Description: The expansion of the Internet, together with the capabilities of modern connected devices, result in a plethora of data that promise fascinating opportunities to understand individuals’ digital behaviours. This course will teach students how to measure digital behaviours using web trackers and data donations, and how to combine these approaches with online surveys. The course has the following learning objectives:
Develop an understanding of what web tracking data and data donations are. Learn how web tracking data and data donations can be collected and analysed, and how they can be combined with surveys. Recognise the challenges and errors that might arise in every step of the process of collecting and analysing both data sources. Develop best practices when using this type of data, specifically, strategies to quantify, minimise and report potential errors. Evaluate the limits of their own and others’ web tracking and data donation collection strategies.
To aid students achieve the above learning outcomes, the course will have two interactive activities:
Using the unique TRI-POL open access datasets, a cross-national longitudinal survey combined with web tracking data, students will familiarise with a web tracking dataset. Likewise, students will learn how to use computational methods such as Monte Carlo simulations and machine learning to quantify the data quality of digital trace data.
Students will get hand-on experience about a specific type of data donation: screenshots and video recordings of the Digital Wellbeing / Screen Time from Android and iOS, which provide information of the time spent on apps and webs from individuals’ devices. They will learn how to automatize the extraction of information from those screenshots. Specifically, students will learn how to run an R script that sends images to Google Vision API, extracts the text from the images, and creates a workable structured dataset.