A Simple Generative Model of Collective Online Behaviour

TL;DR

This paper presents a simple generative model for collective online behavior, showing that recent popularity influences user decisions more than cumulative popularity, and demonstrating the effectiveness of temporal data-driven modeling in understanding online dynamics.

Contribution

Introduces a generative model emphasizing recent popularity over cumulative popularity to explain collective online behavior, validated by temporal data analysis.

Findings

Models emphasizing recent popularity match observed dynamics.

Temporal data can distinguish underlying behavioral mechanisms.

Purely observational data can reveal microscopic decision processes.

Abstract

Human activities increasingly take place in online environments, providing novel opportunities for relating individual behaviours to population-level outcomes. In this paper, we introduce a simple generative model for the collective behaviour of millions of social networking site users who are deciding between different software applications. Our model incorporates two distinct components: one is associated with recent decisions of users, and the other reflects the cumulative popularity of each application. Importantly, although various combinations of the two mechanisms yield long-time behaviour that is consistent with data, the only models that reproduce the observed temporal dynamics are those that strongly emphasize the recent popularity of applications over their cumulative popularity. This demonstrates---even when using purely observational data without experimental design---that temporal data-driven modelling can effectively distinguish between competing microscopic mechanisms, allowing us to uncover new aspects of collective online behaviour.