Measuring Self-monitoring Using Facebook Online Data Based on Snyder's Psychological Theories

Measuring psychological concept self-monitoring (SM) is useful for understanding how people employ impression management strategies in their social interactions. Recently, researchers have attempted to utilize the online user data to measure users’ SM value. However, in earlier researches, self-monitoring individuals’ specific behavioral and psychological characteristics haven’ t been sufficiently considered in the process of features extraction. In this paper, motivated by psychologist Snyder’s SM psychological theories, we propose to extract the behavior character of self-monitoring individuals in social network at the macro-level to measure SM. Besides, some other SM relevant features, situational factors, implicit topic words in status updates and demographics are also extracted. Furthermore, a new SM measuring method is presented by exploiting various kinds of users’ online data. The experimental results on a benchmark dataset show that all these features are effective and our SM measuring method can outperform many baseline methods.

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