2004
  1. Home
  2. A-Z Publications
  3. Computational Communication Research
  4. Volume 4, Issue 2
  5. Article
Volume 4, Issue 2
  • E-ISSN: 2665-9085
file format pdf download PDF

Abstract

Abstract

A potentially powerful method of social-scientific data collection and investigation has been created by an unexpected institution: the law. Article 15 of the EU’s 2018 General Data Protection Regulation (GDPR) mandates that individuals have electronic access to a copy of their personal data, and all major digital platforms now comply with this law by providing users with “data download packages” (DDPs). Through voluntary donation of DDPs, all data collected by public and private entities during the course of citizens’ digital life can be obtained and analyzed to answer social-scientific questions – with consent. Thus, consented DDPs open the way for vast new research opportunities. However, while this entirely new method of data collection will undoubtedly gain popularity in the coming years, it also comes with its own questions of representativeness and measurement quality, which are often evaluated systematically by means of an error framework. Therefore, in this paper we provide a blueprint for digital trace data collection using DDPs, and devise a “total error framework” for such projects. Our error framework for digital trace data collection through data donation is intended to facilitate high quality social-scientific investigations using DDPs while critically reflecting its unique methodological challenges and sources of error. In addition, we provide a quality control checklist to guide researchers in leveraging the vast opportunities afforded by this new mode of investigation.

© Laura Boeschoten, Jef Ausloos, Judith E. Möller, Theo Araujo & Daniel L. Oberski. This is an open access article distributed under the terms of the CC BY-NC 4.0 license. http://creativecommons.org/licenses/by/4.0
Loading

Article metrics loading...

/content/journals/10.5117/CCR2022.2.002.BOES
2022-10-01
2024-11-08
Loading full text...

Full text loading...

/deliver/fulltext/26659085/4/2/CCR2022.2.002.BOES.html?itemId=/content/journals/10.5117/CCR2022.2.002.BOES&mimeType=html&fmt=ahah

