Early Detection of Anorexia in Blog Posts Written in English

This study concentrates on identifying girls with anorexia nervosa through English social media text analysis. A dataset was created comprising 100 blog posts authored by females who have anorexia and another 100 posts written by females likely without an eating disorder. A psychology professor who is an international expert on anorexia confirmed the collected posts. We perform an in-depth series of experiments that utilize multiple sets of textual features, different text classification models, including five machine learning techniques, ten basic text preprocessing methods, two feature filtering methods, and parameter optimization procedures. The best accuracy result of 91.73% was obtained by the random forest machine learning method using a combination of 16 feature sets derived by a heuristic process of combining feature sets and parameter tuning. This result is 4.48% higher than the baseline (87.25%). Among the 16 feature sets, 10 are content-based, containing features that, to one degree or another, describe anorexic girls. A relatively high number of feature sets (6 out of 16) were style-based, while two were sentiment-based. A notable recurring observation across various classification studies, including the present study, is that traditional machine learning techniques tend to outperform deep learning methods. We also present a comparison of the results and findings of this study in English and those of a similar study performed by us using a dataset in Hebrew.