Unveiling Real-World Company Hidden Communication Patterns: Comparing Informal Large-Scale Email Network and Formal Structure

Leonardo Di Perna, Matteo Bianchi, Matteo Matteucci, Marco Brambilla

Abstract: The increasing adoption of digital communication technologies has led to the accumulation of vast amounts of unstructured data, providing new opportunities to analyze organizational dynamics. In this study, we present a comprehensive framework for capturing, processing, and analyzing large-scale digital communication data to uncover hidden informal communication patterns within a real-world corporate environment. Utilizing a unique dataset of 40.425.247 emails within a banking company, spanning a two-year period, we construct a social network highlighting the email interactions. The graph is compared with organizational data, including business unit affiliations, enabling a detailed analysis of the relationship between formal organizational structures and informal social networks. By applying state-of-the-art community detection algorithms, we extract informal communities and compare them to the formal structure using various external validation metrics. Our results reveal that while informal communities may overlap with the formal structure to some extent, they often diverge in ways that may reveal organizational dynamics, communication flow, and inter-departmental collaboration that are not apparent from the formal structure alone. This study aims to pave the way for future research by exploring how human resource management can leverage insights derived from digital communication interactions to foster positive organizational behavior and enhance management practices.

External IDs:dblp:conf/bigdataconf/PernaBM024