| Kantenartikel | Psychoinformatics

Knots in the knowledge map




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Psychoinformatics describes the use of computer science methods to better understand psychological phenomena. [1] For example, attempts are made to use digital footprints to predict mental states such as depressive moods [2] [2] or personality traits [3]from digital footprints. The most frequently analysed digital footprints are currently data from social media or smartphones. In social media, for example, likes or posted texts are analysed [4]on smartphones, on the other hand, call behaviour, GPS data or app usage. [5]

Within the discipline of psychoinformatics, data volumes can quickly take on large proportions, which arrive on the server in different formats and at different speeds (the three Vs of Big Data: Volume, Variety and Veracity). This also has an impact on data analysis strategies, which increasingly rely on machine learning beyond traditional statistical methods in order to be able to take into account non-linear complex relationships within the data.

The two terms digital phenotyping and mobile sensing can be located in the context of the discipline of psychoinformatics. While mobile sensing is aimed, among other things, at detecting psychological variables in data from mobile devices (currently primarily smartphone data), digital phenotyping is a broader term that encompasses all digital footprints of an Internet of Things that can be used to predict psychological variables. [6]

Comparability with analogue phenomena

For a long time, it was very time-consuming for psychology to conduct research in people’s everyday lives. This means, for example, that observational studies are carried out to understand how people behave in their natural environment. It is therefore not surprising that in a previously analogue age, self-report studies with questionnaires were increasingly carried out and observational studies in everyday life were much rarer due to the high resources required. [7]

The study of digital footprints now makes it possible to observe people in their everyday lives. As the smartphone is a constant companion for many people, there are new opportunities to investigate people’s behaviour by studying their digital footprints, even over longer periods of time in different life situations. However, this also poses major ethical challenges, especially when it comes to protecting the identity of the study participants. [8] Ideally, psychoinformatic studies are conducted with privacy-by-design principles. [9]this means that care must be taken to ensure that the study participants cannot be identified on the basis of the recorded data.

Social relevance

The companies behind social media work with business models of surveillance capitalism [10], [11] by analysing the data traces of users in order to understand what characteristics and preferences these people possess.[12] At present, the interfaces to social media are mostly closed (APIcalypse) [13], [14], making research in this area more difficult. Against this background, it seems particularly important to use psychoinformatics methods to demonstrate how well digital footprints predict different variables, as otherwise it remains unclear with what precision the tech industry can identify key characteristics of its users. At the moment, social media and its data business model are unfortunately mostly a black box. This is a major problem due to the negative influences occurring on the platforms, such as the spread of misinformation campaigns and hate speech, as well as the loss of privacy.

However, the social relevance of the psychoinformatics discipline lies not only in understanding how social media works, but also in considering how digital footprints can be used ethically for meaningful applications in psychological health management, for example to support psychodiagnostics or psychological psychotherapy. The interaction of self-report data and the study of digital footprints in particular may also lead to new key findings in psychology. [15]


  1. Montag, C., und Baumeister, H. (Eds.). (2022). Digital Phenotyping and Mobile Sensing: New Developments in Psychoinformatics. Springer Nature.
  2. Seppälä, J., De Vita, I., Jämsä, T., Miettunen, J., Isohanni, M., Rubinstein, K., und Bulgheroni, M. (2019). Mobile phone and wearable sensor-based mHealth approaches for psychiatric disorders and symptoms: systematic review. JMIR Mental Health, 6(2), e9819.
  3. Marengo, D., Elhai, J. D., und Montag, C. (2023). Predicting Big Five personality traits from smartphone data: A meta‐analysis on the potential of digital phenotyping. Journal of Personality, 91(6), 1410-1424.
  4. Marengo, D., und Montag, C. (2020). Digital phenotyping of big five personality via facebook data mining: a meta-analysis. Digital Psychology, 1(1), 52-64.
  5. Montag, C., Baumeister, H., Kannen, C., Sariyska, R., Meßner, E. M., und Brand, M. (2019). Concept, possibilities and pilot-testing of a new smartphone application for the social and life sciences to study human behavior including validation data from personality psychology. J, 2(2), 102–115.
  6. Montag, C., Elhai, J. D., und Dagum, P. (2021). Show me your smartphone … and then I will show you your brain structure and brain function. Human Behavior and Emerging Technologies, 3(5), 891–897.
  7. Simon, A. F., and Wilder, D. (2022). Methods and measures in social and personality psychology: a comparison of JPSP publications in 1982 and 2016. The Journal of Social Psychology, 1–17.
  8. Montag, C., Sindermann, C., und Baumeister, H. (2020). Digital phenotyping in psychological and medical sciences: a reflection about necessary prerequisites to reduce harm and increase benefits. Current Opinion in Psychology, 36, 19-24.
  9. Schaar, P. (2010). Privacy by design. Identity in the Information Society, 3(2), 267-274.
  10. Zuboff, S. (2015). Big other: surveillance capitalism and the prospects of an information civilization. Journal of Information Technology, 30(1), 75-89.
  11. Montag, C., und Elhai, J. D. (2023). On Social Media Design,(Online-) Time Well-spent and Addictive Behaviors in the Age of Surveillance Capitalism. Current Addiction Reports, 1-7.
  12. Montag, C. (2021). Du gehörst uns!: Die psychologischen Strategien von Facebook, TikTok, Snapchat & Co. Karl Blessing Verlag.
  13. Bruns, A. (2019). After the ‘APIcalypse’: Social media platforms and their fight against critical scholarly research. Information, Communication & Society, 22(11), 1544-1566.
  14. Montag, C., Hegelich, S., Sindermann, C., Rozgonjuk, D., Marengo, D., und Elhai, J. D. (2021). On corporate responsibility when studying social media use and well-being. Trends in Cognitive Sciences, 25(4), 268–270.
  15. Montag, C., Dagum, P., Hall, B. J., und Elhai, J. D. (2022). Do we still need psychological self-report questionnaires in the age of the Internet of Things?. Discover Psychology, 2(1), 1.