Linguistic Distancing on Social Media: Indicators of Emotion Regulation Across Age Groups

2026-06-29

AI Summary

This study investigates how linguistic expression reflects age-related differences in emotion regulation, with a focus on psychological distancing as manifested in social media discourse. Leveraging a large-scale corpus of naturalistic social media texts, the research extends and refines computational metrics for quantifying linguistic distancing, offering the first systematic evidence of its association with age in real-world contexts. Integrating natural language processing with computational psychology approaches, the findings demonstrate a significant increase in the use of linguistic distancing markers with advancing age, thereby validating its role as a reliable indicator of adaptive emotion regulation and psychological well-being. These results provide a novel empirical foundation and benchmark for digital mental health interventions targeting emotion regulation across the lifespan.

Abstract

Managing our emotional responses to events is key to emotional well-being, a process referred to as emotion regulation in psychology. Previous work has established that the degree to which we distance events is a type of emotion regulation. When we psychologically distance from events there can be markers in our language. These markers have been referred to as linguistic distancing. We build upon a previous metric to operationalize linguistic distancing, and explore how it changes across the lifespan. We explore this systematically by analyzing large amounts of social media text, a venue where people express their emotions. By investigating how distancing varies across age groups we can better understand how emotion regulation varies with age and provide initial benchmarks on social media data. We provide additional evidence further strengthening the hypothesis that linguistic distancing occurs in proportionally more instances with age. These findings align with past work in psychology which indicate improved well-being with older age. Better understanding how linguistic distancing changes with age is important because it functions as a marker of well-being and can inform effective health interventions. We provide a foundation for further exploring emotion regulation through linguistic distancing in text data.

Problem

• linguistic distancing
• emotion regulation
• age differences
• well-being
• social media

Innovation

• linguistic distancing
• emotion regulation
• social media analysis
• computational linguistics
• age-related differences