We took at cut at the basic idea to determine changes in moods that might indicate purchases. And also mapping to actual behavior. Note pharmaceutical use implications.
By Stanford University, February 4, 2021, in ACM
Machine-learning models can map a persons mood swings and volatility from week to week, according to a new study.
Researchers from Stanford University and the University of Michigan developed a machine learning model that can infer a person's mood from their Facebook posts.
The model was trained using public Facebook postings of close to 3,000 volunteers from an earlier study, then tested on a different set of posts from 640 Facebook users who posted an average of 17 times weekly over 28 weeks. This dataset of 18,000 person-weeks, the largest ever compiled on weekly emotional dynamics, has been made public.
The patterns revealed by the model aligned with the predictions based on classical in-person psychological studies.
Said Stanford's Johannes Eichstaedt, "If this kind of approach is used ethically and legally, with strict privacy protection, we could someday have ways to computationally understand the mind. It could help with diagnosis and pharmaceutical evaluation. It could also help us track the psychological impact of traumatic societal events, such as the Covid pandemic."
From Stanford University
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