{'fraction_non_alphanumeric': 0.01761252446183953, 'fraction_numerical': 0.0, 'mean_word_length': 4.885057471264368, '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}}
Identification of shares containing offensive charge in social media

The increasing use of social media brings with it several problems. The usage of insulting content on social networks, for some people or institutions, constitutes crimes. In this case, the study is about analyzing text content shared by users in the social media with the help of Naive Bayes Method and Vector Space Model from Machine Learning methods, and whether or not these shares contain insults, if any, and at what rate they contain.