Reconstructing signed relations from interaction data
This addresses the challenge of rare signed relation data for researchers studying human behavior and social networks, though it appears incremental as it applies a statistical network approach to a new data type.
The paper tackled the problem of detecting signed (positive/negative) relations from abundant interaction data like proximity or communication, which previously could not be used for this purpose, and showed that the inferred relations correspond to survey-reported ones in four communities.
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.