References

  1. Ahvanooey, M. T., Li, Q., Rabbani, M., & Rajput, A. R. (2020). A survey on smartphones security: Software vulnerabilities, malware, and attacks. arXiv preprint arXiv:2001.09406.
     [Google Scholar]
  2. Amaya, A., Biemer, P. P., & Kinyon, D. (2020). Total Error in a Big Data World: Adapting the TSE Framework to Big Data. Journal of Survey Statistics and Methodology, 8(1), 89–119. https://doi.org/10.1093/jssam/smz056
     [Google Scholar]
  3. Andrei, C.-O., Johansson, J., Koivula, H., & Poutanen, M. (2020). Signal performance analysis of the latest quartet of galileo satellites during the first operational year. 2020 International Conference on Localization and GNSS (ICL-GNSS), 1–6.
     [Google Scholar]
  4. Andrews, S., Ellis, D. A., Shaw, H., & Piwek, L. (2015). Beyond self-report: Tools to compare estimated and real-world smartphone use. PloS one, 10(10), e0139004.
     [Google Scholar]
  5. Araujo, T., Wonneberger, A., Neijens, P., & de Vreese, C. (2017). How much time do you spend online? understanding and improving the accuracy of selfreported measures of internet use. Communication Methods and Measures, 11(3), 173–190.
     [Google Scholar]
  6. Ausloos, J., Veale, M., Mahieu, R., et al. (2019). Getting data subject rights right: A submission to the european data protection board from international data rights academics, to inform regulatory guidance. Journal of Intellectual Property, Information Technology and Electronic Commerce Law, 10.
     [Google Scholar]
  7. Ausloos, J. (2019). GDPR Transparency as a Research Method (SSRN Scholarly Paper No. ID 3465680). Social Science Research Network. Rochester, NY. https://doi.org/10.2139/ssrn.3465680
     [Google Scholar]
  8. Ausloos, J., & Veale, M. (2020). Researching with data rights. Technology and Regulation, 136–157.
     [Google Scholar]
  9. Beauxis-Aussalet, E., & Hardman, L. (2017). Extended methods to handle classification biases. 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 765–774.
     [Google Scholar]
  10. Beinhauer, L., Snijkers, G., & Bakker, J. (2020). Towards a total error framework for sensor and survey data. BigSurv20.
     [Google Scholar]
  11. Bengio, Y., Goodfellow, I., & Courville, A. (2017). Deep learning. MIT press.
     [Google Scholar]
  12. Bethlehem, J., Cobben, F., & Schouten, B. (2011). Handbook of nonresponse in household surveys (Vol. 568). John Wiley & Sons.
     [Google Scholar]
  13. Beyens, I., Pouwels, J. L., van Driel, I. I., Keijsers, L., & Valkenburg, P. M. (2020). The effect of social media on well-being differs from adolescent to adolescent. Scientific Reports, 10(1), 10763. https://doi.org/10.1038/s41598-020-67727-7
     [Google Scholar]
  14. Biemer, P. P. (2010). Total Survey Error: Design, Implementation, and Evaluation. Public Opinion Quarterly, 74(5), 817–848. https://doi.org/10.1093/poq/nfq058
     [Google Scholar]
  15. Biemer, P. P. (2011). Latent class analysis of survey error (Vol. 571). John Wiley & Sons.
     [Google Scholar]
  16. Biemer, P. P. (2016). Errors and inference. In I.Foster, R.Ghani, R. S.Jarmin, F.Kreuter, & J.Lane (Eds.), Big data and social science: A practical guide to methods and tools (pp. 266–297). CRC press.
     [Google Scholar]
  17. Biemer, P. P., & Lyberg, L. (2003). Introduction to survey quality. Wiley.
     [Google Scholar]
  18. Bishop, C. M. (2006). Pattern recognition and machine learning. springer.
     [Google Scholar]
  19. Bland, J. M., & Altman, D. G. (1996). Measurement error and correlation coefficients. BMJ: British Medical Journal, 313(7048), 41.
     [Google Scholar]
  20. Blondel, V. D., Decuyper, A., & Krings, G. (2015). A survey of results on mobile phone datasets analysis [Number: 1 Publisher: SpringerOpen]. EPJ Data Science, 4(1), 1–55. https://doi.org/10.1140/epjds/s13688-015-0046-0
     [Google Scholar]
