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

TL;DR

This paper develops a model to predict personal values of Singaporean social media users from Facebook and Twitter data, using adapted LIWC features and value correlations, achieving improved accuracy and revealing behavioral insights.

Contribution

It introduces a novel stack model incorporating personal value correlations and adapted LIWC features for non-English social media data prediction.

Findings

The model outperforms previous approaches in predicting personal values.

Predicted personal values correlate with online behaviors consistent with social science literature.

The approach is scalable and applicable to large social media datasets.

Abstract

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.