{'fraction_non_alphanumeric': 0.00998003992015968, 'fraction_numerical': 0.0, 'mean_word_length': 5.275, '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}}
Temporal prediction of retweet count

In recent years Twitter became the social network for information sharing and spreading. By retweeting, users spreading information and build cascades of information pathways. In this paper we investigate the possibility predicting the future popularity of emerging retweet cascades immediately after the message appears or in the very early stage of retweeting it. We evaluate our algorithms on four sets of tweets that include the text and the follower network.