Up to now, there was no way to observe and track the affective impacts of the massive amount of complex visual stimuli that people encounter “in the wild” during their many hours of digital life. In this paper, we propose and illustrate how recent advances in AI—trained ensembles of deep neural networks—can be deployed on new data streams that are long sequences of screenshots of study participants’ smartphones obtained unobtrusively during everyday life. We obtained affective valence and arousal ratings of hundreds of images drawn from existing picture repositories often used in psychological studies, and a new screenshot repository chronicling individuals’ everyday digital life from both N = 832 adults and an affect computation model (Parry & Vuong, 2021). Results and analysis suggest that (a) our sample rates images similarly to other samples used in psychological studies, (b) the affect computation model is able to assign valence and arousal ratings similarly to humans, and (c) the resulting computational pipeline can be deployed at scale to obtain detailed maps of the affective space individuals travel through on their smartphones. Leveraging innovative methods for tracking the emotional content individuals encounter on their smartphones, we open the possibility for large-scale studies of how the affective dynamics of everyday digital life shape individuals’ moment-to-moment experiences and well-being.
Rocklin, M. L., Garròn Torres, A. A., Reeves, B., Robinson, T. N., & Ram, N. (2023). The Affective Dynamics of Everyday Digital Life: Opening Computational Possibility. Affective Science. https://doi.org/10.1007/s42761-023-00202-4