Inferring mechanisms of response prioritization on social media under information overload

Abstract

Human decision-making is subject to the biological limits of cognition. The fluidity of information propagation over online social media often leads users to experience information overload. This in turn affects which information received by users are processed and gain a response to, imposing constraints on volumes of, and participation in, information cascades. In this study, we investigate properties contributing to the visibility of online social media notifications by highly active users experiencing information overload via cross-platform social influence. We analyze simulations of a coupled agent-based model of information overload and the multi-action cascade model of conversation with evolutionary model discovery. Evolutionary model discovery automates mechanistic inference on agent-based models by enabling random forest importance analysis on genetically programmed agent-based model rules. The mechanisms of information overload have shown to contribute to a multitude of global properties of online information cascades. We investigate nine characteristics of online messages that may contribute to the prioritization of messages for response. Our results indicate that recency had the largest contribution to message visibility, with individuals prioritizing more recent notifications. Global popularity of the conversation originator had the second highest contribution, and reduced message visibility. Messages that presented opportunity for novel user interaction, yet high reciprocity showed to have relatively moderate contribution to message visibility. Finally, insights from the evolutionary model discovery results helped inform response prioritization rules, which improved the robustness and accuracy of the model of information overload.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jan 14, 2021
Source ID
10.1038/s41598-020-79897-5

Entities

People

  • Chathika Gunaratne
  • Ivan I Garibay
  • William Rand

Organizations

  • United States Department of Defense

Tags

Fields of Study

  • Computer science

Readers

  • Computer Networking
  • Neural Network Machine Learning.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Biotechnology