Characterizing Human Actions in the Digital Platform by Temporal Context
This work addresses the need for more comprehensive modeling of human actions in digital platforms for social scientists, though it appears incremental as it builds on existing sequence-based approaches by adding temporal context.
The paper tackled the problem of modeling human behavior on digital platforms by addressing the abstraction of inter-temporal information in existing sequence-based models, introducing the Action-Timing Context (ATC) framework to jointly embed actions and time intervals, and demonstrated its application on real-world datasets to provide a unified and interpretable view of human activity.
Recent advances in digital platforms generate rich, high-dimensional logs of human behavior, and machine learning models have helped social scientists explain knowledge accumulation, communication, and information diffusion. Such models, however, almost always treat behavior as sequences of actions, abstracting the inter-temporal information among actions. To close this gap, we introduce a two-scale Action-Timing Context(ATC) framework that jointly embeds each action and its time interval. ATC obtains low-dimensional representations of actions and characterizes them with inter-temporal information. We provide three applications of ATC to real-world datasets and demonstrate that the method offers a unified view of human behavior. The presented qualitative findings demonstrate that explicitly modeling inter-temporal context is essential for a comprehensive, interpretable understanding of human activity on digital platforms.