Beyond the Black Box: A Multimodal Approach to Understanding In-App Communication

Inhaltsverzeichnis

Bibliographische Infos

Cover der Ausgabe: M&K Medien & Kommunikationswissenschaft Jahrgang 74 (2026), Heft 1
Open Access Vollzugriff

M&K Medien & Kommunikationswissenschaft

Jahrgang 74 (2026), Heft 1

Autor:innen:
Verlag
Nomos, Baden-Baden
Copyrightjahr
2026
ISSN-Online
2942-3317
ISSN-Print
1615-634X

Kapitelinformationen

Open Access Vollzugriff

Jahrgang 74 (2026), Heft 1

Beyond the Black Box: A Multimodal Approach to Understanding In-App Communication

Autor:innen:
ISSN-Print
1615-634X
ISSN-Online
2942-3317
Kapitelvorschau:

Literaturverzeichnis

  1. Adam, S., Makhortykh, M., Maier, M., Aigenseer, V., Urman, A., Gil Lopez, T., Christner, C., de León, E., & Ulloa, R. (2024). Improving the quality of individual-level web tracking: Challenges of existing approaches and introduction of a new content and long-tail sensitive academic solution. https://doi.org/10.1177/08944393241287793 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  2. AlDayel, A., & Magdy, W. (2021). Stance detection on Social Media: State of the art and trends. Information Processing & Management, 58(4), 102597. https://doi.org/10.1016/j.ipm.2021.102597 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  3. Andone, I., Błaszkiewicz, K., Eibes, M., Trendafilov, B., Montag, C., & Markowetz, A. (2016). How age and gender affect smartphone usage. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct, 9–12. https://doi.org/10.1145/2968219.2971451 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  4. 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. Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  5. Behre, J., Hölig, S., & Möller, J. (2024). Reuters Institute Digital News Report 2024: Ergebnisse für Deutschland. Verlag Hans-Bredow-Institut. https://www.ssoar.info/ssoar/handle/document/94461 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  6. Bestvater, S. E., & Monroe, B. L. (2023). Sentiment is not stance: Target-aware opinion classification for political text analysis. Political Analysis, 31(2), 235–256. https://doi.org/10.1017/pan.2022.10 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  7. Böhmer, M., Hecht, B., Schöning, J., Krüger, A., & Bauer, G. (2011). Falling asleep with Angry Birds, Facebook and Kindle: A large scale study on mobile application usage. Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services, 47–56. Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  8. Bosch, O. J., Sturgis, P., Kuha, J., & Revilla, M. (2024). Uncovering digital trace data biases: Tracking undercoverage in web tracking data. Communication Methods and Measures, 1–21. https://doi.org/10.1080/19312458.2024.2393165 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  9. Breiter, A., & Hepp, A. (2018). The complexity of datafication: Putting digital traces in context. Communicative figurations: Transforming communications in times of deep mediatization, 387–405. Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  10. 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 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  11. Clemm von Hohenberg, B., Stier, S., Cardenal, A. S., Guess, A. M., Menchen-Trevino, E., & Wojcieszak, M. (2024). Analysis of web browsing data: A guide. Social Science Computer Review, 42(6), 1479–1504. https://doi.org/10.1177/08944393241227868 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  12. Fan, Y., Tu, Z., Li, T., Cao, H., Xia, T., Li, Y., Chen, X., & Zhang, L. (2021). Understanding the long-term dynamics of mobile app usage context via graph embedding. IEEE Transactions on Knowledge and Data Engineering, 35(3). https://doi.org/10.1109/TKDE.2021.3110141 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  13. Ferreira, D., Kostakos, V., & Dey, A. K. (2015). AWARE: Mobile context instrumentation framework. Frontiers in ICT, 2. https://doi.org/10.3389/fict.2015.00006 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  14. 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 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  15. 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 öffnen DOI: 10.5771/1615-634X-2026-1-31
