Digital Trace Data Collection for Social Media Effects Research: APIs, Data Donation, and (Screen) Tracking

References

• Abul-Fottouh, D., Song, M. Y., & Gruzd, A. (2020). Examining algorithmic biases in YouTube’s recommendations of vaccine videos. International Journal of Medical Informatics, 140, 104175. https://doi.org/10.1016/j.ijmedinf.2020.104175
   PubMed Web of Science ®Google Scholar
• Allcott, H., Braghieri, L., Eichmeyer, S., & Gentzkow, M. (2020). The welfare effects of social media. The American Economic Review, 110(3), 629–676. https://doi.org/10.1257/aer.20190658
   Web of Science ®Google Scholar
• Allen, J., Howland, B., Mobius, M., Rothschild, D., & Watts, D. J. (2020). Evaluating the fake news problem at the scale of the information ecosystem. Science Advances, 6(14), eaay3539. https://doi.org/10.1126/sciadv.aay3539
   PubMed Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Araujo, T., Ausloos, J., van Atteveldt, W., Loecherbach, F., Moeller, J., Ohme, J., Trilling, D., van de Velde, B., de Vreese, C., & Welbers, K. (2022). OSD2F: An open-source data donation framework. Computational Communication Research, 4(2), 372–387. https://doi.org/10.5117/CCR2022.2.001.ARAU
   Google Scholar
• Araujo, T., Wonneberger, A., Neijens, P., & Vreese, C. D. (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
   Web of Science ®Google Scholar
• Ausloos, J., & Veale, M. (2021). Researching with data rights. Technology and Regulation, 136–157. http://dx.doi.org/10.2139/ssrn.3465680
   Google Scholar
• Bartley, N., Abeliuk, A., Ferrara, E., & Lerman, K. (2021). Auditing algorithmic bias on Twitter. 13th ACM Web Science Conference 2021, 65–73. https://doi.org/10.1145/34475353462491.
   Google Scholar
• Baumgartner, S. E., Sumter, S. R., Petkevič, V., & Wiradhany, W. (2022). A novel iOS data donation approach: automatic processing, compliance, and reactivity in a longitudinal study. Social Science Computer Review, 08944393211071068. https://doi.org/10.1177/08944393211071068
   Web of Science ®Google Scholar
• Bennett, W. L., & Iyengar, S. (2008). A new era of minimal effects? The changing foundations of political communication. Journal of Communication, 58(4), 707–731.
   Web of Science ®Google Scholar
• Boeschoten, L., Ausloos, J., Moeller, J., Araujo, T., & Oberski, D. L. (2020). Digital trace data collection through data donation. ArXiv:2011.09851 [Cs, Stat]. http://arxiv.org/abs/2011.09851
   Google Scholar
• Boeschoten, L., Ausloos, J., Möller, J. E., Araujo, T., & Oberski, D. L. (2022). A framework for privacy preserving digital trace data collection through data donation. Computational Communication Research, 4(2), 388–423. https://doi.org/10.5117/CCR2022.2.002.BOES
   Google Scholar
• Boeschoten, L., Mendrik, A., van der Veen, E., Vloothuis, J., Hu, H., Voorvaart, R., & Oberski, D. L. (2022). Privacy-preserving local analysis of digital trace data: A proof-of-concept. Patterns, 3(3), 100444. https://doi.org/10.1016/j.patter.2022.100444
   PubMedGoogle Scholar
• Borges Do Nascimento, I. J., Beatriz Pizarro, A., Almeida, J., Azzopardi-Muscat, N., André Gonçalves, M., Björklund, M., & Novillo-Ortiz, D. (2022). Infodemics and health misinformation: A systematic review of reviews. Bulletin of the World Health Organization, 100(9), 544–561. https://doi.org/10.2471/BLT.21.287654
   PubMed Web of Science ®Google Scholar
• Breuer, J., Kmetty, Z., Haim, M., & Stier, S. (2022). User-centric approaches for collecting Facebook data in the ‘post-API age’: Experiences from two studies and recommendations for future research. Information, Communication & Society. https://doi.org/10.1080/1369118X.2022.2097015
   Google Scholar
• Burgess, J., Angus, D., Carah, N., Andrejevic, M., Hawker, K., Lewis, K., Obeid, A. K., Smith, A., Tan, J., Fordyce, R., Trott, V., & Li, L. 2021. Critical simulation as hybrid digital method for exploring the data operations and vernacular cultures of visual social media platforms. Preprint SocArXiv. https://doi.org/10.31235/osf.io/2cwsu
   Google Scholar
• Chiatti, A., Cho, M. J., Gagneja, A., Yang, X., Brinberg, M., Roehrick, K., Choudhury, S. R., Ram, N., Reeves, B., & Giles, C. L. (2018). Text extraction and retrieval from smartphone screenshots: Building a repository for life in media. Proceedings of the 33rd Annual ACM Symposium on Applied Computing, 948–955. https://doi.org/10.1145/3167132.3167236
   Google Scholar
• Christner, C., Urman, A., Adam, S., & Maier, M. (2022). Automated tracking approaches for studying online media use: A critical review and recommendations. Communication Methods and Measures, 16(2), 79–95. https://doi.org/10.1080/19312458.2021.1907841
   Web of Science ®Google Scholar
• Cohn, J. (2019). The burden of choice: Recommendations, subversion, and algorithmic culture. Rutgers University Press.
