{'fraction_non_alphanumeric': 0.020338983050847456, 'fraction_numerical': 0.00847457627118644, 'mean_word_length': 5.025510204081633, '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}}
Finding the Most Influential User of a Specific Topic on the Social Networks

The Study on `Maximizing the Spread of Influence through a Social Network` has been strongly attracted attentions recently. One of the most important problems is figuring those who are able to make the strongest influence on the others in spreading information based on a specific topic. We would like to build a system to support viral marketing on social networks and to promote the topic modeling, the propagation model and propagation algorithm to find out the most influential group of users of each topic. The system consists of several steps, such as word extracting, data processing and finding the most influential users in exchanged topics. We especially focus on calculating the users’ influence probabilities via an action log file, using the propagation model – TLT (Topic-aware Linear Threshold) and the propagation algorithm – CELF (Cost Effective Lazy Forward). Furthermore, we also experiment our model with Enron email data, which includes 11,177 emails exchanged among 147 users and estimated in 50 topics. We have received many useful topics and the most influential group of users.