All Users are (Not) Created Equal: Predictors Vary for Different Forms of Facebook Non/use

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Eric P. S. Baumer, Patrick Skeba, Shion Guha, and Geri Gay. (2019). All Users are (Not) Created Equal: Predictors Vary for Different Forms of Facebook Non/use. Proceedings of the ACM Human-Computer Interaction 2, CSCW.

Abstract

Relatively little work has empirically examined use and non-use of social technologies as more than a dichotomous binary, despite increasing calls to do so. This paper compares three different forms of non/use that might otherwise fall under the single umbrella of Facebook “user”: (1) those who have a current active account; (2) those who have deactivated their account; and (3) those who have considered deactivating but not actually done so. A subset of respondents (N=256) from a larger, demographically representative sample of internet users completed measures for usage and perceptions of Facebook, Facebook addiction, privacy experiences and behaviors, and demographics. Multinomial logistic regression modeling shows four specific variables as most predictive of a respondent’s type: negative effects from “addictive” use, subjective intensity of Facebook usage, number of Facebook friends, and familiarity with or use of Facebook’s privacy settings. These findings both fill gaps left by, and help resolve conflicting expectations from, prior work. Furthermore, they demonstrate how valuable insights can be gained by disaggregating “users” based on different forms of engagement with a given technology.

DOI

Who is the “Human” in Human-Centered Machine Learning: The Case of Predicting Mental Health from Social Media

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Stevie Chancellor, Eric P. S. Baumer, and Munmun De Choudhury. (2019). Who is the “Human” in Human-Centered Machine Learning: The Case of Predicting Mental Health from Social Media. Proceedings of the ACM Human-Computer Interaction 2, CSCW.

Abstract

“Human-centered machine learning” (HCML) combines human insights and domain expertise with data-driven predictions to answer societal questions. This area’s inherent interdisciplinarity causes tensions in the obligations researchers have to the humans whose data they use. This paper studies how scientific papers represent human research subjects in HCML. Using mental health status prediction on social media as a case study, we conduct thematic discourse analysis on 55 papers to examine these representations. We identify five discourses that weave a complex narrative of who the human subject is in this research: Disorder/Patient, Social Media, Scientific, Data/Machine Learning, and Person. We show how these five discourses create paradoxical subject and object representations of the human, which may inadvertently risk dehumanization. We also discuss the tensions and impacts of interdisciplinary research; the risks of this work to scientific rigor, online communities, and mental health; and guidelines for stronger HCML research in this nascent area.

DOI