   Google Scholar
• Cramer, H., Garcia-Gathright, J., Springer, A., & Reddy, S. (2018). Assessing and addressing algorithmic bias in practice. Interactions, 25(6), 58–63. https://doi.org/10.1145/3278156
   Google Scholar
• Cronin, J., von Hohenberg, B. C., Gonçalves, J. F. F., Menchen-Trevino, E., & Wojcieszak, M. (2022). The (null) over-time effects of exposure to local news websites: Evidence from trace data. Journal of Information Technology & Politics, 1–15. https://doi.org/10.1080/19331681.2022.2123878
   Web of Science ®Google Scholar
• De Vreese, C. H., Boukes, M., Schuck, A., Vliegenthart, R., Bos, L., & Lelkes, Y. (2017). Linking survey and media content data: Opportunities, considerations, and pitfalls. Communication Methods and Measures, 11(4), 221–244. https://doi.org/10.1080/19312458.2017.1380175
   Web of Science ®Google Scholar
• Diener, E. (1984). Subjective well-being. Psychological Bulletin, 95(3), 542–575. https://doi.org/10.1037/0033-2909.95.3.542
   PubMed Web of Science ®Google Scholar
• European Union. (2016). Regulation (EU) 2016/679 of the European parliament and of the council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing directive 95/46/EC (general data protection regulation). OJ, 59(L 119), 1–89.
   Google Scholar
• Fan, Y., Lehmann, S., & Blok, A. (2022). Extracting the interdisciplinary specialty structures in social media data-based research: A clustering-based network approach. Journal of Informetrics, 16(3), 101310. https://doi.org/10.1016/j.joi.2022.101310
   Web of Science ®Google Scholar
• Freelon, D. (2014). On the interpretation of digital trace data in communication and social computing research. Journal of Broadcasting & Electronic Media, 58(1), 59–75. https://doi.org/10.1080/08838151.2013.875018
   Web of Science ®Google Scholar
• Freelon, D. (2018). Computational research in the post-API age. Political Communication, 35(4), 665–668. https://doi.org/10.1080/10584609.2018.1477506
   Web of Science ®Google Scholar
• Freelon, D., Pruden, M. L., Malmer, D., & Crist, A. (2022). Piegraph. [Computer software]. Retrieved from http://pcad.ils.unc.edu
   Google Scholar
• Freelon, D., & Wells, C. (2020). Disinformation as political communication. Political Communication.
   Google Scholar
• Gaisbauer, F., Pournaki, A., Banisch, S., Olbrich, E., & Guidi, B. (2021). Ideological differences in engagement in public debate on Twitter. Plos One, 16(3), e0249241. https://doi.org/10.1371/journal.pone.0249241
   PubMed Web of Science ®Google Scholar
• Gillespie, T. (2014). The relevance of algorithms. Media Technologies: Essays on Communication, Materiality, and Society, 167(2014), 167.
