Consistency of Responses and Continuations Generated by Large Language Models on Social Media

• URL: http://arxiv.org/abs/2501.08102v6
• Date: Tue, 04 Nov 2025 02:32:20 GMT
• Title: Consistency of Responses and Continuations Generated by Large Language Models on Social Media
• Authors: Wentao Xu, Wenlu Fan, Yuqi Zhu, Bin Wang,
• Abstract summary: Large Language Models (LLMs) demonstrate remarkable capabilities in text generation, yet their emotional consistency and semantic coherence in social media contexts remain insufficiently understood.<n>This study investigates how LLMs handle emotional content and maintain semantic relationships through continuation and response tasks.
• Score: 11.076748897687764
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• EMO-R3: Reflective Reinforcement Learning for Emotional Reasoning in Multimodal Large Language Models [62.3977734456669]
  We propose Reflective Reinforcement Learning for Emotional Reasoning (EMO-R3), a framework designed to enhance the emotional reasoning ability of Multimodal Large Language Models (MLLMs)<n>We introduce Structured Emotional Thinking to guide the model to perform step-by-step emotional reasoning in a structured and interpretable manner, and design a Reflective Emotional Reward that enables the model to re-evaluate its reasoning based on visual-text consistency and emotional coherence.<n>EMO-R3 significantly improves both the interpretability and emotional intelligence of MLLMs, achieving superior performance across multiple visual emotional understanding benchmarks.
  arXiv  Detail & Related papers  (2026-02-27T08:42:52Z)
• A Unified Spoken Language Model with Injected Emotional-Attribution Thinking for Human-like Interaction [50.05919688888947]
  This paper presents a unified spoken language model for emotional intelligence, enhanced by a novel data construction strategy termed Injected Emotional-Attribution Thinking (IEAT)<n>IEAT incorporates user emotional states and their underlying causes into the model's internal reasoning process, enabling emotion-aware reasoning to be internalized rather than treated as explicit supervision.<n> Experiments on the Human-like Spoken Dialogue Systems Challenge (HumDial) Emotional Intelligence benchmark demonstrate that the proposed approach achieves top-ranked performance across emotional trajectory modeling, emotional reasoning, and empathetic response generation.
  arXiv  Detail & Related papers  (2026-01-08T14:07:30Z)
• Emotion-Coherent Reasoning for Multimodal LLMs via Emotional Rationale Verifier [53.55996102181836]
  We propose the Emotional Rationale Verifier (ERV) and an Explanation Reward.<n>Our method guides the model to produce reasoning that is explicitly consistent with the target emotion.<n>We show that our approach not only enhances alignment between explanation and prediction but also empowers MLLMs to deliver emotionally coherent, trustworthy interactions.
  arXiv  Detail & Related papers  (2025-10-27T16:40:17Z)
• Fluent but Unfeeling: The Emotional Blind Spots of Language Models [1.248728117157669]
  A critical gap remains in evaluating whether Large Language Models (LLMs) align with human emotions at a fine-grained level.<n>We introduce Express, a benchmark dataset curated from Reddit communities featuring 251 fine-grained, self-disclosed emotion labels.<n>Our comprehensive evaluation framework examines predicted emotion terms and decomposes them into eight basic emotions using established emotion theories.
  arXiv  Detail & Related papers  (2025-09-11T16:31:13Z)
• UniEmo: Unifying Emotional Understanding and Generation with Learnable Expert Queries [61.5273479616832]
  We propose a unified framework that seamlessly integrates emotional understanding and generation.<n>We show that UniEmo significantly outperforms state-of-the-art methods in both emotional understanding and generation tasks.
  arXiv  Detail & Related papers  (2025-07-31T09:39:27Z)
• Disentangle Identity, Cooperate Emotion: Correlation-Aware Emotional Talking Portrait Generation [63.94836524433559]
  DICE-Talk is a framework for disentangling identity with emotion and cooperating emotions with similar characteristics. We develop a disentangled emotion embedder that jointly models audio-visual emotional cues through cross-modal attention. Second, we introduce a correlation-enhanced emotion conditioning module with learnable Emotion Banks. Third, we design an emotion discrimination objective that enforces affective consistency during the diffusion process.
  arXiv  Detail & Related papers  (2025-04-25T05:28:21Z)
• AI with Emotions: Exploring Emotional Expressions in Large Language Models [0.0]
  Large Language Models (LLMs) play role-play as agents answering questions with specified emotional states.<n>Russell's Circumplex model characterizes emotions along the sleepy-activated (arousal) and pleasure-displeasure (valence) axes.<n> evaluation showed that the emotional states of the generated answers were consistent with the specifications.
  arXiv  Detail & Related papers  (2025-04-20T18:49:25Z)
• GatedxLSTM: A Multimodal Affective Computing Approach for Emotion Recognition in Conversations [35.63053777817013]
  GatedxLSTM is a novel multimodal Emotion Recognition in Conversation (ERC) model. It considers voice and transcripts of both the speaker and their conversational partner to identify the most influential sentences driving emotional shifts. It achieves state-of-the-art (SOTA) performance among open-source methods in four-class emotion classification.