  21. Boeschoten, L., Oberski, D. L., De Waal, T., & Vermunt, J. K. (2018). Updating latent class imputations with external auxiliary variables. Structural Equation Modeling: A Multidisciplinary Journal, 25(5), 750–761.
     [Google Scholar]
  22. Boeschoten, L., Vink, G., & Hox, J. J. (2017). How to obtain valid inference under unit nonresponse?Journal of Official Statistics, 33(4), 963–978.
     [Google Scholar]
  23. Boeschoten, L., Voorvaart, R., Kaandorp, C., Goorbergh, R. v. d., & De Vos, M. (2021). Automatic deidentification of data download packages. arXiv preprint arXiv:2105.02175.
     [Google Scholar]
  24. Bolsen, T., Druckman, J. N., & Cook, F. L. (2014). The Influence of Partisan Motivated Reasoning on Public Opinion. Political Behavior, 36(2), 235–262. https://doi.org/10.1007/s11109-013-9238-0
     [Google Scholar]
  25. Bouko, C. (2020). Emotions through texts and images: A multimodal analysis of reactions to the brexit vote on flickr. Pragmatics, 30(2), 222–246.
     [Google Scholar]
  26. Brakenhoff, T. B., Mitroiu, M., Keogh, R. H., Moons, K. G., Groenwold, R. H., & van Smeden, M. (2018). Measurement error is often neglected in medical literature: A systematic review. Journal of clinical epidemiology, 98, 89–97.
     [Google Scholar]
  27. Breedveld, K., Van Den Broek, A., & Huysmans, F. (2002). Background to the methods used in the time budget survey (tbo). Social and Cultural Planning Office of the Netherlands. Disponible en internet via: http://www.scp.nl/onderzoek/tbo/english/achtergronden/history.pdf.
     [Google Scholar]
  28. Bruns, A. (2019). After the ‘apicalypse’: Social media platforms and their fight against critical scholarly research. Information, Communication & Society, 22(11), 1544–1566.
     [Google Scholar]
  29. Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Conference on fairness, accountability and transparency, 77–91.
     [Google Scholar]
  30. Carroll, R. J., Ruppert, D., Stefanski, L. A., & Crainiceanu, C. M. (2006). Measurement error in nonlinear models: A modern perspective. CRC press.
     [Google Scholar]
  31. Cbs statline. (2020). https://opendata.cbs.nl/#/CBS/nl/dataset/84888NED/table?ts=1619697629253Chambers, R. L., & Skinner, C. J. (2003). Analysis of surveydata. John Wiley & Sons.
     [Google Scholar]
  32. Cochran, W. G. (2007). Sampling techniques. John Wiley & Sons.
     [Google Scholar]
  33. Coleman, J. S. (1990). Foundations of social theory. Belknap Press of Harvard Univ. Press.
     [Google Scholar]
  34. De Leeuw, E. D., Hox, J. J., & Dillman, D. A. (2008). International handbook of survey methodology. Taylor & Francis Group/Lawrence Erlbaum Associates.
     [Google Scholar]
  35. de Haan, J., & Hendriks, R. (2013). Online data, fixed effects and the construction of high-frequency price indexes. Economic Measurement Group Workshop, 28–29.
     [Google Scholar]
  36. Dibeklioğlu, H., Salah, A. A., & Gevers, T. (2015). Recognition of Genuine Smiles. IEEE Transactions on Multimedia, 17(3), 279–294. https://doi.org/10.1109/TMM.2015.2394777
     [Google Scholar]
  37. Doidge, J. C., & Harron, K. L. (2019). Reflections on modern methods: Linkage error bias. International Journal of Epidemiology, 48(6), 2050–2060.
     [Google Scholar]
  38. D’Orazio, V., Honaker, J., & King, G. (2015). Differential Privacy for Social Science Inference. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2676160
     [Google Scholar]
  39. Dwork, C. (2008). Differential privacy: A survey of results. International conference on theory and applications of models of computation, 1–19.
     [Google Scholar]
  40. Dyreson, C. E., & Snodgrass, R. T. (1993). Timestamp semantics and representation. Information Systems, 18(3), 143–166.
     [Google Scholar]
  41. Elevelt, A., Bernasco, W., Lugtig, P., Ruiter, S., & Toepoel, V. (2019). Where You at? Using GPS Locations in an Electronic Time Use Diary Study to Derive Functional Locations. Social Science Computer Review. https://doi.org/10.1177/0894439319877872
