The Effects of Information Overload on Online Conversation Dynamics

TL;DR

This paper presents an agent-based model to study how information overload impacts online conversation dynamics, revealing thresholds and behaviors that influence user responsiveness and information dissemination.

Contribution

It introduces a mechanistic, agent-based model of information overload and calibrates it with Twitter data to explain observed online conversation phenomena.

Findings

Responsiveness drops under high overload thresholds and mid-range information loss rates.

Users typically handle about 7 notifications per hour.

Overexposure to information can reduce response likelihood, contrary to viral spread analogies.

Abstract

The inhibiting effects of information overload on the behavior of online social media users, can affect the population-level characteristics of information dissemination through online conversations. We introduce a mechanistic, agent-based model of information overload and investigate the effects of information overload threshold and rate of information loss on observed online phenomena. We find that conversation volume and participation are lowest under high information overload thresholds and mid-range rates of information loss. Calibrating the model to user responsiveness data on Twitter, we replicate and explain several observed phenomena: 1) Responsiveness is sensitive to information overload threshold at high rates of information loss; 2) Information overload threshold and rate of information loss are Pareto-optimal and users may experience overload at inflows exceeding 30 notifications per hour; 3) Local abundance of small cascades of modest global popularity and local scarcity of larger cascades of high global popularity explains why overloaded users receive, but do not respond to large, highly popular cascades; 4) Users typically work with 7 notifications per hour; 5) Over-exposure to information can suppress the likelihood of response by overloading users, contrary to analogies to biologically-inspired viral spread. Reconceptualizing information spread with the mechanisms of information overload creates a richer representation of online conversation dynamics, enabling a deeper understanding of how (dis)information is transmitted over social media.