Understanding and predicting social behaviour using telecom- and sensor-based digital trace data. Fusion of traditional and innovative data collection methods.

Digital traces of human behavior can be collected from multiple sources, such as websites, social media, smartphone apps or various sensors. They are nonintrusive in terms of how they are collected, provide high precision and granularity. However, these methods are not able to replace data collected by using traditional research techniques (such as surveys and interviews).

The main objective of the 3 year research project is to create innovation in methodology by bringing together the two methodological, data collection paradigms. We will develop and deploy a smartphone based solution (app) that is able provide a unified platform for sensor-based information collection and traditional survey data collection. By doing so we will be able to scrutinize various social problems, apply MI models to understand and predict human behavior, and support policy-related decision making.

Keywords: digital traces, big data, survey data, data fusion, public policy