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Research Article

Linkage Analysis Revised – Linking Digital Traces and Survey Data

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References

  • Adam, S., Maier, M., Aigenseer, V., Urman, A., & Christner, C. (2019). WebTrack–tracking users’ online information behavior while screen-scraping content. 5th International Conference on Computational Social Science (IC2S2), Amsterdam.
  • Alaybek, B., Dalal, R. S., Fyffe, S., Aitken, J. A., Zhou, Y., Qu, X., Roman, A., & Baines, J. I. (2022). All’s well that ends (and peaks) well? a meta-analysis of the peak-end rule and duration neglect. Organizational Behavior and Human Decision Processes, 170, 104149. https://doi.org/10.1016/j.obhdp.2022.104149
  • Al Baghal, T., Sloan, L., Jessop, C., Williams, M. L., & Burnap, P. (2020). Linking twitter and survey data: The impact of survey mode and demographics on consent rates across three UK studies. Social Science Computer Review, 38(5), 517–532. https://doi.org/10.1177/0894439319828011
  • 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
  • Araujo, T., Wonneberger, A., Neijens, P., & de Vreese, C. (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
  • Baden, C., & Lecheler, S. (2012). Fleeting, fading, or far-reaching? A knowledge-based model of the persistence of framing effects. Communication Theory, 22(4), 359–382. https://doi.org/10.1111/j.1468-2885.2012.01413.x
  • Barbaro, M., & Zeller, T., Jr. (2006). A face is exposed for AOL searcher no. 4417749. New York Times. https://www.nytimes.com/2006/08/09/technology/09aol.html
  • Beraldo, D., Milan, S., de Vos, J., Agosti, C., Nadalic Sotic, B., Vliegenthart, R., Kruikemeier, S., Otto, L. P., Vermeer, S., Chu, X., & Votta, F. (2021). Political advertising exposed: Tracking Facebook ads in the 2021 Dutch elections. https://policyreview.info/articles/news/political-advertising-exposed-trackingfacebook-ads-2021-dutch-elections/1543
  • Berent, M. K., Krosnick, J. A., & Lupia, A. (2016). Measuring voter registration and turnout in surveys: Do official government records yield more accurate assessments? Public Opinion Quarterly, 80(3), 597–621. https://doi.org/10.1093/poq/nfw021
  • Beuthner, C., Weiss, B., Silber, H., Keusch, F., & Schröder, J. (2023). Consent to data linkage for different data domains – the role of question order, question wording, and incentives. International Journal of Social Research Methodology, 1–14. https://doi.org/10.1080/13645579.2023.2173847
  • Beyens, I., Pouwels, J. L., van Driel, I. I., Keijsers, L., & Valkenburg, P. M. (2021). Social media use and adolescents’ well-being: Developing a typology of person-specific effect patterns. Communication Research, 1–26. https://doi.org/10.1177/00936502211038196
  • Bishop, L., & Gray, D. (2017). Chapter 7: Ethical challenges of publishing and sharing social media research data. In K. Woodfield (Ed.), The ethics of online research (pp. 159–187). Emerald Publishing Limited.
  • 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
  • Bolger, N., & Laurenceau, J.-P. (2013). Intensive longitudinal methods: An introduction to diary and experience sampling research. Guilford Press.
  • Breuer, J., Bishop, L., & Kinder-Kurlanda, K. (2020). The practical and ethical challenges in acquiring and sharing digital trace data: Negotiating public-private partnerships. New Media & Society, 22(11), 2058–2080. https://doi.org/10.1177/1461444820924622
  • 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, 1–20. https://doi.org/10.1080/1369118X.2022.2097015
  • Brinberg, M., Ram, N., Yang, X., Cho, M.-J., Sundar, S. S., Robinson, T. N., & Reeves, B. (2021). The idiosyncrasies of everyday digital lives: Using the human screenome project to study user behavior on smartphones. Computers in Human Behavior, 114, 106570. https://doi.org/10.1016/j.chb.2020.106570
  • Brosius, A., van Elsas, E. J., & de Vreese, C. H. (2020). Bad news, declining trust? Effects of exposure to economic news on trust in the European union. International Journal of Public Opinion Research, 32(2), 223–242. https://doi.org/10.1093/ijpor/edz025
  • Brouwer, A.-M., van Beers, J. J., Sabu, P., Stuldreher, I. V., Zech, H. G., & Kaneko, D. (2021). Measuring implicit approach–avoidance tendencies towards food using a mobile phone outside the lab. Foods, 10(7), 1440. https://doi.org/10.3390/foods10071440
  • Calderwood, L., & Lessof, C. (2009). Enhancing longitudinal surveys by linking to administrative data. In P. Lynn (Ed.), Methodology of longitudinal surveys (pp. 55–72). John Wiley & Sons.