   Google Scholar
• Grinberg, N., Joseph, K., Friedland, L., Swire-Thompson, B., & Lazer, D. (2019). Fake news on Twitter during the 2016 U.S. presidential election. Science, 363(6425), 374–378. https://doi.org/10.1126/science.aau2706
   PubMed Web of Science ®Google Scholar
• Guess, A., Nagler, J., & Tucker, J. (2019). Less than you think: Prevalence and predictors of fake news dissemination on Facebook. Science Advances, 5(1), eaau4586. https://doi.org/10.1126/sciadv.aau4586
   PubMed Web of Science ®Google Scholar
• Guo, J. (2022). Deep learning approach to text analysis for human emotion detection from big data. Journal of Intelligent Systems, 31(1), 113–126. https://doi.org/10.1515/jisys-2022-0001
   Web of Science ®Google Scholar
• Halavais, A. (2019). Overcoming terms of service: A proposal for ethical distributed research. Information, Communication & Society, 22(11), 1567–1581. https://doi.org/10.1080/1369118X.2019.1627386
   Web of Science ®Google Scholar
• Hancock, J. T., Liu, S. X., Luo, M., & Mieczkowski, H. (2022). Social media and psychological well-being. In S. C. Matz (Ed.), The psychology of technology: Social science research in the age of big data (pp. 195–238). American Psychological Association. https://doi.org/10.1037/0000290-007
   Google Scholar
• Hooker, S. (2021). Moving beyond “algorithmic bias is a data problem. Patterns, 2(4), 100241. https://doi.org/10.1016/j.patter.2021.100241
   PubMedGoogle Scholar
• Howison, J., Wiggins, A., & Crowston, K. (2011). Validity issues in the use of social network analysis with digital trace data. Journal of the Association for Information Systems, 12(12), 767–797. https://doi.org/10.17705/1jais.00282
   Web of Science ®Google Scholar
• Kmetty, Z., & Németh, R. (2022). Which is your favorite music genre? A validity comparison of Facebook data and survey data. Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique, 154(1), 82–104. https://doi.org/10.1177/07591063211061754
   Google Scholar
• Lambrecht, A., & Tucker, C. (2019). Algorithmic Bias? Anastem career ads. Management Science, 65(7), 2966–2981. https://doi.org/10.1287/mnsc.2018.3093
   Web of Science ®Google Scholar
• 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
   PubMed Web of Science ®Google Scholar
• Lee, J. H. S., Li, T., Hsu, W., & Lee, M. L. (2021). Repurpose image identification for fake news detection. In C. Strauss, G. Kotsis, A. M. Tjoa, & I. Khalil (Eds.), Database and Expert Systems Applications: 32nd International Conference, DEXA 2021, Virtual Event, September 27–30, 2021, Proceedings, Part II (Vol. 12924). Springer International Publishing. https://doi.org/10.1007/978-3-030-86475-0
   Google Scholar
• Lee, J., Reeves, N., Ram, B., & Hamilton, J. (2022). The psychology of poverty and life online: Natural Experiments on the Effects of Smartphone Payday Loan Ads on Psychological Stress. Information, Communication & Society, 1–22. https://doi.org/10.1080/1369118X.2022.2109982
   Web of Science ®Google Scholar
• Luiten, A., Hox, J. J. C. M., & De Leeuw, E. D. (2020). Survey nonresponse trends and fieldwork effort in the 21st century: Results of an international study across countries and surveys. Journal of Official Statistics, 36(3), 469–487. https://doi.org/10.2478/jos-2020-0025
   Web of Science ®Google Scholar
• Mackey, T. K., Purushothaman, V., Haupt, M., Nali, M. C., & Li, J. (2021). Application of unsupervised machine learning to identify and characterise hydroxychloroquine misinformation on Twitter. Lancet Digital Health, 3(2), e72–75. https://doi.org/10.1016/S2589-7500(20)30318-6
   PubMedGoogle Scholar
• 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), 2053168017720008. https://doi.org/10.1177/2053168017720008
   Web of Science ®Google Scholar
• Menchen-Trevino, E. (2016). Web historian: Enabling multi-method and independent research with real-world web browsing history data. IConference 2016 Proceedings (iSchools). https://doi.org/10.9776/16611.
   Google Scholar
• Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. NYU Press.