     [Google Scholar]
  42. Freelon, D. (2018). Computational research in the post-api age. Political Communication, 35(4), 665–668.
     [Google Scholar]
  43. Gayo-Avello, D. (2012). No, you cannot predict elections with Twitter. IEEE Internet Computing, 16(6), 91–94.
     [Google Scholar]
  44. Groves, R. M., & Lyberg, L. (2010). Total Survey Error: Past, Present, and Future. Public Opinion Quarterly, 74(5), 849–879. https://doi.org/10.1093/poq/nfq065
     [Google Scholar]
  45. Groves, R. M., & Peytcheva, E. (2008). The impact of nonresponse rates on nonresponse bias: A meta-analysis. Public opinion quarterly, 72(2), 167–189.
     [Google Scholar]
  46. Guerra-Santin, O., & Itard, L. (2010). Occupants’ behaviour: Determinants and effects on residential heating consumption. Building Research & Information, 38(3), 318–338.
     [Google Scholar]
  47. Haenschen, K. (2020). Self-reported versus digitally recorded: Measuring political activity on facebook. Social Science Computer Review, 38(5), 567–583.
     [Google Scholar]
  48. Halavais, A. (2019). Overcoming terms of service: A proposal for ethical distributed research. Information, Communication & Society, 22(11), 1567–1581.
     [Google Scholar]
  49. Harron, K., Goldstein, H., & Dibben, C. (2015). Methodological developments in data linkage. John Wiley & Sons.
     [Google Scholar]
  50. Hjelmaas, E., & Low, B. K. (2001). Face detection: A survey. Computer vision and image understanding, 83(3), 236–274.
     [Google Scholar]
  51. Hsu, R.-L., Abdel-Mottaleb, M., & Jain, A. K. (2002). Face detection in color images. IEEE transactions on pattern analysis and machine intelligence, 24(5), 696–706.
     [Google Scholar]
  52. Hutton, L., & Henderson, T. (2015). ‘‘i didn’t sign up for this!”: Informed consent in social network research. ICWSM.
     [Google Scholar]
  53. Japec, L., Kreuter, F., Berg, M., Biemer, P. P., Decker, P., Lampe, C., Lane, J., O’Neil, C., & Usher, A. (2015). Big data in survey research: AAPOR task force report. Public Opinion Quarterly, 79(4), 839–880.
     [Google Scholar]
  54. Jungherr, A. (2015). Analyzing Political Communication with Digital Trace Data: The Role of Twitter Messages in Social Science Research. Springer International Publishing. https://doi.org/10.1007/978-3319-20319-5
     [Google Scholar]
  55. Kaya, H., Gürpinar, F., & Salah, A. A. (2017). Video-based emotion recognition in the wild using deep transfer learning and score fusion. Image and Vision Computing, 65, 66–75.
     [Google Scholar]
  56. Kaya, H., Gürpınar, F., & Salah, A. A. (2017). Video-based emotion recognition in the wild using deep transfer learning and score fusion. Image and Vision Computing, 65, 66–75. https://doi.org/10.1016/j.imavis.2017.01.012
     [Google Scholar]
  57. Kim, J.-k., & Tam, S. M. (2020). Data integration by combining big data and survey sample data for finite population inference. arXiv preprint arXiv:2003.12156.
     [Google Scholar]
  58. King, G. (2011). Ensuring the Data-Rich Future of the Social Sciences. Science, 331(6018), 719–721. https://doi.org/10.1126/science.1197872
     [Google Scholar]
  59. King, G., & Persily, N. (2019). A New Model for Industry–Academic Partnerships. PS: Political Science & Politics, 1–7. https://doi.org/10.1017/S1049096519001021
     [Google Scholar]
  60. Konitzer, T., Allen, J., Eckman, S., Howland, B., Mobius, M. M., Rothschild, D. M., & Watts, D. (2020). Measuring News Consumption With Behavioral Versus Survey Data. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3548690
     [Google Scholar]
  61. Kosinski, M., Stillwell, D., & Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences, 110(15), 5802–5805. https://doi.org/10.1073/pnas.1218772110
     [Google Scholar]
  62. Kramer, A. D., Guillory, J. E., & Hancock, J. T. (2014). Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences, 111(24), 8788–8790.
