Big data

Aims and scope

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 Metascience using Big Data methods. Research topics include:

• Identification of research topics and trends in large text corpora
• Investigating the scientific communication of psychology researchers on Twitter und Mastodon
• Bibliometric studies using text mining and network analysis
• Methodological and statistical issues in collecting, handling, processing, and analyzing Big Data in psychology
• Leveraging machine learning to automate systematic reviews
• Implications for research supporting infrastructure services in psychology (see PsychTopics)

Central publications

• Bittermann, A., McNamara, D., Simonsmeier, B. A., & Schneider, M. (2023). The Landscape of Research on Prior Knowledge and Learning: A Bibliometric Analysis. Educational Psychology Review, 35, 58.  https://doi.org/10.1007/s10648-023-09775-9

• Bittermann, A. & Rieger, J. (2022). Finding Scientific Topics in Continuously Growing Text Corpora. In A. Cohan et al. (Eds.), Proceedings of the Third Workshop on Scholarly Document Processing (pp. 7–18), Gyeongju, Republic of Korea. Association for Computational Linguistics.  https://aclanthology.org/2022.sdp-1.2/

• Bittermann, A., Batzdorfer, V., Müller, S. M., & Steinmetz, H. (2021). Mining Twitter to detect hotspots in psychology. Zeitschrift für Psychologie, 229(1), 3–14.  https://doi.org/10.1027/2151-2604/a000437

• Batzdorfer, V., Steinmetz, H., Biella, M., & Alizadeh, M. (2021). Conspiracy theories on Twitter: Emerging motifs and temporal dynamics during the COVID-19 pandemic. Journal of Data Science and Analytics.  https://doi.org/10.1007/s41060-021-00298-6

• 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

Events

4th Symposium on Big Data and Research Syntheses in Psychology (with a special focus on Machine Learning & Open Science), 8-10 of May 2023:
Videos and presentations

Research Synthesis and Big Data in Psychology, May 17-21, 2021, online:
Videos and presentations

Research Synthesis incl. Pre-Conference Symposium: Big Data in Psychology, May 27-31, 2019, Dubrovnik, Croatia:
Videos and presentations

Big Data in Psychology 2018, June 7-9, 2018, Trier, Germany:
Videos and presentations