  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.
