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

Microsoft Security Blog
Microsoft Security Blog
WordPress大学
WordPress大学
Stack Overflow Blog
Stack Overflow Blog
D
DataBreaches.Net
罗磊的独立博客
博客园 - 司徒正美
Last Week in AI
Last Week in AI
The Cloudflare Blog
大猫的无限游戏
大猫的无限游戏
Microsoft Azure Blog
Microsoft Azure Blog
B
Blog RSS Feed
The GitHub Blog
The GitHub Blog
宝玉的分享
宝玉的分享
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
小众软件
小众软件
Jina AI
Jina AI
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
Hugging Face - Blog
Hugging Face - Blog
B
Blog
博客园 - 【当耐特】
V
V2EX
Apple Machine Learning Research
Apple Machine Learning Research
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
酷 壳 – CoolShell
酷 壳 – CoolShell

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
Collective response to the media coverage of COVID-19 Pan...
Nicolò Gozzi, Michele Tizzani, Michele Starnini, Fabio Ciulla, D · 2020-06-09 · via cs.SI updates on arXiv.org

The exposure and consumption of information during epidemic outbreaks may alter risk perception, trigger behavioural changes, and ultimately affect the evolution of the disease. It is thus of the uttermost importance to map information dissemination by mainstream media outlets and public response. However, our understanding of this exposure-response dynamic during COVID-19 pandemic is still limited. In this paper, we provide a characterization of media coverage and online collective attention to COVID-19 pandemic in four countries: Italy, United Kingdom, United States, and Canada. For this purpose, we collect an heterogeneous dataset including 227,768 online news articles and 13,448 Youtube videos published by mainstream media, 107,898 users posts and 3,829,309 comments on the social media platform Reddit, and 278,456,892 views to COVID-19 related Wikipedia pages. Our results show that public attention, quantified as users activity on Reddit and active searches on Wikipedia pages, is mainly driven by media coverage and declines rapidly, while news exposure and COVID-19 incidence remain high. Furthermore, by using an unsupervised, dynamical topic modeling approach, we show that while the attention dedicated to different topics by media and online users are in good accordance, interesting deviations emerge in their temporal patterns. Overall, our findings offer an additional key to interpret public perception/response to the current global health emergency and raise questions about the effects of attention saturation on collective awareness, risk perception and thus on tendencies towards behavioural changes.