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Surveys02:16

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Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.
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Detecting Perceived Unfair Treatment Among US College Students Using Mobile Sensing: Pilot Machine Learning Study.

Yiyi Ren1, Raghu Mulukutla2, Jennifer Mankoff3

  • 1Information School, University of Washington, Seattle, WA, United States.

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|October 31, 2025
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Summary
This summary is machine-generated.

Mobile sensing can passively detect perceived unfair treatment (PUT) in college students, identifying behavioral patterns linked to these experiences. This technology offers potential for timely mental health interventions.

Keywords:
anomaly detectiondigital phenotypingmental healthmobile healthpassive sensingperceived discrimination

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Area of Science:

  • Digital Phenotyping
  • Machine Learning in Mental Health
  • Mobile Sensing Applications

Background:

  • Experiences of unfair treatment are linked to negative health outcomes in college students.
  • Current detection methods rely on self-reports, limiting timely intervention.
  • No prior research has explored passive mobile sensing for detecting perceived unfair treatment (PUT).

Purpose of the Study:

  • To investigate the feasibility of using mobile sensing for passive detection of daily PUT experiences.
  • To develop and evaluate machine learning models for PUT detection.
  • To establish a benchmark for future research in this area.

Main Methods:

  • Collected data from 201 undergraduate students over two 10-week terms.
  • Utilized ecological momentary assessment (EMA) for daily self-reported PUT.
  • Implemented user-independent supervised classification and user-dependent anomaly detection models.

Main Results:

  • User-dependent anomaly detection models, particularly LSTM-AE, showed superior performance in detecting PUT.
  • Key behavioral patterns identified include changes in mobility, sleep, and screen time.
  • LightGBM and Random Forest models outperformed baselines in user-independent classification.

Conclusions:

  • Mobile sensing demonstrates potential for passive detection of PUT in college students.
  • Identified behavioral patterns can inform the development of targeted interventions.
  • Mobile technology offers opportunities for timely support to improve student well-being.