惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

T
The Blog of Author Tim Ferriss
罗磊的独立博客
月光博客
月光博客
GbyAI
GbyAI
腾讯CDC
G
Google Developers Blog
H
Hackread – Cybersecurity News, Data Breaches, AI and More
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
U
Unit 42
Engineering at Meta
Engineering at Meta
A
About on SuperTechFans
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
aimingoo的专栏
aimingoo的专栏
C
Check Point Blog
Y
Y Combinator Blog
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Microsoft Azure Blog
Microsoft Azure Blog
I
InfoQ
雷峰网
雷峰网
B
Blog RSS Feed
美团技术团队
M
MIT News - Artificial intelligence
有赞技术团队
有赞技术团队
D
Docker

cs.CL updates on arXiv.org

Playing Along: Learning a Double-Agent Defender for Belief Steering via Theory of Mind RPA-Check: A Multi-Stage Automated Framework for Evaluating Dynamic LLM-based Role-Playing Agents A Triadic Suffix Tokenization Scheme for Numerical Reasoning Hidden Measurement Error in LLM Pipelines Distorts Annotation, Evaluation, and Benchmarking Synthius-Mem: Brain-Inspired Hallucination-Resistant Persona Memory Achieving 94.4% Memory Accuracy and 99.6% Adversarial Robustness on LoCoMo Time is Not a Label: Continuous Phase Rotation for Temporal Knowledge Graphs and Agentic Memory NovBench: Evaluating Large Language Models on Academic Paper Novelty Assessment Policy Split: Incentivizing Dual-Mode Exploration in LLM Reinforcement with Dual-Mode Entropy Regularization METER: Evaluating Multi-Level Contextual Causal Reasoning in Large Language Models Think Before you Write: QA-Guided Reasoning for Character Descriptions in Books METRO: Towards Strategy Induction from Expert Dialogue Transcripts for Non-collaborative Dialogues Retrieval as Generation: A Unified Framework with Self-Triggered Information Planning Do LLMs Know Tool Irrelevance? Demystifying Structural Alignment Bias in Tool Invocations Enhancing Multimodal Large Language Models for Ancient Chinese Character Evolution Analysis via Glyph-Driven Fine-Tuning Polyglot Teachers: Evaluating Language Models for Multilingual Synthetic Data Generation Exploring Knowledge Conflicts for Faithful LLM Reasoning: Benchmark and Method CocoaBench: Evaluating Unified Digital Agents in the Wild MathAgent: Adversarial Evolution of Constraint Graphs for Mathematical Reasoning Data Synthesis Evaluating Memory Capability in Continuous Lifelog Scenario How Robust Are Large Language Models for Clinical Numeracy? An Empirical Study on Numerical Reasoning Abilities in Clinical Contexts Efficient Training for Cross-lingual Speech Language Models Shared Emotion Geometry Across Small Language Models: A Cross-Architecture Study of Representation, Behavior, and Methodological Confounds A Systematic Analysis of the Impact of Persona Steering on LLM Capabilities Uncertainty-Aware Web-Conditioned Scientific Fact-Checking When Valid Signals Fail: Regime Boundaries Between LLM Features and RL Trading Policies When Verification Fails: How Compositionally Infeasible Claims Escape Rejection Mem$^2$Evolve: Towards Self-Evolving Agents via Co-Evolutionary Capability Expansion and Experience Distillation AOP-Smart: A RAG-Enhanced Large Language Model Framework for Adverse Outcome Pathway Analysis OccuBench: Evaluating AI Agents on Real-World Professional Tasks via Language Environment Simulation Advancing Polish Language Modeling through Tokenizer Optimization in the Bielik v3 7B and 11B Series
Embarrassed to observe: The effects of directive language...
Andria Andriuzzi, Géraldine Michel · 2025-08-18 · via cs.CL updates on arXiv.org

In social media, marketers attempt to influence consumers by using directive language, that is, expressions designed to get consumers to take action. While the literature has shown that directive messages in advertising have mixed results for recipients, we know little about the effects of directive brand language on consumers who see brands interacting with other consumers in social media conversations. On the basis of a field study and three online experiments, this study shows that directive language in brand conversation has a detrimental downstream effect on engagement of consumers who observe such exchanges. Specifically, in line with Goffman's facework theory, because a brand that encourages consumers to react could be perceived as face-threatening, consumers who see a brand interacting with others in a directive way may feel vicarious embarrassment and engage less (compared with a conversation without directive language). In addition, we find that when the conversation is nonproduct-centered (vs. product-centered), consumers expect more freedom, as in mundane conversations, even for others; therefore, directive language has a stronger negative effect. However, in this context, the strength of the brand relationship mitigates this effect. Thus, this study contributes to the literature on directive language and brand-consumer interactions by highlighting the importance of context in interactive communication, with direct relevance for social media and brand management.