Measuring Online Behavior Change with Observational Studies: a Review
October 30, 2023 ยท The Cartographer ยท ๐ ACM Computing Surveys
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"Title-pattern auto-detect: Measuring Online Behavior Change with Observational Studies: a Review"
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Authors
Arianna Pera, Gianmarco de Francisci Morales, Luca Maria Aiello
arXiv ID
2310.19951
Category
cs.CY: Computers & Society
Cross-listed
cs.SI,
physics.soc-ph
Citations
0
Venue
ACM Computing Surveys
Last Checked
4 days ago
Abstract
Exploring online behavior change is imperative for societal progress in the context of 21st-century challenges. We analyze 148 articles (2000-2023) focusing on behavior change in the digital space and build a map that categorizes behaviors, behavior change detection methodologies, platforms of reference, and theoretical frameworks that characterize the analysis of online behavior change. Our findings reveal a focus on sentiment shifts, an emphasis on API-restricted platforms, and limited integration of theory. We call for methodologies able to capture a wider range of behavior types, diverse data sources, and stronger theory-practice alignment in the study of online behavior and its change.
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