  • Cardenal, A. S., Aguilar-Paredes, C., Galais, C., & Pérez-Montoro, M. (2019). Digital technologies and selective exposure: How choice and filter bubbles shape news media exposure. The International Journal of Press/politics, 24(4), 465–486. https://doi.org/10.1177/1940161219862988
  • Chaffee, S. H., & Metzger, M. J. (2001). The end of mass communication? Mass Communication & Society, 4(4), 365–379. https://doi.org/10.1207/S15327825MCS0404\_3
  • Chiatti, A., Yang, X., Brinberg, M., Cho, M. J., Gagneja, A., Ram, N., Reeves, B., & Giles, C. L. (2017). Text extraction from smartphone screenshots to archive in situ media behavior. Proceedings of the Knowledge Capture Conference, Austin, Texas, USA, 1–4.
  • 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
  • De Haan-Rietdijk, S., Voelkle, M. C., Keijsers, L., & Hamaker, E. L. (2017). Discrete-vs. continuous-time modeling of unequally spaced experience sampling method data. Frontiers in Psychology, 8, 1849. https://doi.org/10.3389/fpsyg.2017.01849
  • 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
  • De Vreese, C. H., & Neijens, P. (2016). Measuring media exposure in a changing communications environment. Communication Methods and Measures, 10(2–3), 69–80. https://doi.org/10.1080/19312458.2016.1150441
  • Doherty, S. T., Lemieux, C. J., & Canally, C. (2014). Tracking human activity and well-being in natural environments using wearable sensors and experience sampling. Social Science & Medicine, 106, 83–92. https://doi.org/10.1016/j.socscimed.2014.01.048
  • Eveland, W. P., Jr., Hutchens, M. J., & Shen, F. (2009). Exposure, attention, or “use” of news? assessing aspects of the reliability and validity of a central concept in political communication research. Communication Methods and Measures, 3(4), 223–244. https://doi.org/10.1080/19312450903378925
  • Feldman, L., Stroud, N. J., Bimber, B., & Wojcieszak, M. (2013). Assessing selective exposure in experiments: The implications of different methodological choices. Communication Methods and Measures, 7(3–4), 172–194. https://doi.org/10.1080/19312458.2013.813923
  • Froomkin, A. M. (2019). Big data: Destroyer of informed consent. Yale Journal of Law and Technology, 21(27), 27–54.
  • Geers, S., Bos, L., & de Vreese, C. H. (2018). Effects of issue and poll news on electoral volatility: Conversion or crystallization? Acta Politica, 54(4), 521–539. https://doi.org/10.1057/s41269-018-0089-x
  • Geiß, S. (2019). The media’s conditional agenda-setting power: How baselines and spikes of issue salience affect likelihood and strength of agenda-setting. Communication Research, 49(2), 296–323. https://doi.org/10.1177/0093650219874968
  • Groot Kormelink, T., & Costera Meijer, I. (2018). What clicks actually mean: Exploring digital news user practices. Journalism, 19(5), 668–683. https://doi.org/10.1177/1464884916688290
  • Guess, A., Aslett, K., Tucker, J., Bonneau, R., & Nagler, J. (2021). Cracking open the news feed: Exploring what U.S. Facebook users see and share with large-scale platform data. Journal of Quantitative Description: Digital Media, 1, 1–48. https://doi.org/10.51685/jqd.2021.006
  • Guess, A., Munger, K., Nagler, J., & Tucker, J. (2019). How accurate are survey responses on social media and politics? Political Communication, 36(2), 241–258. https://doi.org/10.1080/10584609.2018.1504840
  • Haenschen, K. (2020). Self-reported versus digitally recorded: Measuring political activity on facebook. Social Science Computer Review, 38(5), 567–583. https://doi.org/10.1177/0894439318813586
  • Haim, M., & Nienierza, A. (2019). Computational observation: Challenges and opportunities of automated observation within algorithmically curated media environments using a browser plug-in. Computational Communication Research, 1(1), 79–102. https://doi.org/10.5117/ccr2019.1.004.haim
  • Hamaker, E. L., Asparouhov, T., Brose, A., Schmiedek, F., & Muthén, B. (2018). At the frontiers of modeling intensive longitudinal data: Dynamic structural equation models for the affective measurements from the COGITO study. Multivariate Behavioral Research, 53(6), 820–841. https://doi.org/10.1080/00273171.2018.1446819
  • Hefner, D., Rothmund, T., Klimmt, C., & Gollwitzer, M. (2011). Implicit measures and media effects research: Challenges and opportunities. Communication Methods and Measures, 5(3), 181–202. https://doi.org/10.1080/19312458.2011.597006
  • Jürgens, P., Stark, B., & Magin, M. (2020). Two half-truths make a whole? On bias in self-reports and tracking data. Social Science Computer Review, 38(5), 600–615. https://doi.org/10.1177/0894439319831643
  • Keijsers, L., & van Roekel, E. (2018). Longitudinal methods in adolescent psychology: Where could we go from here? and should we? In Reframing adolescent research (pp. 56–77). Routledge.
