Exploiting Social Graph Networks for Emotion Prediction

TL;DR

This paper presents a scalable machine learning approach that leverages social graph networks constructed from mobile data to improve emotion prediction, considering physiological, environmental, and social factors.

Contribution

It introduces a novel architecture that integrates social network information into emotion prediction models without additional data collection costs.

Findings

Social network integration improves prediction accuracy.

The architecture handles dynamic social network distributions.

Graph topology impacts model performance.

Abstract

Emotion prediction plays an essential role in mental health and emotion-aware computing. The complex nature of emotion resulting from its dependency on a person's physiological health, mental state, and his surroundings makes its prediction a challenging task. In this work, we utilize mobile sensing data to predict happiness and stress. In addition to a person's physiological features, we also incorporate the environment's impact through weather and social network. To this end, we leverage phone data to construct social networks and develop a machine learning architecture that aggregates information from multiple users of the graph network and integrates it with the temporal dynamics of data to predict emotion for all the users. The construction of social networks does not incur additional cost in terms of EMAs or data collection from users and doesn't raise privacy concerns. We propose an architecture that automates the integration of a user's social network affect prediction, is capable of dealing with the dynamic distribution of real-life social networks, making it scalable to large-scale networks. Our extensive evaluation highlights the improvement provided by the integration of social networks. We further investigate the impact of graph topology on model's performance.