  16. Freelon, D., Pruden, M. L., Malmer, D., Wu, Q., Xia, Y., Johnson, D., Chen, E., & Crist, A. (2024). What’s in your PIE? Understanding the contents of personalized information environments with PIEGraph. Journal of the Association for Information Science and Technology, 75(10), 1119–1133. https://doi.org/10.1002/asi.24869 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  17. Frisson, C., Malacria, S., Bailly, G., & Dutoit, T. (2016). Inspectorwidget: A system to analyze users’ behaviors in their applications. 1548–1554. https://doi.org/10.1145/2851581.2892388 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  18. Gitelman, L. (Ed.). (2013). “Raw data” Is an oxymoron. MIT Press. Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  19. Görland, S. (2020). Medien, Zeit und Beschleunigung: Mobile Mediennutzung in Interimszeiten. Springer. Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  20. Guess, A. M., Nyhan, B., & Reifler, J. (2020). Exposure to untrustworthy websites in the 2016 US election. Nature Human Behaviour, 4(5), 472–480. https://doi.org/10.1038/s41562-020-0833-x Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  21. Hasebrink, U., & Popp, J. (2006). Media repertoires as a result of selective media use. A conceptual approach to the analysis of patterns of exposure. Communications, 31(3), 369–387. https://doi.org/10.1515/COMMUN.2006.023 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  22. Hong, J. I., Boriello, G., Landay, J. A., McDonald, D. W., Schilit, B. N., & Tygar, J. D. (2003). Privacy and security in the location-enhanced world wide web. Proceedings of Fifth International Conference on Ubiquitous Computing: Ubicomp, https://scholar.google.com/citations?view_op=view_citation&hl=en&user=MoFbcc0AAAAJ&cstart=300&pagesize=100&sortby=pubdate&citation_for_view=MoFbcc0AAAAJ:4TOpqqG69KYC. Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  23. Jungherr, A. (2015). Analyzing political communication with digital trace data: The role of twitter messages in social science research. Springer. Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  24. Jungherr, A., & Theocharis, Y. (2017). The empiricist’s challenge: Asking meaningful questions in political science in the age of big data. Journal of Information Technology & Politics, 14(2), 97–109. https://doi.org/10.1080/19331681.2017.1312187 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  25. Jürgens, P., & Stark, B. (2022). Mapping exposure diversity: The divergent effects of algorithmic curation on news consumption. Journal of Communication, 72(3), 322–344. https://doi.org/10.1093/joc/jqac009 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  26. Krieter, P. (2019). Can I record your screen? Mobile screen recordings as a long-term data source for user studies. 1–10. https://doi.org/10.1145/3365610.3365618 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  27. Krieter, P. (2020). Looking inside—mobile screen recordings as a privacy friendly long-term data source to analyze user behavior. Universität Bremen. https://doi.org/10.26092/elib/103 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  28. Krieter, P., Zerrer, P., Puschmann, C., & Geise, S. (2024). Following topics across all apps and media formats: Mobile keyword tracking as a privacy-friendly data source in mobile media research. Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization, 126–131. Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  29. Kühnemann, H. (2021). Anwendungen des Web Scraping in der amtlichen Statistik. AStA Wirtschafts- Und Sozialstatistisches Archiv, 15(1), 5–25. https://doi.org/10.1007/s11943-021-00280-5 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  30. Kulshrestha, J., Oliveira, M., Karaçalık, O., Bonnay, D., & Wagner, C. (2021). Web routineness and limits of predictability: Investigating demographic and behavioral differences using web tracking data. 15, 327–338. https://doi.org/10.48550/arXiv.2012.15112 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  31. Liu, B. (2012). Sentiment analysis and opinion mining. Springer International Publishing. https://doi.org/10.1007/978-3-031-02145-9 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  32. Maier, M., Adam, S., Gil Lopez, T., Makhortykh, M., Bromme, L., Christner, C., de León, E., & Urman, A. (2025). Populist radical-right attitudes, political involvement and selective information consumption: Who tunes out and who prefers attitude-consonant information. Mass Communication and Society, 28(1), 101–129. https://doi.org/10.1080/15205436.2024.2310156 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  33. McMillan, D., McGregor, M., & Brown, B. (2015). From in the wild to in vivo: Video Analysis of Mobile Device Use. 494–503. https://doi.org/10.1145/2785830.2785883 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  34. Meijer, C. I., & Kormelink, G. T. (2015). Checking, sharing, clicking