  • Koch, T. K., Romero, P., & Stachl, C. (2022). Age and gender in language, emoji, and emoticon usage in instant messages. Computers in Human Behavior, 126, 106990. https://doi.org/10.1016/j.chb.2021.106990
  • Krieter, P. (2019). Can i record your screen? mobile screen recordings as a long-term data source for user studies. Proceedings of the 18th International Conference on Mobile and Ubiquitous Multimedia, Pisa, Italy, 1–10.
  • Krieter, P., & Breiter, A. (2018). Analyzing mobile application usage: Generating log files from mobile screen recordings. Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services, 1–10. https://doi.org/10.1145/3229434.3229450
  • Kroon, A., Welbers, K., Trilling, D., & Atteveldt, W. V. (2023). Using transfer-learning for measuring media effects: Challenges in automated analysis of multiple formats, languages, and modalities. Communication Methods and Measures.
  • Kümpel, A. S. (2022). Social media information environments and their implications for the uses and effects of news: The PINGS framework. Communication Theory, 32(2), 223–242. https://doi.org/10.1093/ct/qtab012
  • Maier, J., Hampe, J. F., & Jahn, N. (2016). Breaking out of the lab: Measuring real-time responses to televised political content in real-world settings. Public Opinion Quarterly, 80(2), 542–553. https://doi.org/10.1093/poq/nfw010
  • Makhortykh, M., de León, E., Urman, A., Christner, C., Sydorova, M., Adam, S., Maier, M., & Gil-Lopez, T. (2022). Panning for gold: Lessons learned from the platform-agnostic automated detection of political content in textual data. arXiv Preprint arXiv: 220700489. https://arxiv.org/abs/2207.00489
  • Marino, C., Finos, L., Vieno, A., Lenzi, M., & Spada, M. M. (2017). Objective Facebook behaviour: Differences between problematic and non-problematic users. Computers in Human Behavior, 73, 541–546. https://doi.org/10.1016/j.chb.2017.04.015
  • McFall, S. L., Conolly, A., & Burton, J. (2014). Collecting biomarkers and biological samples using trained interviewers. Lessons from a Pilot study. Survey Research Methods, 8, 57–66. https://doi.org/10.18148/srm/2014.v8i1.5471
  • Menchen-Trevino, E. (2013). Collecting vertical trace data: Big possibilities and big challenges for multi-method research. Policy & Internet, 5(3), 328–339. https://doi.org/10.1002/1944-2866.poi336
  • Menchen-Trevino, E. (2016). Web historian: Enabling multi-method and independent research with real-world web browsing history data. iConference 2016 Proceedings. https://doi.org/10.9776/16611
  • Naab, T. K., Karnowski, V., & Schlütz, D. (2018). Reporting mobile social media use: How survey and experience sampling measures differ. Communication Methods and Measures, 13(2), 126–147. https://doi.org/10.1080/19312458.2018.1555799
  • Napa Scollon, C., Prieto, C.-K., & Diener, E. (2009). Experience sampling: Promises and pitfalls, strength and weaknesses. In E. Diener (Ed.), Assessing well-being: The collected works of Ed Diener (pp. 157–180). Springer Netherlands.
