{'fraction_non_alphanumeric': 0.02, 'fraction_numerical': 0.0, 'mean_word_length': 5.014423076923077, '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': 2, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Predicting User Quality in Anonymous Chat Networks

Online social networks like Facebook and Twitter have been multiplying over the past few years, along with the amount of data that these sites handle and process. A lot of work has been done to optimize the quality and informativity of the data that reaches their users, such as Facebook’s EdgeRank newsfeed calculation, or friend and follower suggestions. However, little such analysis and optimization has been done for less conventional platforms such as anonymous chat networks, which have much to gain from this kind of work. In particular, being able to predict users of poor “quality” is of paramount importance as they rarely have long conversations and are therefore over represented in the matching queue, which in turn affects the conversation quality of a large set of users. Past attempts at anonymous social networks (i.e. Chatroulette, etc.) have failed because of this very problem, as users of high quality leave the network after repeated interactions with suboptimal users. Offering a better filter of users would allow anonymous networks to create sub queues, or queues populated almost entirely of one type of user, enabling a better user experience and higher activation rate.