   Google Scholar
• Ohme, J., & Araujo, T. (2022). Digital data donations: A quest for best practices. Patterns, 3(4), 100467. https://doi.org/10.1016/j.patter.2022.100467
   PubMedGoogle Scholar
• Ohme, J., Araujo, T., de Vreese, C. H., & Piotrowski, J. T. (2021). Mobile data donations: Assessing self-report accuracy and sample biases with the iOS screen time function. Mobile Media & Communication, 9(2), 293–313. https://doi.org/10.1177/2050157920959106
   Web of Science ®Google Scholar
• Orben, A. (2020). Teenagers, screens and social media: A narrative review of reviews and key studies. Social Psychiatry and Psychiatric Epidemiology, 55(4), 407–414. https://doi.org/10.1007/s00127-019-01825-4
   PubMed Web of Science ®Google Scholar
• Otto, L. P., Thomas, F., Glogger, I., & De Vreese, C. H. (2022). Linking media content and survey data in a dynamic and digital media environment – mobile longitudinal linkage analysis. Digital Journalism, 10(1), 200–215. https://doi.org/10.1080/21670811.2021.1890169
   Web of Science ®Google Scholar
• Parry, D. A., Davidson, B. I., Sewall, C. J. R., Fisher, J. T., Mieczkowski, H., & Quintana, D. S. (2021). A systematic review and meta-analysis of discrepancies between logged and self-reported digital media use. Nature Human Behaviour, 5(11), 1535–1547. https://doi.org/10.1038/s41562-021-01117-5
   PubMed Web of Science ®Google Scholar
• Reeves, B., Ram, N., Robinson, T. N., Cummings, J. J., Giles, C. L., Pan, J., Chiatti, A., Cho, M., Roehrick, K., Yang, X., Gagneja, A., Brinberg, M., Muise, D., Lu, Y., Luo, M., Fitzgerald, A., & Yeykelis, L. (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. https://doi.org/10.1080/07370024.2019.1578652
   PubMed Web of Science ®Google Scholar
• Robertson, R. E., Jiang, S., Joseph, K., Friedland, L., Lazer, D., & Wilson, C. (2018). Auditing partisan audience bias within google search. Proceedings of the ACM on Human-Computer Interaction, 2(CSCW), 1–22. https://doi.org/10.1145/3274417
   Google Scholar
• 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
   Web of Science ®Google Scholar
• Singh, B., & Sharma, D. K. (2022). Predicting image credibility in fake news over social media using multi-modal approach. Neural Computing & Applications, 34(24), 21503–21517. https://doi.org/10.1007/s00521-021-06086-4
   PubMed Web of Science ®Google Scholar
• Stadel, M., & Stulp, G. (2022). Balancing bias and burden in personal network studies. Social Networks, 70, 16–24. https://doi.org/10.1016/j.socnet.2021.10.007
   Web of Science ®Google Scholar
• Stier, S., Breuer, J., Siegers, P., & Thorson, K. (2020). Integrating survey data and digital trace data: Key issues in developing an emerging field. Social Science Computer Review, 38(5), 503–516. https://doi.org/10.1177/0894439319843669
   Web of Science ®Google Scholar
• Sun, X., Ram, N., Reeves, B., Cho, M. -J., Fitzgerald, A., & Robinson, T. N. (2022). Connectedness and independence of young adults and parents in the digital world: Observing smartphone interactions at multiple timescales using screenomics. Journal of Social and Personal Relationships. https://doi.org/10.1177/02654075221104268
   Web of Science ®Google Scholar
• Thorson, K., Cotter, K., Medeiros, M., & Pak, C. (2021). Algorithmic inference, political interest, and exposure to news and politics on Facebook. Information, Communication & Society, 24(2), 183–200. https://doi.org/10.1080/1369118X.2019.1642934
   Web of Science ®Google Scholar
• Toth, R., & Trifonova, T. (2021). Somebody’s watching me: Smartphone use tracking and reactivity. Computers in Human Behavior Reports, 4, 100142. https://doi.org/10.1016/j.chbr.2021.100142
   Google Scholar
• Tufekci, Z. (2014). Engineering the public: Big data, surveillance and computational politics. First Monday, https://doi.org/10.5210/fm.v19i7.4901
   Google Scholar
