Socio-semantic networks as mutualistic networks

Several studies have shown that discourse and social relationships are intertwined and co-evolve. However, we lack theoretical models to explain the phenomenon. Inspired by recent work in ecology, we propose to model socio-semantic networks as an interaction between two intermingled data generating processes: a social community process and a document-based process. We consider the link between semantic and social ties as analogous to the interactions found in pollination networks whereby agents visit hidden topics in a similar way that insects visit specific plants for pollination. We use the ENRON socio-semantic email network to investigate if it exhibits properties that characterize mutualistic networks, namely moderate connectance, heterogeneous degree distribution, moderate modularity and high nestedness. To do so, we build a plant-pollinator matrix where “insect species” are communities detected via block modelling, “plant species” are latent topics detected with topic modelling, and the interaction between the two is the total number of visits a community makes to specific topics. Our results show that the ENRON socio-semantic interaction matrix respects the aforementioned criteria of mutualism paving the way for the development of a relevant framework to better understand the dynamic of human socio-semantic interactions.