{'fraction_non_alphanumeric': 0.02551984877126654, 'fraction_numerical': 0.0, 'mean_word_length': 5.303571428571429, '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}}
Social Media account linkage using user-generated geo-location data

Since cyber offenders often create multiple accounts on different Social Media Platforms (SMPs) to serve their disparate malicious intentions, law enforcement agency investigators often encounter the task of recognizing all the accounts used by the same person across SMPs, i.e., account linkage (AL). Although a number of techniques have been proposed for AL, their performance may be degraded by factors such as information asymmetry, poor data quality, data unavailability, as well as application scope limitation. In this paper, we solve AL in an unsupervised manner by utilizing user-generated geo-location data in SMPs, which is more robust than common clues used in existing techniques. A co-clustering-based AL framework is proposed in which account clusterings in temporal and spatial dimensions are carried out synchronously and enhance the results of each other. Experiments carried out on a real-world dataset demonstrate the feasibility and validity of the proposed framework.