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

云风的 BLOG
云风的 BLOG
Blog — PlanetScale
Blog — PlanetScale
博客园 - 【当耐特】
博客园_首页
The GitHub Blog
The GitHub Blog
月光博客
月光博客
Hugging Face - Blog
Hugging Face - Blog
有赞技术团队
有赞技术团队
博客园 - 三生石上(FineUI控件)
D
Docker
Stack Overflow Blog
Stack Overflow Blog
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
Apple Machine Learning Research
Apple Machine Learning Research
Vercel News
Vercel News
酷 壳 – CoolShell
酷 壳 – CoolShell
雷峰网
雷峰网
小众软件
小众软件
I
InfoQ
A
About on SuperTechFans
T
The Blog of Author Tim Ferriss
S
SegmentFault 最新的问题
Microsoft Azure Blog
Microsoft Azure Blog
博客园 - Franky

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
Measuring Cultural Dynamics Through the Eurovision Song C...
David García, Dorian Tanase · 2013-01-14 · via cs.SI updates on arXiv.org

Measuring culture and its dynamics through surveys has important limitations, but the emerging field of computational social science allows us to overcome them by analyzing large-scale datasets. In this article, we study cultural dynamics through the votes in the Eurovision song contest, which are decided by a crowd-based scheme in which viewers vote through mobile phone messages. Taking into account asymmetries and imperfect perception of culture, we measure cultural relations among European countries in terms of cultural affinity. We propose the Friend-or-Foe coefficient, a metric to measure voting biases among participants of a Eurovision contest. We validate how this metric represents cultural affinity through its relation with known cultural distances, and through numerical analysis of biased Eurovision contests. We apply this metric to the historical set of Eurovision contests from 1975 to 2012, finding new patterns of stronger modularity than using votes alone. Furthermore, we define a measure of polarization that, when applied to empirical data, shows a sharp increase within EU countries during 2010 and 2011. We empirically validate the relation between this polarization and economic indicators in the EU, showing how political decisions influence both the economy and the way citizens relate to the culture of other EU members.