Work

Understanding Urban Behavior: Information Theory Insights from WhatsApp Traffic Analysis

Year: 2025

Type: conference-paper

Abstract: Instant messaging represents the most popular digital service worldwide, and WhatsApp is the most used app of this kind. By studying WhatsApp traffic, we can gain major insight into both human behavior and network infrastructure needs. This work presents a unique analysis of mobile networks using WhatsApp uplink traffic. We apply information theory metrics such as Shannon Permutation Entropy, Statistical Complexity, and the Causality Complexity-Entropy Plane to understand specific network patterns, usage behaviors, and areas where users are more likely to engage in online conversations. We also demonstrate how these metrics can be used as features for machine learning techniques such as the K-means algorithm, showing they can be used to identify regions with similar patterns in the WhatsApp network traffic.

Source: International Conference on Communications

DOI: 10.1109/icc52391.2025.11161829

Authors: Geymerson S. Ramos, Razvan Stanica, Osvaldo A. Rosso, Andre L. L. Aquino

Institutions: Institut national de recherche en sciences et technologies du numérique, Institut National des Sciences Appliquées de Lyon, Universidade Federal de Alagoas

Language: English

Topic: Human Mobility and Location-Based Analysis

Subfield: Transportation

Field: Social Sciences

Domain: Social Sciences

Open Access status: green

Funder: Agence Nationale de la Recherche

Award: Traffic Collection, Contextual Analysis, Data-driven Optimization for 5G