{'fraction_non_alphanumeric': 0.020640569395017794, 'fraction_numerical': 0.0028469750889679717, 'mean_word_length': 4.882845188284519, '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}}
CC Prediction in the Enron Corpus

Email is the most popular communication tool on the web. To improve the way we handle email messages, machine learning techniques have been proposed in different areas, from adaptive spam filtering to automated message foldering (i.e., predicting the correct folder to store a message). One of the ideas recently proposed is to automatically predict the recipients of an already composed message; a problem also referred to as CC Prediction problem. The goal of this problem is to predict the most likely recipients for a message, given its current text and given the email addresses already specified. If successfully automated, this idea can be a valuable addition to email clients, particularly in the large corporations. It can prevent a user from forgetting to add an important collaborator or manager as recipient. Also, it can be used to identify people in an organization that are working in a similar topic or project, or to find people with appropriate expertise or skills. In this work we investigate this problem using the textual contents and social network information features from a large real world collection of email messages, the Enron Email corpus [ 14]. Using a classification-based reranking scheme to combine the two different types of features, experiments indicate that we can correctly predict CC-recipients in more than 56% of the test cases.