分析社交媒体文本，发现AI使语言更积极且复杂度上升。
Detecting Effects of AI-Mediated Communication on Language Complexity and Sentiment
- 对比2020与2024年特朗普相关推文，用可读性与情感分数检测变化
- 情感极性均值从0.04升至0.12，中性内容占比由54.8%降至39.8%
- 适合关注AI对社会语言影响的研究者和从业者参考
鉴于大语言模型对语言模式产生的微妙类人效应，本研究通过分析时间序列中的语言变化，检测人工智能中介通信（AI-MC）在社交媒体上的影响。我们对比了2020年（ChatGPT前）的970,919条推文与2024年同期相同主题下20,000条推文，全部涉及美国总统选举期间的唐纳德·特朗普。采用Flesch-Kincaid可读性评分与情感极性分数，分析文本复杂度与情绪倾向的变化。结果显示，平均情感极性显著提升（0.12 vs. 0.04），中性内容比例从2020年的54.8%下降至2024年的39.8%，而正面表达比例则从28.6%上升至45.9%。这些发现表明，不仅人工智能在社交媒体沟通中日益普及，也正在重塑语言表达与情绪模式。
原文摘要 · Abstract (English)
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.
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