Reconstructing signed relations from interaction data

TL;DR

This paper demonstrates how interaction data can be used to reconstruct signed social relations, aligning with survey data and enabling analysis of community homophily and group cohesion.

Contribution

It introduces a statistical network method to infer signed relations from interaction data, a novel approach compared to traditional survey-based data collection.

Findings

Reconstructed signed relations match survey-reported relations.

Revealed homophily patterns based on gender, religion, and finances.

Analyzed triads to understand group cohesion.

Abstract

Positive and negative relations play an essential role in human behavior and shape the communities we live in. Despite their importance, data about signed relations is rare and commonly gathered through surveys. Interaction data is more abundant, for instance, in the form of proximity or communication data. So far, though, it could not be utilized to detect signed relations. In this paper, we show how the underlying signed relations can be extracted with such data. Employing a statistical network approach, we construct networks of signed relations in four communities. We then show that these relations correspond to the ones reported in surveys. Additionally, the inferred relations allow us to study the homophily of individuals with respect to gender, religious beliefs, and financial backgrounds. We evaluate the importance of triads in the signed network to study group cohesion.