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

博客园 - 聂微东
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
月光博客
月光博客
博客园 - 三生石上(FineUI控件)
The Cloudflare Blog
博客园 - Franky
IT之家
IT之家
V
Visual Studio Blog
博客园 - 【当耐特】
阮一峰的网络日志
阮一峰的网络日志
V
V2EX
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
博客园 - 司徒正美
爱范儿
爱范儿
Hugging Face - Blog
Hugging Face - Blog
宝玉的分享
宝玉的分享
博客园 - 叶小钗
有赞技术团队
有赞技术团队
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
酷 壳 – CoolShell
酷 壳 – CoolShell
量子位
罗磊的独立博客
小众软件
小众软件
Jina AI
Jina AI

cs.SI updates on arXiv.org

Hiding in Plain Sight: Finding MAHA on Reddit Prism: Structural Symmetry Scanning via Duality-Constrained Laplacian Projection MV-Gate: Insider Threat Detection via Multi-View Behavioral Statistics and Semantic Modeling Algorithmic Cultivation: How Social Media Feeds Shape User Language Universal Dynamics of Punctuated Progress AI-Mediated Communication Can Steer Collective Opinion CitePrism: Human-in-the-Loop AI for Citation Auditing and Editorial Integrity Explainable Detection of Depression Status Shifts from User Digital Traces Can Visual Mamba Improve AI-Generated Image Detection? An In-Depth Investigation ScioMind: Cognitively Grounded Multi-Agent Social Simulation with Anchoring-Based Belief Dynamics and Dynamic Profiles Humanwashing -- It Should Leave You Feeling Dirty When Do LLMs Generate Realistic Social Networks? A Multi-Dimensional Study of Culture, Language, Scale, and Method Moltbook Moderation: Uncovering Hidden Intent Through Multi-Turn Dialogue Linking Extreme Discourse to Structural Polarization in Signed Interaction Networks Predicting Channel Closures in the Lightning Network with Machine Learning Latent Causal Void: Explicit Missing-Context Reconstruction for Misinformation Detection Predictive Maps of Multi-Agent Reasoning: A Successor-Representation Spectrum for LLM Communication Topologies Large Language Models for Causal Relations Extraction in Social Media: A Validation Framework for Disaster Intelligence When Can Digital Personas Reliably Approximate Human Survey Findings? RAwR: Role-Aware Rewiring via Approximate Equitable Partition GravityGraphSAGE: Link Prediction in Directed Attributed Graphs Structure-Centric Graph Foundation Model via Geometric Bases Attention-based graph neural networks: a survey When AI Meets Science: Research Diversity, Interdisciplinarity, Visibility, and Retractions across Disciplines in a Global Surge Scalable inference of spatial regions and temporal signatures from time series Can LLMs Emulate Human Belief Dynamics? Predicting Post Virality with Temporal Cross-Attention over Trend Signals H3: A Healthcare Three-Hop Index for Physician Referral Network Prediction Dynamic Graph with Similarity-Aware Attention Graph Neural Network for Recommender Systems Spectral Graph Sparsification Preserves Representation Geometry in Graph Neural Networks
Modelling the Climate Change Debate in Italy through Info...
Irene Scalco, Giulia Colafrancesco, Matteo Cinelli · 2025-03-21 · via cs.SI updates on arXiv.org

Climate change is one of the most critical challenges of the twenty-first century. Public understanding of climate issues and of the goals regarding the climate transition is essential to translate awareness into concrete actions. In this context, social media platforms play a crucial role in disseminating information about climate change and climate policy. To better understand the dynamics of information circulation and the emergence of information voids we propose a model that takes into account the supply and demand of information related to the Italian climate-transition discourse. We conceptualise information supply as the production of content on Facebook, Instagram and GDELT (an online news database) while leveraging Google searches to capture information demand. Our findings highlight responsiveness and temporal coupling between supply and demand, particularly during moments of heightened public attention triggered by significant external events. These responsive interactions reveal an overall adaptive information ecosystem. However, we also observe persistent information voids which may limit public understanding and delay meaningful engagement.