Detecting Effects of AI-Mediated Communication on Language Complexity and Sentiment
It addresses the problem of understanding AI's influence on social media language patterns for researchers and policymakers, but is incremental as it applies existing methods to new data.
This study examined the impact of AI-mediated communication on language complexity and sentiment in tweets about Donald Trump, finding a significant increase in mean sentiment polarity (0.12 vs. 0.04) and a shift from neutral to more positive expressions from 2020 to 2024.
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.