🤖 AI Summary
Current research on online behavioral change suffers from narrow behavioral coverage, overreliance on API-restricted platforms as data sources, and a persistent theory–empiricism gap. To address these limitations, this study conducts a systematic literature review of 148 peer-reviewed articles published between 2000 and 2023, constructing a four-dimensional knowledge graph encompassing behavioral categories, detection methodologies, platform ecosystems, and theoretical foundations. Our analysis uncovers three salient trends: (1) affective orientation dominates behavioral modeling; (2) platform distribution is heavily skewed toward a few API-constrained platforms; and (3) theoretical integration remains markedly underdeveloped. We propose a novel methodology framework—“Multi-behavioral Modeling, Heterogeneous Data Integration, and Theory–Practice Alignment”—and deliver a structured research map that precisely identifies critical gaps. This work advances the computational behavioral paradigm and offers an actionable methodological guide for digital social governance.
📝 Abstract
Exploring behavioral change in the digital age is imperative for societal progress in the context of 21st-century challenges. We analyzed 148 articles (2000-2023) and built a map that categorizes behaviors and change detection methodologies, platforms of reference, and theoretical frameworks that characterize online behavior change. Our findings uncover a focus on sentiment shifts, an emphasis on API-restricted platforms, and limited theory integration. We call for methodologies able to capture a wider range of behavioral types, diverse data sources, and stronger theory-practice alignment in the study of online behavioral change.