and linking: Changing patterns of news use between 2004 and 2014. Digital Journalism, 3(5), 664–679. https://doi.org/10.1080/21670811.2014.937149 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  35. Menchen-Trevino, E., & and Karr, C. (2012). Researching real-world web use with roxy: Collecting observational web data with informed consent. Journal of Information Technology & Politics, 9(3), 254–268. https://doi.org/10.1080/19331681.2012.664966 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  36. Merten, L. (2021). Block, hide or follow—Personal news curation practices on social media. Digital Journalism, 9(8), 1018–1039. https://doi.org/10.1080/21670811.2020.1829978 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  37. Möller, J., van de Velde, R. N., Merten, L., & Puschmann, C. (2020). Explaining online news engagement based on browsing behavior: Creatures of habit? Social Science Computer Review, 38(5), 616–632. https://doi.org/10.1177/0894439319828012 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  38. Muise, D., Markowitz, D., Reeves, B., Ram, N., & Robinson, T. (2024). (Mis) measurement of political content exposure within the smartphone ecosystem: Investigating common assumptions. Journal of Quantitative Description: Digital Media, 4. Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  39. Munzert, S., & Nyhuis, D. (2019). Die Nutzung von Webdaten in den Sozialwissenschaften. In C. Wagemann, A. Goerres, & M. Siewert (Eds.), Handbuch Methoden der Politikwissenschaft (pp. 1–25). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-16937-4_22-1 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  40. Naab, T. K., Karnowski, V., & Schlütz, D. (2019). 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 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  41. Ohme, J., Araujo, T., Boeschoten, L., Freelon, D., Ram, N., Reeves, B. B., & Robinson, T. N. (2024). Digital trace data collection for social media effects research: APIs, data donation, and (screen) tracking. Communication Methods and Measures, 18(2), 124–141. https://doi.org/10.1080/19312458.2023.2181319 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  42. Parry, D., Davidson, B., Sewall, C., Fisher, J., Mieczkowski, H., & Quintana, D. (2021). A systematic review and meta-analysis of discrepancies between logged and self-reported digital media use. Nature Human Behaviour, 5, 1535–1547. https://doi.org/10.1038/s41562-021-01117-5 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  43. Parry, D., & Toth, R. (2025). Extracting meaningful measures of smartphone usage from Android event log data: A methodological primer. Computational Communication Research, 7(1), 1. Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  44. Peng, T.-Q., & Zhu, J. J. H. (2020). Mobile phone use as sequential processes: From discrete behaviors to sessions of behaviors and trajectories of sessions. J. Comput. Mediat. Commun., 25(2), 129–146. https://doi.org/10.1093/jcmc/zmz029 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  45. Perriam, J., Birkbak ,Andreas, & and Freeman, A. (2020). Digital methods in a post-API environment. International Journal of Social Research Methodology, 23(3), 277–290. https://doi.org/10.1080/13645579.2019.1682840 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  46. Reeves, B., Ram, N., Robinson, T. N., Cummings, J. J., Giles, C. L., Pan, J., Chiatti, A., Cho, M., Roehrick, K., & Yang, X. (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 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  47. Richter, S., & Stier, S. (2022). Learning about the unknown Spitzenkandidaten: The role of media exposure during the 2019 European Parliament elections. European Union Politics, 23(2), 309–329. https://doi.org/10.1177/14651165211051171 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  48. Riebling, J. (2019). The medium data problem in social science. In C. M. Stützer, M. Welker, & M. Egger (Eds.), Computational Social Science in the Age of Big Data. Concepts, Methodologies, Tools, and Applications (pp. 77–103). Herbert von Halem Verlag. Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  49. 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 öffnen DOI: 10.5771/1615-634X-2026-1-31
