Well-established cognitive models coming from anthropology have shown that,
due to the cognitive constraints that limit our "bandwidth" for social
interactions, humans organize their social relations according to a regular
structure. In this work, we postulate that similar regularities can be found in
other cognitive processes, such as those involving language production. In
order to investigate this claim, we analyse a dataset containing tweets of a
heterogeneous group of Twitter users (regular users and professional writers).
Leveraging a methodology similar to the one used to uncover the
well-established social cognitive constraints, we find regularities at both the
structural and semantic level. At the former, we find that a concentric layered
structure (which we call ego network of words, in analogy to the ego network of
social relationships) very well captures how individuals organise the words
they use. The size of the layers in this structure regularly grows
(approximately 2-3 times with respect to the previous one) when moving
outwards, and the two penultimate external layers consistently account for
approximately 60% and 30% of the used words, irrespective of the number of the
total number of layers of the user. For the semantic analysis, each ring of
each ego network is described by a semantic profile, which captures the topics
associated with the words in the ring. We find that ring #1 has a special role
in the model. It is semantically the most dissimilar and the most diverse among
the rings. We also show that the topics that are important in the innermost
ring also have the characteristic of being predominant in each of the other
rings, as well as in the entire ego network. In this respect, ring #1 can be
seen as the semantic fingerprint of the ego network of words.

本研究探讨了类似于社交关系中的认知约束在语言产出中是否存在类似的规律，并通过使用特定的方法在 Twitter 数据集中发现了语言使用的结构和语义层次的规律性。结构层次中，研究员通过寻找一种叫做单词的自我网络的同心圆层次结构来捕捉单词使用的规律。语义层次中，研究员通过每个同心圆的语义资料库来描述自我网络，发现第一个同心圆扮演着特殊的角色，也是自我网络的 “语义指纹”。