{'fraction_non_alphanumeric': 0.015723270440251572, 'fraction_numerical': 0.0, 'mean_word_length': 5.409395973154362, '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}}
Identifying Tweets with Fake News

The extensive use of social media has tremendous impact on culture, business, and politics on the world at large with potentially positive and negative effects. For example, social media coverage of crisis events may be used by authorities for effective disaster management or by malicious entities to spread rumors and fake news for financial or political benefit. Considering the harmful consequences of fake news in social media, there is a profound need to detect false information, control and/or prevent it from spreading. In this paper, we propose an advanced framework to identify tweets with fake news contents using techniques including statistical analysis of Twitter user account, reverse image searching, cross verification of fake news sources, and data mining. Experimental results on a large miscellaneous events dataset demonstrate the effectiveness of our proposed approach in identifying fake tweets.