  • Niederdeppe, J. (2016). Meeting the challenge of measuring communication exposure in the digital age. Communication Methods and Measures, 10(2–3), 170–172. https://doi.org/10.1080/19312458.2016.1150970
  • Ohme, J. (2020). Mobile but not mobilized? Differential gains from mobile news consumption for citizens’ political knowledge and campaign participation. Digital Journalism, 8(1), 103–125. https://doi.org/10.1080/21670811.2019.1697625
  • Ohme, J., Araujo, T., Boeschoten, L., Freelon, D., Ram, N., Reeves, B. B., & Robinson, T. N. (2023). Digital trace data collection for social media effects research: APIs, data donation, and (screen) tracking. Communication Methods and Measures, 1–18. https://doi.org/10.1080/19312458.2023.2181319
  • 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
  • Otto, L. P., & Kruikemeier, S. (2023). The smartphone as a tool for mobile communication research: Assessing mobile campaign perceptions and effects with experience sampling. New Media & Society.
  • 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
  • Otto, L. P., Thomas, F., & Maier, M. (2018). Everyday dynamics of media skepticism and credibility: An ambulatory assessment study. In K. Otto & A. Köhler (Eds.), Trust in media and journalism. Empirical perspectives on ethics, norms, impacts and populism in Europe (pp. 111–133). Springer VS.
  • Otto, L. P., Thomas, F., Maier, M., & Ottenstein, C. (2020). Only one moment in time? Investigating the dynamic relationship of emotions and attention toward political information with mobile experience sampling. Communication Research, 47(8), 1131–1154. https://doi.org/10.1177/0093650219872392
  • 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
  • Peng, Y., Lock, I., & Sallah, A. A. (2023). Automated visual analysis for the study of social media effects: Opportunities, approaches, and challenges. Communication Methods and Measures.
  • Pipal, C., Song, H., & Boomgaarden, H. G. (2023). If you have choices, why not choose (and share) all of them? a multiverse approach to understanding news engagement on social media. Digital Journalism, 11(2), 255–275. https://doi.org/10.1080/21670811.2022.2036623
  • Puschmann, C., & Pentzold, C. (2021). A field comes of age: Tracking research on the internet within communication studies, 1994 to 2018. Internet Histories, 5(2), 135–153. https://doi.org/10.1080/24701475.2020.1749805
  • 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
  • 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
  • Salganik, M. J. (2019). Bit by bit: Social research in the digital age. Princeton University Press.
  • Sanna, L., Romano, S., Corona, G., & Agosti, C. (2021). YTTREX: Crowdsourced analysis of YouTube’s recommender system during COVID-19 pandemic. In J. A. Lossio-Ventura, J. C. Valverde Rebaza, E. Díaz, & H. Alatrista-Salas (Eds.), Information management and big data (pp. 107–121). Springer International Publishing.
  • 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
  • Scharkow, M. (2017). Content Analysis, Automatic. In J. Matthes, C. S. Davis, & R. F. Potter (Eds.), The International Encyclopedia of Communication Research Methods (pp. 1–14). Hoboken, New Jersey, USA: John Wiley & Sons, Inc. https://doi.org/10.1002/9781118901731.iecrm0043
  • Scharkow, M. (2019). The reliability and temporal stability of self-reported media exposure: A meta-analysis. Communication Methods and Measures, 13(3), 198–211. https://doi.org/10.1080/19312458.2019.1594742
  • Scharkow, M., & Bachl, M. (2017). How measurement error in content analysis and self-reported media use leads to minimal media effect findings in linkage analyses: A simulation study. Political Communication, 34(3), 323–343. https://doi.org/10.1080/10584609.2016.1235640
  • Schnauber-Stockmann, A., & Karnowski, V. (2020). Mobile devices as tools for media and communication research: A scoping review on collecting self-report data in repeated measurement designs. Communication Methods and Measures, 14(3), 145–164. https://doi.org/10.1080/19312458.2020.1784402
  • Schuck, A. R. T., Vliegenthart, R., & De Vreese, C. H. (2016). Matching theory and data: Why combining media content with survey data matters. British Journal of Political Science, 46(1), 205–213. https://doi.org/10.1017/S0007123415000228
  • Silber, H., Breuer, J., Beuthner, C., Gummer, T., Keusch, F., Siegers, P., Stier, S., & Weiß, B. (n.d.). Linking surveys and digital trace data: Insights from two studies on determinants of data sharing behaviour. Journal of the Royal Statistical Society, n/a. https://doi.org/10.1111/rssa.12954
  • Slater, M. D. (2007). Reinforcing spirals: The mutual influence of media selectivity and media effects and their impact on individual behavior and social identity. Communication Theory, 17(3), 281–303. https://doi.org/10.1111/j.1468-2885.2007.00296.x
  • Slater, M. D. (2015). Reinforcing spirals model: Conceptualizing the relationship between media content exposure and the development and maintenance of attitudes. Media Psychology, 18(3), 370–395. https://doi.org/10.1080/15213269.2014.897236
  • Statista. (2021). Top U.S. smartphone activities 2018. https://www.statista.com/statistics/187128/leading-us-smartphone-activities/. 09 11, 2023.
