{'fraction_non_alphanumeric': 0.016684045881126174, 'fraction_numerical': 0.0, 'mean_word_length': 5.2745098039215685, '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}}
Utilizing Natural Honeypots for Efficiently Labeling Astroturfer Profiles

Astroturfing is the practice of using a fake online social media (OSM) profile in order to influence public opinion, while giving the impression that the profile belongs to an authentic human user. In attempting to train a classifier for discriminating between authentic users and astroturfers, a labeled dataset must first be arranged. The labeling is generally done manually, by human judges, on a collection of profiles garnered from the social media network. However, the fact that any randomly collected set of profiles will statistically contain a small proportion of astroturfers, renders this process inefficient: a lot of time and effort is invested on manually labeling lots of data, while producing only a small set of astroturfer profiles. We present here a method for quickly and efficiently collecting a data set for manual labeling, with a high percent of astroturfers.