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preferred-citation:
  type: article
  title: "The Affective Dynamics of Everyday Digital Life: Opening Computational Possibility"
  authors:
  - family-names: "Rocklin"
    given-names: "Maia L."
    affiliation: Stanford University, Stanford, USA
  - family-names: "Garron Torres"
    given-names: "Anna Angelina"
    affiliation: Stanford University, Stanford, USA
  - family-names: "Reeves"
    given-names: "Byron"
    affiliation: Stanford University, Stanford, USA
  - family-names: "Robinson"
    given-names: "Thomas N."
    affiliation: Stanford University, Stanford, USA
  - family-names: "Ram"
    given-names: "Nilam"
    affiliation: Stanford University, Stanford, USA
  keywords:
  - longitudinal
  - intraindividual variability
  - screenomics
  - machine learning
  - software sustainability
  - media effects
  doi: 
  date-released: 
  repository: "https://github.com/The-Change-Lab/affectivedynamics"
  abstract: "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."