  arXiv  Detail & Related papers  (2025-03-26T18:46:18Z)
• Mechanistic Interpretability of Emotion Inference in Large Language Models [16.42503362001602]
  We show that emotion representations are functionally localized to specific regions in large language models. We draw on cognitive appraisal theory to show that emotions emerge from evaluations of environmental stimuli. This work highlights a novel way to causally intervene and precisely shape emotional text generation.
  arXiv  Detail & Related papers  (2025-02-08T08:11:37Z)
• Enriching Multimodal Sentiment Analysis through Textual Emotional Descriptions of Visual-Audio Content [56.62027582702816]
  Multimodal Sentiment Analysis seeks to unravel human emotions by amalgamating text, audio, and visual data.<n>Yet, discerning subtle emotional nuances within audio and video expressions poses a formidable challenge.<n>We introduce DEVA, a progressive fusion framework founded on textual sentiment descriptions.
  arXiv  Detail & Related papers  (2024-12-12T11:30:41Z)
• When Words Smile: Generating Diverse Emotional Facial Expressions from Text [72.19705878257204]
  We introduce an end-to-end text-to-expression model that explicitly focuses on emotional dynamics.<n>Our model learns expressive facial variations in a continuous latent space and generates expressions that are diverse, fluid, and emotionally coherent.
  arXiv  Detail & Related papers  (2024-12-03T15:39:05Z)
• Recognizing Emotion Regulation Strategies from Human Behavior with Large Language Models [44.015651538470856]
  Human emotions are often not expressed directly, but regulated according to internal processes and social display rules. No method to automatically classify different emotion regulation strategies in a cross-user scenario exists. We make use of the recently introduced textscDeep corpus for modeling the social display of the emotion shame. A fine-tuned Llama2-7B model is able to classify the utilized emotion regulation strategy with high accuracy.
  arXiv  Detail & Related papers  (2024-08-08T12:47:10Z)
• Exploiting Emotion-Semantic Correlations for Empathetic Response Generation [18.284296904390143]
  Empathetic response generation aims to generate empathetic responses by understanding the speaker's emotional feelings from the language of dialogue. Recent methods capture emotional words in the language of communicators and construct them as static vectors to perceive nuanced emotions. We propose a dynamical Emotion-Semantic Correlation Model (ESCM) for empathetic dialogue generation tasks.
  arXiv  Detail & Related papers  (2024-02-27T11:50:05Z)
• Enhancing Emotional Generation Capability of Large Language Models via Emotional Chain-of-Thought [50.13429055093534]
  Large Language Models (LLMs) have shown remarkable performance in various emotion recognition tasks. We propose the Emotional Chain-of-Thought (ECoT) to enhance the performance of LLMs on various emotional generation tasks.
  arXiv  Detail & Related papers  (2024-01-12T16:42:10Z)
• Emotion Rendering for Conversational Speech Synthesis with Heterogeneous Graph-Based Context Modeling [50.99252242917458]
  Conversational Speech Synthesis (CSS) aims to accurately express an utterance with the appropriate prosody and emotional inflection within a conversational setting. To address the issue of data scarcity, we meticulously create emotional labels in terms of category and intensity. Our model outperforms the baseline models in understanding and rendering emotions.
  arXiv  Detail & Related papers  (2023-12-19T08:47:50Z)
• Dynamic Causal Disentanglement Model for Dialogue Emotion Detection [77.96255121683011]
  We propose a Dynamic Causal Disentanglement Model based on hidden variable separation. This model effectively decomposes the content of dialogues and investigates the temporal accumulation of emotions. Specifically, we propose a dynamic temporal disentanglement model to infer the propagation of utterances and hidden variables.
  arXiv  Detail & Related papers  (2023-09-13T12:58:09Z)
• Emotion-aware Chat Machine: Automatic Emotional Response Generation for Human-like Emotional Interaction [55.47134146639492]
  This article proposes a unifed end-to-end neural architecture, which is capable of simultaneously encoding the semantics and the emotions in a post. Experiments on real-world data demonstrate that the proposed method outperforms the state-of-the-art methods in terms of both content coherence and emotion appropriateness.
  arXiv  Detail & Related papers  (2021-06-06T06:26:15Z)
• Detecting Emotion Primitives from Speech and their use in discerning Categorical Emotions [16.886826928295203]
  Emotion plays an essential role in human-to-human communication, enabling us to convey feelings such as happiness, frustration, and sincerity. This work investigated how emotion primitives can be used to detect categorical emotions such as happiness, disgust, contempt, anger, and surprise from neutral speech. Results indicated that arousal, followed by dominance was a better detector of such emotions.
  arXiv  Detail & Related papers  (2020-01-31T03:11:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.