Corporate language in social media: A corpus-based linguistic case study of target group-oriented corporate communication on Instagram and LinkedIn

Abstract

This thesis investigates how corporate language can be used to address different target groups in social media communication. Using the engineering company FC-Gruppe as a case study, it investigates how linguistic strategies on Instagram and LinkedIn can be tailored to audiences such as employees, potential trainees and students, experienced professionals, and business partners. A corpus of company posts was compiled and analyzed according to selected linguistic criteria, including pronoun use, sentence structure, jargon, emotionality, gender-inclusive language, and modality. To support the analysis, a Python-based prototype employing the NLP library spaCy was developed for automated text evaluation. The findings show that each target group requires distinct linguistic features and communicative approaches, and that combining several groups within one post is only effective in limited cases. The study contributes a practical framework for aligning corporate language with target group needs and demonstrates how corpus linguistics and NLP can be applied to optimize social media communication.