{'fraction_non_alphanumeric': 0.009554140127388535, 'fraction_numerical': 0.0, 'mean_word_length': 5.484536082474227, '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}}
Characterizing the Visual Social Media Environment of Eating Disorders

Eating disorders are often exacerbated by exposure to triggering images on social media. Standard approaches to filtering of social media by detecting hashtags or keywords are difficult to keep accurate because those migrate or change over time. In this work we present proof-of-concept demonstrations to show that Deep Learning classification algorithms are effective at classifying images related to eating disorders. We discuss some of the challenges in this domain and show that careful curation of the training data improves performance substantially.