  Personal values have significant influence on individuals' behaviors,
preferences, and decision making. It is therefore not a surprise that personal
values of a person could influence his or her social media content and
activities. Instead of getting users to complete personal value questionnaire,
researchers have looked into a non-intrusive and highly scalable approach to
predict personal values using user-generated social media data. Nevertheless,
geographical differences in word usage and profile information are issues to be
addressed when designing such prediction models. In this work, we focus on
analyzing Singapore users' personal values, and developing effective models to
predict their personal values using their Facebook data. These models leverage
on word categories in Linguistic Inquiry and Word Count (LIWC) and correlations
among personal values. The LIWC word categories are adapted to non-English word
use in Singapore. We incorporate the correlations among personal values into
our proposed Stack Model consisting of a task-specific layer of base models and
a cross-stitch layer model. Through experiments, we show that our proposed
model predicts personal values with considerable improvement of accuracy over
the previous works. Moreover, we use the stack model to predict the personal
values of a large community of Twitter users using their public tweet content
and empirically derive several interesting findings about their online behavior
consistent with earlier findings in the social science and social media
literature.
