oa OSD2F: An Open-Source Data Donation Framework

References

1. Araujo, T., Wonneberger, A., Neijens, P., & Vreese, C. de. (2017). How Much Time Do You Spend Online? Understanding and Improving the Accuracy of Self-Reported Measures of Internet Use. Communication Methods and Measures, 11(3), 173–190. https://doi.org/10.1080/19312458.2017.1317337.
   [Google Scholar]
2. Ausloos, J., & Dewitte, P. (2018). Shattering one-way mirrors – data subject access rights in practice. International Data Privacy Law, 8(1), 4–28. https://doi.org/10.1093/idpl/ipy001
   [Google Scholar]
3. Ausloos, J., & Veale, M. (2020). Researching with Data Rights. Technology and Regulation, 2020, 136–157.
   [Google Scholar]
4. Baumgartner, S. E., Sumter, S. R., Petkevic, V., & Wiradhany. (2021). Presenting a Novel Data Collection and Automated Processing Approach for iOS Smartphone Data. 71st Annual ICA Conference, Virtual Conference.
   [Google Scholar]
5. Bodó, B., Helberger, N., Irion, K., Zuiderveen Borgesius, F., Moller, J., van de Velde, B., Bol, N., van Es, B., de Vreese, C., IViR (FdR), & Political Communication & Journalism (ASCoR, FMG). (2017). Tackling the Algorithmic Control Crisis – the Technical, Legal, and Ethical Challenges of Research into Algorithmic Agents. Yale Journal of Law and Technology, 19, 133–180.
   [Google Scholar]
6. Boeschoten, L., Ausloos, J., Moeller, J., Araujo, T., & Oberski, D. L. (2020). Digital trace data collection through data donation. ArXiv:2011.09851 [Cs, Stat].
   [Google Scholar]
7. Boeschoten, L., Mendrik, A., van der Veen, E., Vloothuis, J., Hu, H., Voorvaart, R., & Oberski, D. (2022). Privacy preserving local analysis of digital trace data: A proof-of-concept. Patterns,3(3). https://doi.org/10.1016/j.patter.2022.100444
   [Google Scholar]
8. Brinberg, M., Vanderbilt, R. R., Solomon, D. H., Brinberg, D., & Ram, N. (2021). Using technology to unobtrusively observe relationship development. Journal of Social and Personal Relationships, 02654075211028654. https://doi.org/10.1177/02654075211028654
   [Google Scholar]
9. Bruns, A. (2019). After the ‘APIcalypse’: Social media platforms and their fight against critical scholarly research. Information, Communication & Society, 22(11), 1544–1566. https://doi.org/10.1080/1369118X.2019.1637447
   [Google Scholar]
10. Christner, C., Urman, A., Adam, S., & Maier, M. (2021). Automated Tracking Approaches for Studying Online Media Use: A Critical Review and Recommendations. Communication Methods and Measures, 1–17. https://doi.org/10.1080/19312458.2021.1907841
    [Google Scholar]
11. Freelon, D. (2018). Computational Research in the Post-API Age. Political Communication, 35(4), 665–668. https://doi.org/10.1080/10584609.2018.1477506
    [Google Scholar]
12. Halavais, A. (2019). Overcoming terms of service: A proposal for ethical distributed research. Information, Communication & Society, 0(0), 1–15. https://doi.org/10.1080/1369118X.2019.1627386
    [Google Scholar]
13. Kobayashi, T., & Boase, J. (2012). No Such Effect? The Implications of Measurement Error in Self-Report Measures of Mobile Communication Use. Communication Methods and Measures, 6(2), 126–143. https://doi.org/10.1080/19312458.2012.679243
    [Google Scholar]
14. Lazer, D., Hargittai, E., Freelon, D., Gonzalez-Bailon, S., Munger, K., Ognyanova, K., & Radford, J. (2021). Meaningful measures of human society in the twenty-first century. Nature, 595(7866), 189–196. https://doi.org/10.1038/s41586-021-03660-7
    [Google Scholar]
15. Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabasi, A. L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., Jebara, T., King, G., Macy, M., Roy, D., & Van Alstyne, M. (2009). Life in the network: The coming age of computational social science. Science (New York, N.Y.), 323(5915), 721–723. https://doi.org/10.1126/science.1167742
    [Google Scholar]
16. Menchen-Trevino, E. (2016). Web historian: Enabling multi-method and independent research with real-world web browsing history data. https://doi.org/10.9776/16611
    [Google Scholar]
17. Ohme, J., Araujo, T., de Vreese, C. H., & Piotrowski, J. T. (2020). Mobile data donations: Assessing self-report accuracy and sample biases with the iOS Screen Time function. Mobile Media & Communication, 2050157920959106. https://doi.org/10.1177/2050157920959106
    [Google Scholar]
18. Puschmann, C. (2019). Beyond the Bubble: Assessing the Diversity of Political Search Results. Digital Journalism, 7(6), 824–843. https://doi.org/10.1080/21670811.2018.1539626
    [Google Scholar]
19. Reeves, B., Robinson, T., & Ram, N. (2020). Time for the Human Screenome Project. Nature, 577(7790), 314–317. https://doi.org/10.1038/d41586-020-00032-5
    [Google Scholar]
20. Scharkow, M. (2016). The Accuracy of Self-Reported Internet Use—A Validation Study Using Client Log Data. Communication Methods and Measures, 10(1), 13–27. https://doi.org/10.1080/19312458.2015.1118446
    [Google Scholar]
21. Verbeij, T., Pouwels, J. L., Beyens, I., & Valkenburg, P. M. (2021). The accuracy and validity of self-reported social media use measures among adolescents. Computers in Human Behavior Reports, 3, 100090. https://doi.org/10.1016/j.chbr.2021.100090
    [Google Scholar]