  50. Sen, I., Flöck, F., Weller, K., Weiß, B., & Wagner, C. (2021). A total error framework for digital traces of human behavior on online platforms. Public Opinion Quarterly, 85(S1), 399–422. https://doi.org/10.1093/poq/nfab018 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  51. Spiekermann, S., & Cranor, L. F. (2009). Engineering privacy. IEEE transactions on software engineering, 35(1), 67–82. https://doi.org/10.1109/TSE.2008.88 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  52. Stier, S., Bleier, A., Bonart, M., Mörsheim, F., Bohlouli, M., Nizhegorodov, M., Posch, L., Maier, J., Rothmund, T., & Staab, S. (2018). Systematically monitoring social media: The case of the German federal election 2017 (GESIS Papers). GESIS - Leibniz-Institut für Sozialwissenschaften. https://doi.org/10.21241/ssoar.56149 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  53. 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 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  54. 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), 194016122090701. https://doi.org/10.1177/1940161220907018 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  55. Stier, S., Mangold, F., Scharkow, M., & Breuer, J. (2021). Post post-broadcast democracy? News exposure in the age of online intermediaries. American Political Science Review, 1–7. https://doi.org/10.1017/s0003055421001222 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  56. Tong, L., Mingyang, Z., Hancheng, C., Yong, L., Sasu, T., & Pan, H. (2020). What apps did you use? : Understanding the long-term evolution of mobile app usage. 66–76. https://doi.org/10.1145/3366423.3380095 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  57. Tong, L., Tong, X., Huandong, W., Zhen, T., Sasu, T., Zhu, H., & Pan, H. (2022). Smartphone app usage analysis: Datasets, methods, and applications. IEEE communications surveys and tutorials, 24(2), 937–966. https://doi.org/10.1109/comst.2022.3163176 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  58. Toth, R., & Trifonova, T. (2021). Somebody’s watching me: Smartphone use ttracking and reactivity. Computers in Human Behavior Reports, 4, 100142. https://doi.org/10.1016/j.chbr.2021.100142 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  59. Trezza, D. (2023). To scrape or not to scrape, this is dilemma. The post-API scenario and implications on digital research. Frontiers in Sociology, 8. https://doi.org/10.3389/fsoc.2023.1145038 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  60. von Andrian-Werburg, M. T., Siegers, P., & Breuer, J. (2023). A Re-evaluation of Online Pornography Use in Germany: A Combination of Web Tracking and Survey Data Analysis. Archives of Sexual Behavior, 1–13. https://doi.org/10.1007/s10508-023-02666-8 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  61. Wieland, M. (2023). Informiert oder (doch nur) abgelenkt? Potenziale und Herausforderungen automatisierter Nachrichtenkontakte in mobilen sozialen Medien. Herbert von Halem Verlag. Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  62. Wojcieszak, M., de Leeuw, S., Menchen-Trevino, E., Lee, S., Huang-Isherwood, K. M., & Weeks, B. (2023). No polarization from partisan news: Over-time evidence from trace data. The International Journal of Press/Politics, 28(3), 601–626. https://doi.org/10.1177/19401612211047194 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  63. Yee, A. Z. H., Yu, R., Lim, S. S., Lim, K. H., Dinh, T. T. A., Loh, L., Hadianto, A., & Quizon, M. (2023). ScreenLife Capture: An open-source and user-friendly framework for collecting screenomes from Android smartphones. Behavior Research Methods, 55(8), 4068–4085. https://doi.org/10.3758/s13428-022-02006-z Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  64. Zerrer, P. T. (2024). Political action and news use of the Fridays for Future movement in Germany. https://doi.org/10.26092/elib/3604 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  65. Zerrer, P., Puschmann, C., & Pressmann, P. (2025, January 28). German Politics Online: A List of social media accounts. https://doi.org/10.17605/OSF.IO/A7SJD Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31
  66. Zhu, J. J. H., Chen, H., Peng, T.-Q., Liu, X. F., & Dai, H. (2018). How to measure sessions of mobile phone use? Quantification, evaluation, and applications. Mobile Media & Communication, 6(2), 215–232. https://doi.org/10.1177/2050157917748351 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-31

Zitation

Download RIS Download BibTex

Downloads

Download Kapitel (PDF) Download kompl. Werk (PDF)

Hinweis

Dieses Werk ist urheberrechtlich geschützt und darf nur nach den Vorgaben der Nutzungs- und Lizenzvereinbarung genutzt werden. Jede darüber hinaus gehende Nutzung des Werkes ist ausdrücklich untersagt und wird verfolgt.

Per E-Mail teilen

Medien