All moves in digital environments cause log data. Even the logs are not generally available for researchers once accessed, those can draw through picture of human activities. It is widely recognized that we can do nothing without leaving digital traces in sociotechnical environments. Human artefacts are the second digital trace to study. Those consist of text, pictures, emoticons etc. That people intentionally (or unintentionally in some cases) post to digital environments and leave behind. The third digital trace is social network and who are in mutual interaction. The fourth set of traces consist of activities executed in certain services e.g., sharing some content created by other people.
Pros and Cons
The most evident benefit of using digital traces is the non-disruptiveness of the data collection. Data can be collected without notification in most cases. And even people are notified there is no action required. This is in many cases a blessing for the researchers as they very often struggle with low participation rate and yield.
The power of data scraping as data acquisition method is collecting data from other websites, services or applications. Very soon researchers end up with large amount of data and need to efficiently manage and analyse it. Similar to any other pattern recognition or automated text analysis the data can analysed by utilising those automated methods yet those require some technical expertise along with context knowledge.
Digital traces is a way to cost efficiently conduct ethonographic research in digital environments. Even the technology does large amount of data management routines the sorting algorithms are the critical factor. The primary function of those is put the collection of data element into predesignated order the whole research setting can fall due to ill designed algorithm. It is also noteworthy that in most cases human capability (or attention span) do not allow manual analysis of the data.
CO:RE Methodological practices/examples: studying children and youth online: Qualitative Analysis of Social Media Trace Data Concerning Online Peer Support for Adolescent Sexting (Hartikainen, Razi, Wisniewsky, 2022)
CO:RE Methodological practices/examples: studying children and youth online: Researching Adolescents’ Digital Technology Usage with a Smartphone-based Ecological Momentary Assessment (EMA) (Lebedikova, Tkaczyk, Blahosova, Elavsky, Smahel, 2022)
Jussila, J., Vuori, V., Okkonen, J. and Helander, N. (2017) Reliability and perceived value of sentiment analysis for Twitter data. in Proceedings of the 5th International Conference on Strategic Innovative Marketing.
Rafaeli, A., Ashtar, S., & Altman, D. (2019). Digital Traces: New Data, Resources, and Tools for Psychological-Science Research. Current Directions in Psychological Science, 28(6), 560–566. https://doi.org/10.1177/0963721419861410