{'fraction_non_alphanumeric': 0.01977401129943503, 'fraction_numerical': 0.0, 'mean_word_length': 5.365269461077844, '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}}
Post Classification in the Social Networks using the Map-reduce Algorithm

Wrongdoing is increasing through social media. Detecting them requires highlighting the most interesting topics in the posts. This essential part in the characterization of social network users could be done by a classification of posts. For this, we use a tuple of keywords and the Map-reduce algorithm for data collection and extraction. The main purpose is to achieve success on software realization which will establish a network between social networks to extract data and to speed up the classification of posts. The proposed method consists of verifying a sequence of keywords in the posts, following a grammar in order to determine classes. It allows the categorization of posts and monitoring of social networks. The categorization facilitates research of a particular post containing specific words. Thus, we contribute to increase capacity for wrongdoing prevention and strengthening cyber-security. Keywords—Big Data; map-reduce; social network; cybersecurity; classification