{'fraction_non_alphanumeric': 0.017733230531996914, 'fraction_numerical': 0.0, 'mean_word_length': 6.0162162162162165, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Machine Learning Framework to Detect and Document Text-Based Cyberstalking

Cyberstalking is becoming a social and international problem, where cyberstalkers utilise the Internet to target individuals and disguise themselves without fear of any consequences. Several technologies, methods, and techniques are used by perpetrators to terrorise victims. While spam email filtering systems have been effective by applying various statistical and machine learning algorithms, utilising text categorization and filtering to detect textand email-based cyberstalking is an interesting new application. There is also the need to gather evidence by the victim. To this end we discuss a framework to detect cyberstalking in messages; short message service, multimedia messaging service, chat, instance messaging and emails, and as well as to support documenting evidence. Our framework consists of five main modules: a detection module which detects cyberstalking using message categorisation; an attacker identification module based on cyberstalkers’ previous messages history, personalisation module, aggregator module and messages and evidence collection module. We discuss our ongoing work and how different text categorization and machine learning approaches can be applied to identify cyberstalkers.