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

WordPress大学
WordPress大学
Stack Overflow Blog
Stack Overflow Blog
人人都是产品经理
人人都是产品经理
Y
Y Combinator Blog
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
D
DataBreaches.Net
GbyAI
GbyAI
Microsoft Security Blog
Microsoft Security Blog
博客园_首页
大猫的无限游戏
大猫的无限游戏
Jina AI
Jina AI
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
Engineering at Meta
Engineering at Meta
IT之家
IT之家
MongoDB | Blog
MongoDB | Blog
The GitHub Blog
The GitHub Blog
月光博客
月光博客
U
Unit 42
Hugging Face - Blog
Hugging Face - Blog
博客园 - 叶小钗
腾讯CDC
B
Blog RSS Feed
博客园 - Franky
爱范儿
爱范儿

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
On spatial variation in the detectability and density of ...
Víctor H. Masías, Fernando Crespo, Pilar Navarro R., Razan Masoo · 2021-03-10 · via cs.SI updates on arXiv.org

Although much has been published regarding street protests on social media, few works have attempted to characterize social media users' spatial behavior in such events. The research reported here uses spatial capture-recapture methods to determine the influence of the built environment, physical proximity to protest location, and collective posting rhythm on variations in users' spatial detectability and density during a protest in Mexico City. The best-obtained model, together with explaining the spatial density of users, shows that there is high variability in the detectability of social media user protest supporters and that the collective posting rhythm and the day of observation are significant explanatory factors. The implication is that studies of collective spatial behavior would benefit by focussing on users' activity centres and their urban environment, rather than their physical proximity to the protest location, the latter being unable to adequately explain spatial variations in users' detectability and density during the protest event.