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

On Predicting Personal Values of Social Media Users using Community-Specific Language Features and Personal Value Correlation