• Valkenburg, P. M. (2022). Theoretical foundations of social media uses and effects. In J. Nesi, E. H. Telzer, & M. J. Prinstein (Eds.), Handbook of adolescent digital media use and mental health (1st ed, pp. 39–60). Cambridge University Press. https://doi.org/10.1017/9781108976237.004
   Google Scholar
• Valkenburg, P. M., Beyens, I., Meier, A., & Vanden Abeele, M. M. P. (2022). Advancing our understanding of the associations between social media use and well-being. Current Opinion in Psychology, 47, 101357. https://doi.org/10.1016/j.copsyc.2022.101357
   PubMed Web of Science ®Google Scholar
• Valkenburg, P., Beyens, I., Pouwels, J. L., van Driel, I. I., & Keijsers, L. (2021). Social media use and adolescents’ self-esteem: heading for a person-specific media effects paradigm. The Journal of Communication, 71(1), 56–78. https://doi.org/10.1093/joc/jqaa039
   Web of Science ®Google Scholar
• Vanden Abeele, M. M. P. (2020). Digital wellbeing as a dynamic construct. Communication Theory. https://doi.org/10.1093/ct/qtaa024
   Google Scholar
• van Driel, I. I., Giachanou, A., Pouwels, J. L., Boeschoten, L., Beyens, I., & Valkenburg, P. M. (2022). Promises and pitfalls of social media data donations. Communication Methods and Measures, 16(4), 266–282. https://doi.org/10.1080/19312458.2022.2109608
   Web of Science ®Google Scholar
• Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146–1151. https://doi.org/10.1126/science.aap9559
   PubMed Web of Science ®Google Scholar
• Vuorre, M., & Przybylski, A. K. 2022. Global well-being and mental health in the internet age. Preprint PsyArXiv. https://doi.org/10.31234/osf.io/9tbjy
   Google Scholar
• Wagner, C., Strohmaier, M., Olteanu, A., Kıcıman, E., Contractor, N., & Eliassi-Rad, T. (2021). Measuring algorithmically infused societies. Nature, 595(7866), 197–204. https://doi.org/10.1038/s41586-021-03666-1
   PubMed Web of Science ®Google Scholar
• Wojcieszak, M., Menchen-Trevino, E., Goncalves, J. F. F., & Weeks, B. (2022). 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, 27(4), 194016122110091. https://doi.org/10.1177/19401612211009160
   Web of Science ®Google Scholar
• World Health Organization. (2022, September 1). Infodemics and misinformation negatively affect people’s health behaviours, new WHO review finds. https://www.who.int/europe/news/item/01-09-2022-infodemics-and-misinformation-negatively-affect-people-s-health-behaviours–new-who-review-finds
   Google Scholar
• Yang, K. -C., Pierri, F., Hui, P. -M., Axelrod, D., Torres-Lugo, C., Bryden, J., & Menczer, F. (2021). The COVID-19 infodemic: Twitter versus Facebook. Big Data & Society, 8(1), 20539517211013860. https://doi.org/10.1177/20539517211013861
   Web of Science ®Google Scholar
• Yang, X., Ram, N., Robinson, T., & Reeves, B. (2019). Using screenshots to predict task switching on smartphones. Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems, 1–6. https://doi.org/10.1145/3290607.3313089
   Google Scholar
• Yee, A. Z. H., Yu, R., Lim, S. S., Lim, K. H., Dinh, T. T. A., Loh, L., Hadianto, A., & Quizon, M. (2022). ScreenLife capture: An open-source and user-friendly framework for collecting screenome data from Android smartphones. Behavior Research Methods, 1–18. https://doi.org/10.3758/s13428-022-02006-z
   Web of Science ®Google Scholar
• Yeykelis, L., Cummings, J. J., & Reeves, B. (2014). Multitasking on a single device: arousal and the frequency, anticipation, and prediction of switching between media content on a computer: Multitasking and Arousal. The Journal of Communication, 64(1), 167–192. https://doi.org/10.1111/jcom.12070
   PubMed Web of Science ®Google Scholar
• Zhang, L. C. (2012). Topics of statistical theory for register‐based statistics and data integration. Statistica Neerlandica, 66(1), 41–63. https://doi.org/10.1111/j.1467-9574.2011.00508.x
   Web of Science ®Google Scholar