  • 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
  • Stier, S., Kirkizh, N., Froio, C., & Schroeder, R. (2020). Populist attitudes and selective exposure to online news: A cross-country analysis combining web tracking and surveys. The International Journal of Press/politics, 25(3), 426–446. https://doi.org/10.1177/1940161220907018
  • Struminskaya, B., Lugtig, P., Keusch, F., & Höhne, J. K. (2020). Augmenting surveys with data from sensors and apps: Opportunities and challenges. Social Science Computer Review, 0894439320979951. https://doi.org/10.1177/0894439320979951
  • 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, 40(4), 1126–1150. https://doi.org/10.1177/02654075221104268
  • Thomas, F. (2022). A methodological framework for analyzing the appearance and duration of media effects. Journal of Communication, 72(3), 401–428. https://doi.org/10.1093/joc/jqac013
  • Thomas, F., Shehata, A., Otto, L. P., Möller, J., & Prestele, E. (2021). How to capture reciprocal communication dynamics: Comparing longitudinal statistical approaches in order to analyze within- and between-person effects. Journal of Communication, 71(2), 187–219. https://doi.org/10.1093/joc/jqab003
  • 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
  • Valkenburg, P. M., 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. Journal of Communication, 71(1), 56–78. https://doi.org/10.1093/joc/jqaa039
  • Valkenburg, P. M., & Peter, J. (2013). The differential susceptibility to media effects model. Journal of Communication, 63(2), 221–243. https://doi.org/10.1111/jcom.12024
  • 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. Online First. https://doi.org/10.1080/19312458.2022.2109608
  • 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), 1–11. https://doi.org/10.1016/j.chbr.2021.100090
  • Vermeer, S., Van Remoortere, A., & Vliegenthart, R. (2022). Still going strong? the role of traditional media in the 2021 Dutch parliamentary elections. Acta Politica, 1–17. https://doi.org/10.1057/s41269-022-00270-7
  • Vliegenthart, R. (2014). Moving up. Applying aggregate level time series analysis in the study of media coverage. Quality & Quantity, 48(5), 2427–2445. https://doi.org/10.1007/s11135-013-9899-0
  • Vliegenthart, R., Schuck, A. R. T., Boomgaarden, H. G., & De Vreese, C. H. (2008). News coverage and support for European integration, 1990–2006. International Journal of Public Opinion Research, 20(4), 415–439. https://doi.org/10.1093/ijpor/edn044
  • Vogler, D., Weston, M., Ryffel, Q., Rauchfleisch, A., Jürgens, P., Eisenegger, M., Schwaiger, L., & Christen, U. (2023). Mobile news consumption and its relation to young adults’ knowledge about and participation in referendums. Media and Communication, 11(1), 6–18. https://doi.org/10.17645/mac.v11i1.6029
  • Vorderer, P., Krömer, N., & Schneider, F. M. (2016). Permanently online – permanently connected: Explorations into university students’ use of social media and mobile smart devices. Computers in Human Behavior, 63, 694–703. https://doi.org/10.1016/j.chb.2016.05.085
  • Welbers, K., Loecherbach, F., Lin, Z., & Trilling, D. (2023). Anything you would like to share: Evaluating drop-out and accuracy of two data donation studies. 73rd Annual ICA Conference, Toronto, Canada.
  • Wojcieszak, M., von Hohenberg, B. C., Casas, A., Menchen-Trevino, E., de Leeuw, S., Gonçalves, A., & Boon, M. (2022). Null effects of news exposure: A test of the (un) desirable effects of a ‘news vacation’and ‘news binging’. Humanities and Social Sciences Communications, 9(1), 1–10. https://doi.org/10.1057/s41599-022-01423-x