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.

在线社交网络的出现导致了关于在线社交群体及其与个体个性关系的丰富文献的发展。通过对广泛的社交互动进行推断，可以得到社交结构，这些互动形成了复杂的、有时是多层次的网络，对其中的高阶结构进行了社群检测算法的提取。然而，关于社群检测算法在与其分类的个体文化生产之间的关系，却很少受到质疑。在这项工作中，我们假设社交网络及其文化生产是纠缠在一起的，并提出了一种基于文化的在线社交群体的定义，即将在线产出归类为社群相关的个体集合。我们利用这种似乎自指的在线社交群体描述，结合社区检测算法和自然语言处理分类算法的混合方法。该分析的一个关键结果是可以使用自然语言处理分类算法的准确度来评价社群检测算法的得分。另一个结果是我们可以以超过 85% 的准确率分配随机用户的意见。