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Recognizing these emotional states is essential for implementing effective warning systems for social networks. However, detecting emotions during passive social media use---the predominant mode of engagement---is challenging. We introduce the first predictive model that estimates user emotions during passive social media consumption alone. We conducted a study with 29 participants who interacted with a controlled social media feed. Our apparatus captured participants' behavior and their physiological signals while they browsed the feed and filled out self-reports from two validated emotion models. Using this data for supervised training, our emotion classifier robustly detected up to 8 emotional states and achieved 83% peak accuracy to classify affect. Our analysis shows that behavioral features were sufficient to robustly recognize participants' emotions. It further highlights that within 8 seconds following a change in media content, objective features reveal a participant's new emotional state. We show that grounding labels in a componential emotion model outperforms dimensional models in higher-resolutional state detection. Our findings also demonstrate that using emotional properties of images, predicted by a deep learning model, further improves emotion recognition.<\/jats:p>","DOI":"10.1145\/3659606","type":"journal-article","created":{"date-parts":[[2024,5,15]],"date-time":"2024-05-15T12:20:41Z","timestamp":1715775641000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Detecting Users' Emotional States during Passive Social Media Use"],"prefix":"10.1145","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7162-0133","authenticated-orcid":false,"given":"Christoph","family":"Gebhardt","sequence":"first","affiliation":[{"name":"Department of Computer Science, ETH Z\u00fcrich, Zurich, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0215-3432","authenticated-orcid":false,"given":"Andreas","family":"Brombach","sequence":"additional","affiliation":[{"name":"Department of Computer Science, ETH Z\u00fcrich, Zurich, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1351-1606","authenticated-orcid":false,"given":"Tiffany","family":"Luong","sequence":"additional","affiliation":[{"name":"Department of Computer Science, ETH Z\u00fcrich, Zurich, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5068-3474","authenticated-orcid":false,"given":"Otmar","family":"Hilliges","sequence":"additional","affiliation":[{"name":"Department of Computer Science, ETH Z\u00fcrich, Zurich, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9655-9519","authenticated-orcid":false,"given":"Christian","family":"Holz","sequence":"additional","affiliation":[{"name":"Department of Computer Science, ETH Z\u00fcrich, Zurich, Switzerland"}]}],"member":"320","published-online":{"date-parts":[[2024,5,15]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2021.12.133"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1108\/itp-11-2020-0753"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/ppc.12803"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1054972.1055040"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2675133.2675297"},{"volume-title":"Electrodermal activity","author":"Boucsein Wolfram","key":"e_1_2_1_6_1","unstructured":"Wolfram Boucsein. 2012. 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