     [Google Scholar]
  63. Kreuter, F., Sakshaug, J. W., & Tourangeau, R. (2016). The framing of the record linkage consent question. International Journal of Public Opinion Research, 28(1), 142–152.
     [Google Scholar]
  64. Li, S., & Deng, W. (2020). Deep Facial Expression Recognition: A Survey. IEEE Transactions on Affective Computing, 1–1. https://doi.org/10.1109/TAFFC.2020.2981446
     [Google Scholar]
  65. Lohr, S. L. (2008). Coverage and sampling. International handbook of survey methodology, 97–112.
     [Google Scholar]
  66. Lohr, S. L. (2009). Multiple-frame surveys. Handbook of statistics (pp. 71–88). Elsevier.
     [Google Scholar]
  67. Lovestone, S., & Consortium, E. (2020). The european medical information framework: A novel ecosystem for sharing healthcare data across europe. Learning Health Systems, 4(2), e10214.
     [Google Scholar]
  68. Manikonda, L., Hu, Y., & Kambhampati, S. (2014). Analyzing user activities, demographics, social network structure and user-generated content on instagram. arXiv preprint arXiv:1410.8099.
     [Google Scholar]
  69. Martínez-García, Á. (2017). La imagen en la era digital. Egregius. Retrieved August12, 2020, from https://idus.us.es/handle/11441/91571
     [Google Scholar]
  70. Mellon, J., & Prosser, C. (2017). Twitter and Facebook are not representative of the general population: Political attitudes and demographics of British social media users. Research& Politics, 4(3), 205316801772000. https://doi.org/10.1177/2053168017720008
     [Google Scholar]
  71. Menchen-Trevino, E. (2016). Web historian: Enabling multimethod and independent research with real-world web browsing history data. IConference 2016 Proceedings.
     [Google Scholar]
  72. Messing, S., DeGregorio, C., Hillenbrand, B., King, G., Mahanti, S., Mukerjee, Z., Nayak, C., Persily, N., State, B., & Wilkins, A. (2020). Facebook PrivacyProtected Full URLs Data Set [type: dataset]. https://doi.org/10.7910/DVN/TDOAPG
  73. Munafò, M. R., & Davey Smith, G. (2018). Robust research needs many lines of evidence. Nature, 553(7689),399–401. https://doi.org/10.1038/d41586018-01023-3
     [Google Scholar]
  74. Murphy, K. P. (2012). Machine learning: A probabilistic perspective. MIT press.
     [Google Scholar]
  75. Myers, G. J., Badgett, T., Thomas, T. M., & Sandler, C. (2004). The art of software testing (Vol. 2). Wiley Online Library.
     [Google Scholar]
  76. Nissenbaum, H. (2004). Privacy as contextual integrity. Wash. L. Rev., 79, 119.
     [Google Scholar]
  77. Oberski, D. L., Kirchner, A., Eckman, S., & Kreuter, F. (2017). Evaluating the quality of survey and administrative data with generalized multitraitmultimethod models. Journal of the American Statistical Association, 112(520), 1477–1489.
     [Google Scholar]
  78. Oberski, D. L., & Kreuter, F. (2020). Differential privacy and social science: An urgent puzzle. Harvard Data Science Review, 2(1). https://doi.org/10.1162/99608f92.63a22079
     [Google Scholar]
  79. Papacharissi, Z. (2010). A networked self: Identity, community, and culture on social network sites. Routledge.
     [Google Scholar]
  80. Parry, D. A., Davidson, B. I., Sewall, C. J., Fisher, J. T., Mieczkowski, H., & Quintana, S. (2021). A systematic review and meta-analysis of discrepancies between logged and self-reported digital media use. Nature Human Behaviour, 1–13. https://doi.org/10.1038/s41562-021-01117-5
     [Google Scholar]
  81. Patil, P., Peng, R. D., & Leek, J. T. (2016). A statistical definition for reproducibility and replicability. BioRxiv, 066803.
     [Google Scholar]
  82. Pentland, A. (2010). Honest signals: How they shape our world. MIT press.
     [Google Scholar]
  83. Perriam, J., Birkbak, A., & Freeman, A. (2020). Digital methods in a post-api environment. International Journal of Social Research Methodology, 23(3), 277–290.
     [Google Scholar]
