Despite heavy investments by local and national governments, crime continues to remain a serious problem in the society. Current state-of-the-art is below average and relies heavily on crime intelligence and efforts of human personnel across several law enforcement agencies.

Over the last few years, crowdsourcing has been promoted worldwide through several Suspicious Activity Reporting (SAR) campaigns. However, such systems have proven to be largely unsuccessful owing to complicated reporting processes, excessive noise, limited authorized personnel, lack of motivation and one-way communication.

To address these issues and improve counterintelligence, this thesis proposes two approaches: (i) Combination of Machine Learning and Natural Language Processing to promote generic reporting while automating the process of crime detection, summarisation and delegation (ii) Improving crowd intelligence by enabling better citizen involvement through newer information sharing techniques, sophisticated sensors and responsive feedback. In this thesis, we also investigate lone wolf terrorism as a special case of crime, and evaluate application of our proposed methods for its mitigation.