A Computational Analysis of Syntactic Simplification in Gen Z Digital Communication Using NLP Tools

Abstract

In this paper, syntactic simplification patterns in Generation Z digital communication are analyzed based on the computational linguistic analysis of the data with Natural Language Processing (NLP) tools. With the trend of online communication platforms influencing the daily use of language, young users tend to use the simplified syntactic forms to ensure faster and more efficient communication. The data set used in this research is a set of 160 comments obtained in one of the discussion threads in the r/LawSchool sub-community on the Reddit platform, which is an informal online communication mainly between students of universities and young adults. The corpus that was gathered consists of 10,476 words and was processed in Python with NLP libraries, such as NLTK, spaCy, Stanza, and TextBlob. These linguistic aspects analyzed were the sentence length, the part-of-speech allocation and frequency of words in order to determine the patterns of simplified syntactic constructions. The findings indicate that Gen Z digital communication often depends on shorter sentence constructions, extensive use of pronouns and verbs, and frequent use of function words that help to maintain grammatical coherence and limit the use of complex lexical meanings. These tendencies are then explained within the framework of the Cognitive Load Theory that explains that the restrictions of working memory prompt speakers and writers to simplify language in communication situations where the speed is important. The results indicate that syntactic simplification in online communication is not linguistic loss but a survival technique due to cognitive and communicative resource limitations.
Keywords: Gen Z communication, syntactic simplification, computational linguistics, NLP analysis, cognitive load theory