A Statistical Framework for Modeling Behavioral Engagement via Topic and Psycholinguistic Features: Evidence from High-Dimensional Text Data

Dan Li () and Yi Zhang
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Dan Li: College of Economics and Management, Taiyuan University of Technology, Taiyuan 030024, China
Yi Zhang: College of Economics and Management, Taiyuan University of Technology, Taiyuan 030024, China

Mathematics, 2025, vol. 13, issue 15, 1-34

Abstract: This study investigates how topic-specific expression by women delivery riders on digital platforms predicts their community engagement, emphasizing the mediating role of self-disclosure and the moderating influence of cognitive and emotional language features. Using unsupervised topic modeling (Top2Vec, Topical Vectors via Embeddings and Clustering) and psycholinguistic analysis (LIWC, Linguistic Inquiry and Word Count), the paper extracted eleven thematic clusters and quantified self-disclosure intensity, cognitive complexity, and emotional polarity. A moderated mediation model was constructed to estimate the indirect and conditional effects of topic probability on engagement behaviors (likes, comments, and views) via self-disclosure. The results reveal that self-disclosure significantly mediates the influence of topical content on engagement, with emotional negativity amplifying and cognitive complexity selectively enhancing this pathway. Indirect effects differ across topics, highlighting the heterogeneous behavioral salience of expressive themes. The findings support a statistically grounded, semantically interpretable framework for predicting user behavior in high-dimensional text environments. This approach offers practical implications for optimizing algorithmic content ranking and fostering equitable visibility for marginalized digital labor groups.

Keywords: self-disclosure; topic modeling; moderated mediation; cognitive complexity; emotional polarity; women gig workers; community engagement; psycholinguistics; text-based prediction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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