The availability of Big Data is becoming increasingly common in many felds such as business, computer science, government, and the social and behavioral sciences including psychology.
There are four key characteristics that may qualify data as Big Data, namely Volume, Velocity, Variety, and Veracity. High-volume data refers to the size of the dataset. If it is too large, it can lead to problems with storage and analysis. High-velocity data means that the data are received at a high rate and/or have to be processed within a short period of time (e.g., real-time and interactive processing).
High-variety data are data consisting of many types of structured and unstructured data containing a mixture of text, pictures, videos, and numbers. Another characteristic of Big Data is the veracity, which indicates the importance of the quality (or truthfulness) of the data. Big Data with potential high relevance for psychology include social media data, health/physiological tracker data, geolocation data, dynamic public records, travel route data, and behavioral and genetic data.
The overall aim of this research area is to address methods and applications using Big Data in psychology. In line with the scientometric research tradition at ZPID, the current focus is on scientometrics and meta-psychology using Big Data methods. Research topics include:
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Bittermann, A. & Fischer, A. (2018). How to identify hot topics in psychology using topic modeling. Zeitschrift für Psychologie, 226, 3–13. https://doi.org/10.1027/2151-2604/a000318