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We perform an in-depth series of experiments that utilize multiple sets of textual features, different text classification models, including 5 machine learning techniques, 10 basic text preprocessing methods, 2 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.<\/jats:p>","DOI":"10.1145\/3779415","type":"journal-article","created":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:44:41Z","timestamp":1765547081000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Early Detection of Anorexia in Blog Posts Written in English"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4834-1272","authenticated-orcid":false,"given":"Yaakov","family":"HaCohen-Kerner","sequence":"first","affiliation":[{"name":"Department of Computer Science, Jerusalem College of Technology (Lev Academic Center), Jerusalem, Israel"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5728-3473","authenticated-orcid":false,"given":"Natan","family":"Manor","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Jerusalem College of Technology (Lev Academic Center), Jerusalem, Israel"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2285-1259","authenticated-orcid":false,"given":"Michael","family":"Goldmeier","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Jerusalem College of Technology (Lev Academic Center), Jerusalem, Israel"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9725-4549","authenticated-orcid":false,"given":"Eytan","family":"Bachar","sequence":"additional","affiliation":[{"name":"Department of Psychology, The Hebrew University of Jerusalem, Jerusalem, Israel"}]}],"member":"320","published-online":{"date-parts":[[2026,1,13]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1037\/prj0000130"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(18)32279-7"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(07)61414-7"},{"issue":"1","key":"e_1_3_3_5_2","first-page":"95","article-title":"Classification of anxiety disorders using machine learning methods: A literature review","volume":"4","author":"Arif M.","year":"2020","unstructured":"M. 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