Extracting Active "Ego Networks" of Words: Methodology, Robustness, and Cross-Domain Validation

The "ego network of words" model captures structural properties in language production associated with cognitive constraints. While previous research focused on the layer-based structure and its semantic properties, this paper argues that an essential element, the concept of an active network, is missing. Drawing inspiration from social ego networks, where the active part includes relationships regularly nurtured by individuals, we establish the notion of an active ego network of words. We demonstrate that without the active network concept, an ego network becomes vulnerable to the amount of data considered, leading to the disappearance of the layered structure in larger datasets. To address this, we define a methodology for extracting the active part of the ego network of words and validate it using interview transcripts and tweets. The robustness of our method to varying input data sizes and temporal stability is demonstrated. In addition, our results are well-aligned with prior analyses of the ego network of words, where the limitation of the data collected led automatically (and implicitly) to approximately consider the active part of the network only. Moreover, the validation on the transcripts dataset (MediaSum) highlights the generalizability of the model across diverse domains and the ingrained cognitive constraints in language usage.