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Predicting Sociodemographic Attributes from Mobile Usage Patterns: Applications and Privacy Implications.

Rouzbeh Razavi1, Guisen Xue1, Ikpe Justice Akpan2

  • 1Department of Management and Information Systems, Kent State University, Kent, Ohio, USA.

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Summary
This summary is machine-generated.

Predicting user demographics like gender and age from mobile usage data is possible. Mobile footprints offer insights but raise privacy concerns, requiring careful consideration of ethical implications.

Keywords:
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Area of Science:

  • Computer Science
  • Sociology
  • Human-Computer Interaction

Background:

  • Mobile devices generate extensive digital footprints.
  • These footprints can serve as proxies for user characteristics, including demographics.
  • Predicting demographics from mobile usage offers benefits for personalization and market research.

Purpose of the Study:

  • To assess the accuracy of predicting sociodemographic attributes (age, gender, income, education) from mobile usage metadata.
  • To quantify the predictive power of different demographic attributes.
  • To explore practical applications and privacy implications of demographic inference.

Main Methods:

  • Utilized machine learning algorithms.
  • Analyzed mobile usage data from 235 demographically diverse users.
  • Examined prediction accuracy for age, gender, income, and education.

Main Results:

  • Gender prediction achieved the highest accuracy (balanced accuracy = 0.862).
  • Education level prediction was more challenging (balanced accuracy = 0.719).
  • Age and income were classifiable above/below thresholds with acceptable accuracy.

Conclusions:

  • Mobile usage data can predict certain demographic attributes with varying accuracy.
  • Findings highlight potential benefits for targeted services and market research.
  • Ethical considerations, including privacy and discrimination risks, are crucial.