Identifying Social Satisfaction from Social Media

TL;DR

This study develops a regression-based method to predict social satisfaction from social media data, enabling real-time large-scale assessment of social and economic well-being.

Contribution

The paper introduces a novel approach to estimate social satisfaction using behavioral and linguistic features from social media, validated with a large participant sample and economic data.

Findings

Social media features significantly predict social satisfaction.

Predicted social satisfaction correlates with local economic indexes.

Method enables scalable, real-time social well-being assessment.

Abstract

We demonstrate the critical need to identify social situation and instability factors by acquiring public social satisfaction in this research. However, subject to the large amount of manual work cost in subject recruitment and data processing, conventional self-reported method cannot be implemented in real time or applied in large scale investigation. To solve the problem, this paper proposed an approach to predict users' social satisfaction, especially for the economy-related satisfaction based on users' social media records. We recruited 2,018 Cantonese active participants from each city in Guangdong province according to the population distribution. Both behavioral and linguistic features of the participants are extracted from the online records of social media, i.e., Sina Weibo. Regression models are used to predict Sina Weibo users' social satisfaction. Furthermore, we consult the economic indexes of Guangdong in 2012, and calculate the correlations between these indexes and the predicted social satisfaction. Results indicate that social satisfaction can be significantly expressed by specific social media features; local economy satisfaction has significant positive correlations with several local economy indexes, which supports that it is reliable to predict social satisfaction from social media.