Measuring Online Behavior Change with Observational Studies: a Review

TL;DR

This paper presents a comprehensive survey of observational studies quantifying online behavior change, introducing a tripartite Environment–Event–Behavior taxonomy and mapping methods, platforms, event types, and theoretical frameworks. It reveals a heavy focus on sentiment and Twitter/Reddit data, with modest theoretical grounding and calls for broader data sources, diversified behavioral targets, and stronger theory integration. The authors also outline methodological trends, such as prevalent before-after and time-series approaches, and advocate for ethical governance and collaboration between computational and social-science perspectives to advance the field.

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.

Figures (8)

• Figure 1: Number of retrieved papers by publishing year.
• Figure 2: Number of works by type of online platform (a) and in-depth details on the specific social media (b) and forum platforms (c) analyzed. Dot connectors characterize the simultaneous presence of multiple characteristics.
• Figure 3: Number of works by type of triggering event (a) and technique used by scholars for event detection (b). Dot connectors characterize the simultaneous presence of multiple characteristics.
• Figure 4: Number of works by behavior type (a), object of the behavior (b), and type of measured used to extract the behavior (c). Dot connectors characterize the simultaneous presence of multiple characteristics.
• Figure 5: Number of works by intersection between behavior type and proxy measure used to operationalize the behavior.