  84. Pfeffer, J., Mayer, K., & Morstatter, F. (2018). Tampering with twitter’s sample api. EPJ Data Science, 7(1), 50.
     [Google Scholar]
  85. Privitera, G. J. (2018). Research methods for the behavioral sciences. Sage Publications.
     [Google Scholar]
  86. Quan-Haase, A., & Young, A. L. (2010). Uses and gratifications of social media: A comparison of facebook and instant messaging. Bulletin of science, technology & society, 30(5), 350–361.
     [Google Scholar]
  87. Reeves, B., Ram, N., Robinson, T. N., Cummings, J. J., Giles, C. L., Pan, J., Chiatti, A., Cho, M., Roehrick, K., Yang, X., et al. (2019). Screenomics: A framework to capture and analyze personal life experiences and the ways that technology shapes them. Human–Computer Interaction, 1–52.
     [Google Scholar]
  88. Reeves, B., Ram, N., Robinson, T. N., Cummings, J. J., Giles, C. L., Pan, J., Chiatti, A., Cho, M., Roehrick, K., Yang, X., et al. (2021). Screenomics: A framework to capture and analyze personal life experiences and the ways that technology shapes them. Human–Computer Interaction, 36(2), 150–201.
     [Google Scholar]
  89. Revilla, M., Ochoa, C., & Loewe, G. (2017). Using Passive Data From a Meter to Complement Survey Data in Order to Study Online Behavior. Social Science Computer Review, 35(4), 521–536. https://doi.org/10.1177/0894439316638457
     [Google Scholar]
  90. Ruktanonchai, N. W., Ruktanonchai, C. W., Floyd, J. R., & Tatem, A. J. (2018). Using google location history data to quantify fine-scale human mobility. International Journal of Health Geographics, 17(1), 28.
     [Google Scholar]
  91. Saris, W. E., & Gallhofer, I. N. (2007). Design, evaluation, and analysis of questionnaires for survey research [OCLC: ocm80358503]. Wiley-Interscience.
     [Google Scholar]
  92. Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological methods, 7(2), 147.
     [Google Scholar]
  93. Scheerman, M., Voort, L., & Zarrabi, N.Secure processing of sensitive data on shared hpc systems. In: 2020. https://www.compbiomed-conference.org/wp-content/uploads/2020/03/Fri_11_15_Narges_Zarrabi-slides-compbiomed-website_smaller.pdf
     [Google Scholar]
  94. The schemes rely on global open standards. (2021). https://www.europeanpaymentscouncil.eu/what-we-do/sepa-payment-scheme-management/schemes-rely-global-open-standards
  95. Schoen, H., Gayo-Avello, D., Takis Metaxas, P., Mustafaraj, E., Strohmaier, M., & Gloor, P. (2013). The power of prediction with social media (D. Gayo-Avello Panagiotis Takis Metax, Ed.). Internet Research, 23(5), 528–543. https://doi.org/10.1108/IntR-06-2013-0115
     [Google Scholar]
  96. Sen, I., Floeck, F., Weller, K., Weiss, B., & Wagner, C. (2019). A total error framework for digital traces of humans. arXiv preprint arXiv:1907.08228.
     [Google Scholar]
  97. Settanni, M., Azucar, D., & Marengo, D. (2018). Predicting Individual Characteristics from Digital Traces on Social Media: A Meta-Analysis. Cyberpsychology, Behavior, and Social Networking, 21(4), 217–228. https://doi.org/10.1089/cyber.2017.0384
     [Google Scholar]
  98. Shirima, K., Mukasa, O., Schellenberg, J. A., Manzi, F., John, D., Mushi, A., Mrisho, M., Tanner, M., Mshinda, H., & Schellenberg, D. (2007). The use of personal digital assistants for data entry at the point of collection in a large household survey in southern tanzania. Emerging themes in epidemiology, 4(1), 1–8.
     [Google Scholar]
  99. Singer, E. (1993). Informed consent and survey response: A summary of the empirical literature. Journal of Official Statistics, 9(2), 361.
     [Google Scholar]
  100. Singh, R. G., & Ruj, S. (2020). A Technical Look At The Indian Personal Data Protection Bill [arXiv:2005.13812]. arXiv:2005.13812 [cs]. Retrieved August12, 2020, from http://arxiv.org/abs/2005.13812
     [Google Scholar]
  101. Skatova, A., & Goulding, J. (2019). Psychology of personal data donation. PloS one, 14(11), e0224240.
