# Detecting Effects of AI-Mediated Communication on Language Complexity and Sentiment

arXiv: https://arxiv.org/abs/2504.19556 · HTML: https://mlsanity.com/paper/2504.19556/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 14 references that are in the map.
- First posted on arXiv 28 Apr 2025.

## Builds on (most cited first, 2 of 2)

- [Artificial intelligence in communication impacts language and social relationships](https://mlsanity.com/paper/2102.05756/) (2021)
- [Exploring the Effects of AI-assisted Emotional Support Processes in Online Mental Health Community](https://mlsanity.com/paper/2202.10065/) (2022)

## Abstract (arXiv)

Given the subtle human-like effects of large language models on linguistic patterns, this study examines shifts in language over time to detect the impact of AI-mediated communication (AI- MC) on social media. We compare a replicated dataset of 970,919 tweets from 2020 (pre-ChatGPT) with 20,000 tweets from the same period in 2024, all of which mention Donald Trump during election periods. Using a combination of Flesch-Kincaid readability and polarity scores, we analyze changes in text complexity and sentiment. Our findings reveal a significant increase in mean sentiment polarity (0.12 vs. 0.04) and a shift from predominantly neutral content (54.8% in 2020 to 39.8% in 2024) to more positive expressions (28.6% to 45.9%). These findings suggest not only an increasing presence of AI in social media communication but also its impact on language and emotional expression patterns.