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Predicting Sociodemographic Attributes from Mobile Usage Patterns: Applications and Privacy Implications
16 references·0 citing·60 second hopsampled
Predicting Sociodemographic Attributes from Mobile Usage Patterns: Applications and Privacy Implications (2023)Predicting Sociodemographic Attributes fr…Smartphone usage among older adults (2021)Smartphone usage among older …Predicting customer’s gender and age depending on mobile phone data (2019)Predicting customer’s gender …Do smartphone usage scales predict behavior? (2019)Do smartphone usage scales pr…The Role of User Privacy Concerns in Shaping Competition Among Platforms (2018)The Role of User Privacy Conc…Measuring smartphone usage and task switching with log tracking and self-reports (2018)Measuring smartphone usage an…Aspects of Data Ethics in a Changing World: Where Are We Now? (2018)Aspects of Data Ethics in a C…A new aspect on P2P online lending default prediction using meta-level phone usage data in China (2018)A new aspect on P2P online le…Predicting Smartphone Operating System from Personality and Individual Differences (2016)Predicting Smartphone Operati…Predicting poverty and wealth from mobile phone metadata (2015)Predicting poverty and wealth…Differential innovation of smartphone and application use by sociodemographics and personality (2014)Differential innovation of sm…A study of age and gender seen through mobile phone usage patterns in Mexico (2014)A study of age and gender see…Privacy as part of the app decision-making process (2013)Privacy as part of the app de…Gender differences in mediated communication: Women connect more than do men (2013)Gender differences in mediate…On the relationship between socio-economic factors and cell phone usage (2012)On the relationship between s…Predicting consumer decisions to adopt mobile commerce: Cross country empirical examination between China and Malaysia (2011)Predicting consumer decisions…The Personalization Privacy Paradox: An Empirical Evaluation of Information Transparency and the Willingness to be Profiled Online for Personalization1 (2006)The Personalization Privacy P…A "nutrition label" for privacy (2009)A methodology for empirical analysis of permission-based security models and its application to android (2010)Android permissions (2012)Leakiness and creepiness in app space (2014)A Design Space for Effective Privacy Notices* (2018)Exploring How Privacy and Security Factor into IoT Device Purchase Behavior (2019)Sex Differences in Social Behavior: A Social-Role Interpretation. 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References

  • 2006The Personalization Privacy Paradox: An Empirical Evaluation of Information Transparency and the Willingness to be Profiled Online for Personalization11,249 cited
  • 2015Predicting poverty and wealth from mobile phone metadata700 cited
  • 2011Predicting consumer decisions to adopt mobile commerce: Cross country empirical examination between China and Malaysia584 cited
  • 2013Gender differences in mediated communication: Women connect more than do men366 cited
  • 2019Do smartphone usage scales predict behavior?326 cited
  • 2013Privacy as part of the app decision-making process315 cited
  • 2018Measuring smartphone usage and task switching with log tracking and self-reports201 cited
  • 2021Smartphone usage among older adults186 cited
  • 2018Aspects of Data Ethics in a Changing World: Where Are We Now?131 cited
  • 2018The Role of User Privacy Concerns in Shaping Competition Among Platforms126 cited
  • 2018A new aspect on P2P online lending default prediction using meta-level phone usage data in China107 cited
  • 2016Predicting Smartphone Operating System from Personality and Individual Differences92 cited
  • 2014Differential innovation of smartphone and application use by sociodemographics and personality88 cited
  • 2012On the relationship between socio-economic factors and cell phone usage

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85 cited
  • 2014A study of age and gender seen through mobile phone usage patterns in Mexico43 cited
  • 2019Predicting customer’s gender and age depending on mobile phone data42 cited
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