solutions. Additionally, we highlight which areas need to be given more attention.