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

MyScale Blog
MyScale Blog
Apple Machine Learning Research
Apple Machine Learning Research
H
Help Net Security
雷峰网
雷峰网
V
Visual Studio Blog
G
Google Developers Blog
Microsoft Azure Blog
Microsoft Azure Blog
Hugging Face - Blog
Hugging Face - Blog
爱范儿
爱范儿
IT之家
IT之家
Engineering at Meta
Engineering at Meta
Microsoft Security Blog
Microsoft Security Blog
aimingoo的专栏
aimingoo的专栏
大猫的无限游戏
大猫的无限游戏
M
MIT News - Artificial intelligence
月光博客
月光博客
A
About on SuperTechFans
B
Blog RSS Feed
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
The GitHub Blog
The GitHub Blog
N
Netflix TechBlog - Medium
J
Java Code Geeks
云风的 BLOG
云风的 BLOG
Blog — PlanetScale
Blog — PlanetScale

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
Under the Conditions of Non-Agenda Ownership: Social Medi...
Artem Zakharchenko, Yuliia Maksimtsova, Valentyn Iurchenko, Vikt · 2019-09-04 · via cs.SI updates on arXiv.org

Owing to its history and challenging circumstances, social networks community in Ukraine is a very interesting polygon for the study of communications in the constantly changing environment, especially in the political discourse. This unique environment requires three dimensions to ascertain the political position of its participant. But 2019 presidential elections made this object even more spectacular. The winner of elections comedian Volodymyr Zelenskyi reached 73% of votes without any issue ownership, with empty agenda, and this influenced the electoral content of social networks and their authors behavior. We saw, that the issue ownership by other candidates succeeds in making their issues more salient in social networks. But the new phenomena, the non-agenda ownership, overcome any ideological influence, especially under the conditions of punishment mechanism applied to old politicians. Analyzing social media content and users behavior in the period between two rounds of elections, we found considerable overlaps between this campaign and the 2016 Trump campaign. We approved the widespread of filter bubbles, negative campaign messages, fake news and conspiracy theories. Active and powerful core of Ukrainian Facebook that was responsible for the Revolution of dignity now became less significant and even turns into the huge filter bubble of active people. We also proved that manipulations and fake news in the environment of private groups may be as much powerful as in a case of classical communication based around the opinion leaders.