Large Language Models (LLMs) demonstrate remarkable capabilities in text generation, yet their emotional consistency and semantic coherence in social media contexts remain insufficiently understood. This study investigates how LLMs handle emotional content and maintain semantic relationships through continuation and response tasks using three open-source models: Gemma, Llama3 and Llama3.3 and one commercial Model:Claude. By analyzing climate change discussions from Twitter and Reddit, we examine emotional transitions, intensity patterns, and semantic consistency between human-authored and LLM-generated content. Our findings reveal that while both models maintain high semantic coherence, they exhibit distinct emotional patterns: these models show a strong tendency to moderate negative emotions. When the input text carries negative emotions such as anger, disgust, fear, or sadness, LLM tends to generate content with more neutral emotions, or even convert them into positive emotions such as joy or surprise. At the same time, we compared the LLM-generated content with human-authored content. The four models systematically generated responses with reduced emotional intensity and showed a preference for neutral rational emotions in the response task. In addition, these models all maintained a high semantic similarity with the original text, although their performance in the continuation task and the response task was different. These findings provide deep insights into the emotion and semantic processing capabilities of LLM, which are of great significance for its deployment in social media environments and human-computer interaction design.

翻译：大型语言模型（LLMs）在文本生成方面展现出卓越能力，但其在社交媒体语境中的情感一致性与语义连贯性仍未得到充分理解。本研究通过使用三种开源模型（Gemma、Llama3和Llama3.3）和一种商业模型（Claude），基于续写与回复任务，探究LLMs如何处理情感内容并保持语义关联。通过分析来自Twitter和Reddit的气候变化讨论，我们考察了情感转换、强度模式以及人类撰写内容与LLM生成内容之间的语义一致性。研究发现，尽管所有模型均保持较高的语义连贯性，但它们表现出明显的情感模式：这些模型显示出强烈的中和负面情感的倾向。当输入文本带有愤怒、厌恶、恐惧或悲伤等负面情绪时，LLM倾向于生成情感更中立的内容，甚至将其转化为喜悦或惊讶等正面情绪。同时，我们将LLM生成内容与人类撰写内容进行比较。四种模型在回复任务中系统性地生成了情感强度降低的回应，并表现出对中性理性情感的偏好。此外，这些模型均与原始文本保持了较高的语义相似度，尽管它们在续写任务和回复任务中的表现存在差异。这些发现为理解LLM的情感与语义处理能力提供了深刻见解，对其在社交媒体环境中的部署及人机交互设计具有重要意义。