Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Malmi, Eric; Weber, Ingmar (2016)
Languages: English
Types: Preprint
Subjects: Computer Science - Social and Information Networks

Classified by OpenAIRE into

mesheuropmc: mental disorders
Understanding the demographics of app users is crucial, for example, for app developers, who wish to target their advertisements more effectively. Our work addresses this need by studying the predictability of user demographics based on the list of a user's apps which is readily available to many app developers. We extend previous work on the problem on three frontiers: (1) We predict new demographics (age, race, and income) and analyze the most informative apps for four demographic attributes included in our analysis. The most predictable attribute is gender (82.3 % accuracy), whereas the hardest to predict is income (60.3 % accuracy). (2) We compare several dimensionality reduction methods for high-dimensional app data, finding out that an unsupervised method yields superior results compared to aggregating the apps at the app category level, but the best results are obtained simply by the raw list of apps. (3) We look into the effect of the training set size and the number of apps on the predictability and show that both of these factors have a large impact on the prediction accuracy. The predictability increases, or in other words, a user's privacy decreases, the more apps the user has used, but somewhat surprisingly, after 100 apps, the prediction accuracy starts to decrease.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Al Zamal, F.; Liu, W.; and Ruths, D. 2012. Homophily and latent attribute inference: Inferring latent attributes of twitter users from neighbors. In Proc. ICWSM.
    • Brea, J.; Burroni, J.; Minnoni, M.; and Sarraute, C. 2014.
    • Chen, X.; Wang, Y.; Agichtein, E.; and Wang, F. 2015.
    • Chittaranjan, G.; Blom, J.; and Gatica-Perez, D. 2013. Mining large-scale smartphone data for personality studies. Personal and Ubiquitous Computing 17(3):433-450.
    • Culotta, A.; Ravi, N. K.; and Cutler, J. 2015. Predicting the demographics of twitter users from website traffic data. In Proc. ICWSM.
    • Duggan, M.; Ellison, N. B.; Lampe, C.; Lenhart, A.; and Mary, M. 2015. Social media update 2014. http://www.pewinternet.org/2015/01/ 09/social-media-update-2014/.
    • Goel, S.; Hofman, J. M.; and Sirer, M. I. 2012. Who does what on the web: A large-scale study of browsing behavior.
    • Hu, J.; Zeng, H.-J.; Li, H.; Niu, C.; and Chen, Z. 2007.
    • Mislove, A.; Lehmann, S.; Ahn, Y.-Y.; Onnela, J.-P.; and Rosenquist, J. N. 2011. Understanding the demographics of twitter users. In Proc. ICWSM.
    • Riederer, C.; Zimmeck, S.; Phanord, C.; Chaintreau, A.; and Bellovin, S. M. 2015. “I don't have a photograph, but you can have my footprints” - revealing the demographics of location data. In Proc. ICWSM.
    • Sarraute, C.; Blanc, P.; and Burroni, J. 2014. A study of age and gender seen through mobile phone usage patterns in mexico. In Proc. ASONAM, 836-843. IEEE.
    • Seneviratne, S.; Seneviratne, A.; Mohapatra, P.; and Mahanti, A. 2014. Predicting user traits from a snapshot of apps installed on a smartphone. ACM SIGMOBILE Mobile Computing and Communications Review 18(2):1-8.
    • Seneviratne, S.; Seneviratne, A.; Mohapatra, P.; and Mahanti, A. 2015. Your installed apps reveal your gender and more! ACM SIGMOBILE Mobile Computing and Communications Review 18(3):55-61.
    • Weber, I., and Castillo, C. 2010. The demographics of web search. In Proc. SIGIR.
    • Weber, I., and Jaimes, A. 2011. Who uses web search for what: and how. In Proc. WSDM, 15-24.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article

Collected from