  The advent of online social networks has led to the development of an
abundant literature on the study of online social groups and their relationship
to individuals' personalities as revealed by their textual productions. Social
structures are inferred from a wide range of social interactions. Those
interactions form complex -- sometimes multi-layered -- networks, on which
community detection algorithms are applied to extract higher order structures.
The choice of the community detection algorithm is however hardily questioned
in relation with the cultural production of the individual they classify. In
this work, we assume the entangled nature of social networks and their cultural
production to propose a definition of cultural based online social groups as
sets of individuals whose online production can be categorized as social
group-related. We take advantage of this apparently self-referential
description of online social groups with a hybrid methodology that combines a
community detection algorithm and a natural language processing classification
algorithm. A key result of this analysis is the possibility to score community
detection algorithms using their agreement with the natural language processing
classification. A second result is that we can assign the opinion of a random
user at >85% accuracy.
