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

aimingoo的专栏
aimingoo的专栏
I
InfoQ
B
Blog RSS Feed
D
Docker
GbyAI
GbyAI
N
Netflix TechBlog - Medium
Y
Y Combinator Blog
F
Fortinet All Blogs
P
Proofpoint News Feed
Microsoft Azure Blog
Microsoft Azure Blog
人人都是产品经理
人人都是产品经理
Martin Fowler
Martin Fowler
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
M
MIT News - Artificial intelligence
C
Check Point Blog
Vercel News
Vercel News
云风的 BLOG
云风的 BLOG
博客园 - Franky
Google DeepMind News
Google DeepMind News
WordPress大学
WordPress大学
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
V
V2EX
Last Week in AI
Last Week in AI
L
LangChain Blog

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
Tu crois que c'est vrai ? Diversite des regimes d'enoncia...
Manon Berriche · 2025-11-23 · via cs.CL updates on arXiv.org

This thesis addresses two paradoxes: (1) why empirical studies find that fake news represent only a small share of the information consulted and shared on social media despite the absence of editorial control or journalistic norms, and (2) how political polarization has intensified even though users do not appear especially receptive to fake news. To investigate these issues, two complementary studies were carried out on Twitter and Facebook, combining quantitative analyses of digital traces with online observation and interviews. This mixed-methods design avoids reducing users to single reactions to identified fake items and instead examines the variety of practices across different interactional situations, online and offline, while recording socio-demographic traits. The first study mapped users who shared at least one item labeled fake by fact-checkers in the French Twittersphere. The second used a corpus of items flagged by Facebook users to study reactions to statements whose epistemic status is uncertain. Three main findings emerge. First, sharing fake news is concentrated among a limited group of users who are not less educated or cognitively disadvantaged but are more politicized and critical of institutions; owing to their high activity and prolific sharing, they can help set the agenda for their political camp. Second, exposed users can deploy varying forms of critical distance depending on their social position and the interactional norms of the situations they inhabit: either discursive caution (prudence énonciative) or interventions ('points d'arrêt') that express disagreement or corrections. Third, these forms of critical distance seldom yield genuine deliberative debates or agonistic pluralism; rather, they often produce dialogues of the deaf among a small, particularly active minority.