{'fraction_non_alphanumeric': 0.018138801261829655, 'fraction_numerical': 0.0, 'mean_word_length': 5.441624365482234, '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}}
Dark Web portal overlapping community detection based on topic models

A hot research topic is the study and monitoring of online communities. Of course, homeland security institutions from many countries are using data mining techniques to perform this task, aiming to anticipate and avoid a possible menace to local peace. Tools such as social networks analysis and text mining have contributed to the understanding of these kinds of groups in order to develop counter-terrorism applications. A key application is the discovery of sub-communities of interests which main topic could be a possible homeland security threat. However, most algorithms detect disjoint communities, which means that every community member belongs to a single community. Thus, final conclusions can be omitting valuable information which leads to wrong results interpretations. In this paper, we propose a novel approach to combine traditional network analysis methods for overlapping community detection with topic-model based text mining techniques. Afterwards, we developed a sub-community detection algorithm that allow each member belong more than one sub-community. Experiments were performed using an English language based forum available in the Dark Web portal (islamicAwakening).