A. Zaman, Boyu Zhang, V. Silenzio, E. Hoque, Henry A. Kautz
{"title":"Individual-level Anxiety Detection and Prediction from Longitudinal YouTube and Google Search Engagement Logs","authors":"A. Zaman, Boyu Zhang, V. Silenzio, E. Hoque, Henry A. Kautz","doi":"10.36190/2021.41","DOIUrl":"https://doi.org/10.36190/2021.41","url":null,"abstract":"Anxiety disorder is one of the world's most prevalent mental health conditions, arising from complex interactions of biological and environmental factors and severely interfering one's ability to lead normal life activities. Current methods for detecting anxiety heavily rely on in-person interviews, which can be expensive, time-consuming, and blocked by social stigmas. In this work, we propose an alternative method to identify individuals with anxiety and further estimate their levels of anxiety using personal online activity histories from YouTube and the Google Search engine, platforms that are used by millions of people daily. We ran a longitudinal study and collected multiple rounds of anonymized YouTube and Google Search logs from volunteering participants, along with their clinically validated ground-truth anxiety assessment scores. We then developed explainable features that capture both the temporal and contextual aspects of online behaviors. Using those, we were able to train models that (i) identify individuals having anxiety disorder with an average F1 score of 0.83 and (ii) assess the level of anxiety by predicting the gold standard Generalized Anxiety Disorder 7-item scores (ranges from 0 to 21) with a mean square error of 1.87 based on the ubiquitous individual-level online engagement data. Our proposed anxiety assessment framework is cost-effective, time-saving, scalable, and opens the door for it to be deployed in real-world clinical settings, empowering care providers and therapists to learn about anxiety disorders of patients non-invasively at any moment in time.","PeriodicalId":217298,"journal":{"name":"arXiv: Human-Computer Interaction","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116361451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"White Noise from the White Goods? Privacy by Design for Ambient Domestic Computing","authors":"Lachlan D. Urquhart","doi":"10.2139/SSRN.2865738","DOIUrl":"https://doi.org/10.2139/SSRN.2865738","url":null,"abstract":"Within this chapter we consider the emergence of ambient domestic computing systems, both conceptually and empirically. We critically assess visions of post-desktop computing, paying particular attention to one contemporary trend: the internet of things (IoT). We examine the contested nature of this term, looking at the historical trajectory of similar technologies, and the regulatory issues they can pose, particularly in the home. We also look to the emerging regulatory solution of privacy by design, unpacking practical challenges it faces. The novelty of our contribution stems from a turn to practice through a set of empirical perspectives. We present findings that document the practical experiences and viewpoints of 13 leading experts in technology law and design.","PeriodicalId":217298,"journal":{"name":"arXiv: Human-Computer Interaction","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116585861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}