Information Degradation and Misinformation in Gossip Networks

2025-01-22

AI Summary

This paper investigates the co-evolution of timeliness and semantic distortion in information propagation over social networks. Focusing on gossip-style diffusion, it proposes a unified model integrating Age of Information (AoI) and semantic degradation: a discrete-time Markov chain captures state deterioration across multi-hop transmissions, embedded within a random graph propagation framework; theoretical analysis is conducted under fully connected and ring topologies. The work establishes, for the first time, a strict linear proportionality between misinformation propagation rate and AoI; proves that gossiping reduces average AoI yet accelerates semantic distortion; and provides a unified analytical framework characterizing the fundamental trade-off between information “freshness” and “accuracy.” These results offer a novel theoretical perspective and quantitative foundation for understanding the rapid spread of misinformation.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Information Theory
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Web Mining and Content Analysis: Content-based information diffusion
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

We study networks of gossiping users where a source observing a process sends updates to an underlying graph. Nodes in the graph update their neighbors randomly and nodes always accept packets that have newer information, thus attempting to minimize their age of information (AoI). We show that while gossiping reduces AoI, information can rapidly degrade in such a network. We model degradation by arbitrary discrete-time Markov chains on k states. As a packet is transmitted through the network it modifies its state according to the Markov chain. In the last section, we specialize the Markov chain to represent misinformation spread, and show that the rate of misinformation spread is proportional to the age of information in both the fully-connected graph and ring graph.

Problem

• Misinformation Spread
• Social Networks
• Information Accuracy

Innovation

• Markov Chain Model
• Information Inaccuracy
• Error Propagation in Social Networks