{'fraction_non_alphanumeric': 0.017013232514177693, 'fraction_numerical': 0.001890359168241966, 'mean_word_length': 5.229411764705882, '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}}
Text Mining based Rumor Detection on Twitter data

- Rumors are misleading information that is not sustained at the time of circulation and are not true at the time of verification. In other words, Rumors are a set of linguistic, symbolic or tactile propositions whose veracity is not quickly or ever confirmed. As the popularity of social networking sites has increased, in recent years, incorrect information and rumors have circulated widely causing a significant influence on people’s lives. Microblogging platforms are an excellent way to spread rumors and automatically disprove them in critical situations. Existing approaches to detecting rumors have depended on hand-crafted features for utilizing machine learning algorithms, which necessitates a significant amount of manual effort. In this work, we have used stylometric and word vector features and put them into machine learning models. These features are extracted from the twitter-16 dataset and by applying SVM, we have attained the highest accuracy in comparison to existing newest studies.