{'fraction_non_alphanumeric': 0.015, 'fraction_numerical': 0.00125, 'mean_word_length': 5.459677419354839, '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}}
Fake Tweet Detection Using Machine Learning

Nowadays, online social media plays a vital role in real-world applications with potentially positive and negative effects. When some event has occurred, many of us discuss it online through social networking. Once sudden events happen there’s conjointly faux news that’s broadcasted that makes confusion because of the character of the events. A replacement and possible direction is confirming tweets at the side of media contents mistreatment data processing techniques. The planned framework consists of 2 major components: classifiers and neural networks. A classifier is responsible for classifying the tweet content and constructing a feature set whereas the neural network is to provide previous tweet credibility predictions stored in the memory.