     [Google Scholar]
  102. Stier, S., Breuer, J., Siegers, P., & Thorson, K. (2019). Integrating survey data and digital trace data: Key issues in developing an emerging field.
     [Google Scholar]
  103. Stodden, V., Leisch, F., & Peng, R. D. (2014). Implementing reproducible research. CRC Press.
     [Google Scholar]
  104. Stodden, V., & Miguez, S. (2014). Best practices for computational science: Software infrastructure and environments for reproducible and extensible research. Journal of Open Research Software, 2(1).
     [Google Scholar]
  105. Suda, Y. (2020). Japan’s Personal Information Protection Policy Under Pressure. Asian Survey, 60(3), 510–533. https://doi.org/10.1525/as.2020.60.3.510
     [Google Scholar]
  106. Szell, M., Lambiotte, R., & Thurner, S. (2010). Multirelational organization of large-scale social networks in an online world [Publisher: National Academy of Sciences Section: Social Sciences]. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1004008107
     [Google Scholar]
  107. Utrecht university research data management support [Accessed: 2020-07-18]. (n.d.).
  108. Valkenburg, P. M., Sumter, S. R., & Peter, J. (2011). Gender differences in online and offline self-disclosure in pre-adolescence and adolescence. British Journal of Developmental Psychology, 29(2), 253–269.
     [Google Scholar]
  109. Valliant, R., Dever, J. A., & Kreuter, F. (2018). Practical Tools for Designing and Weighting Survey Samples. Springer International Publishing : Imprint: Springer. Retrieved August5, 2020, from https://link.springer.com/10.1007/978-3-319-93632-1
     [Google Scholar]
  110. van der Sloot, B. (2020). The general data protection regulation in plain language. Amsterdam University Press.
     [Google Scholar]
  111. van Eldik, A., Kneer, J., & Jansz, J. (2019). Urban & online: Social media use among adolescents and sense of belonging to a super-diverse city. Media and Communication, 7(2), 242–253.
     [Google Scholar]
  112. Wachter, S., Mittelstadt, B., & Russell, C. (2017). Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR. Harvard Journal of Law & Technology (Harvard JOLT), 31(2), 841–888. Retrieved August7, 2020, from https://heinonline.org/HOL/P?h=hein.journals/hjlt31&i=860
     [Google Scholar]
  113. Weeks, B. E., Menchen-Trevino, E., Calabrese, C., Casas, A., & Wojcieszak, M. (2021). Partisan media, untrustworthy news sites, and political misperceptions. New Media & Society, 14614448211033300.
     [Google Scholar]
  114. Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., et al. (2016). The fair guiding principles for scientific data management and stewardship. Scientific data, 3(1), 1–9.
     [Google Scholar]
  115. Wojcieszak, M., Menchen-Trevino, E., Goncalves, J. F., & Weeks, B. (2021). Avenues to news and diverse news exposure online: Comparing direct navigation, social media, news aggregators, search queries, and article hyperlinks. The International Journal of Press/Politics, 19401612211009160.
     [Google Scholar]
  116. Wong, J., & Henderson, T. (2019). The right to data portability in practice: Exploring the implications of the technologically neutral gdpr. International Data Privacy Law, 9(3), 173–191.
     [Google Scholar]
  117. Zhang, L.-C. (2012). Topics of statistical theory for registerbased statistics and data integration. Statistica Neerlandica, 66(1), 41–63.
     [Google Scholar]
/content/journals/10.5117/CCR2022.2.002.BOES
Loading
A framework for privacy preserving digital trace data collection through data donation
CCR 4, 388 (2022); https://doi.org/10.5117/CCR2022.2.002.BOES
/content/journals/10.5117/CCR2022.2.002.BOES
/content/journals/10.5117/CCR2022.2.002.BOES
Loading

Data & Media loading...

Keyword(s): Data donation; Digital trace data; Informed consent; Privacy; Total Error Framework

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error