Detecting Effects of AI-Mediated Communication on Language Complexity and Sentiment
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908340181794816 |
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| author | Sussman, Kristen Carter, Daniel |
| author_facet | Sussman, Kristen Carter, Daniel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_19556 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Detecting Effects of AI-Mediated Communication on Language Complexity and Sentiment Sussman, Kristen Carter, Daniel Computation and Language Human-Computer Interaction J.4; K.4.0; I.2.7 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. |
| title | Detecting Effects of AI-Mediated Communication on Language Complexity and Sentiment |
| topic | Computation and Language Human-Computer Interaction J.4; K.4.0; I.2.7 |
| url | https://arxiv.org/abs/2504.19556 |