From Post to Values: Mining Schwartz Values of Individuals from Social Media

This paper aims to provide a novel method called Automatic Estimation of Schwartz Values (AESV) from social media, which automatically conducts text categorization based on Schwartz theory. AESV comprises three key components: training, feature extraction and values computation. Specifically, a training corpus is firstly built from the Web for each Schwartz value type and the feature vector is then extracted by using Chi statistics. Last but most important, as for individual values calculation, the personal posts are collected as input data which are converted to a word vector. The similarities between input vector and each value feature vector are used to calculate the individual value priorities. An experiment with 101 participants has been conducted, implying that AESV could obtain the competitive results, which are close to manually measurement by expert survey. In a further experiment, 92 users with different patterns on Sina weibo are tested, indicating that AESV algorithm is robust and could be widely applied in surveying the values for a huge amount of people, which is normally expensive and time-consuming in social science research. It is noted that our work is promising to automatically measure individual's values just using his/her posts on social media.