Characterizing Human Actions in the Digital Platform by Temporal Context

TL;DR

This paper introduces a novel two-scale framework called ATC that jointly embeds human actions and their time intervals to better understand behavior on digital platforms, emphasizing the importance of inter-temporal context.

Contribution

The paper presents the ATC framework, a new method that captures inter-temporal information in human actions, providing a unified and interpretable view of digital platform behavior.

Findings

ATC effectively models inter-temporal context in human actions.

The method offers a comprehensive understanding of digital platform behavior.

Qualitative analysis shows the importance of temporal context for interpretability.

Abstract

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.