Abstract
Social media can be beneficial in detecting early signs of emotional difficulty. We utilized the Satisfaction with Life (SWL) index as a cognitive health measure and presented models to predict an individual’s SWL. Our models considered ego, temporal, and link Facebook features collected through the myPersonality.org project. We demonstrated the strong correlation between Big 5 personality features and SWL, and we used this insight to build two-step Random Forest Regression models from ego features. As an intermediate step, the two-step model predicts Big 5 features that are later incorporated in the SWL prediction models. We showed that the two-step approach more accurately predicted SWL than one-step models. By incorporating temporal features we demonstrated that “mood swings” do not affect SWL prediction and confirmed SWL’s high temporal consistency. Strong link features, such as the SWL of top friends or significant others, increased prediction accuracy. Our final model incorporated ego features, predicted personality features, and the SWL of strong links. The final model out-performed previous research on the same dataset by 45%.
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Collins, S., Sun, Y., Kosinski, M., Stillwell, D., Markuzon, N. (2015). Are You Satisfied with Life?: Predicting Satisfaction with Life from Facebook. In: Agarwal, N., Xu, K., Osgood, N. (eds) Social Computing, Behavioral-Cultural Modeling, and Prediction. SBP 2015. Lecture Notes in Computer Science(), vol 9021. Springer, Cham. https://doi.org/10.1007/978-3-319-16268-3_3
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DOI: https://doi.org/10.1007/978-3-319-16268-3_3
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