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

Blog — PlanetScale
Blog — PlanetScale
J
Java Code Geeks
N
Netflix TechBlog - Medium
Martin Fowler
Martin Fowler
A
About on SuperTechFans
腾讯CDC
B
Blog RSS Feed
H
Hackread – Cybersecurity News, Data Breaches, AI and More
Microsoft Azure Blog
Microsoft Azure Blog
D
Docker
Y
Y Combinator Blog
Microsoft Security Blog
Microsoft Security Blog
F
Fortinet All Blogs
I
InfoQ
博客园 - 【当耐特】
美团技术团队
GbyAI
GbyAI
量子位
宝玉的分享
宝玉的分享
爱范儿
爱范儿
有赞技术团队
有赞技术团队
博客园 - Franky
L
LangChain Blog
阮一峰的网络日志
阮一峰的网络日志

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
Dynamic Emotions of Supporters and Opponents of Anti-raci...
Jaihyun Park · 2021-09-29 · via cs.SI updates on arXiv.org

Social media empowers citizens to raise the voice and expressed civil outrage leads to collective action to change the society. Since social media welcomes anyone regardless of the political ideology or perspectives, social media is where the supporters and opponents of specific issue discuss. This study attempts to empirically examine a recent anti-racism movement initiated by the death of George Floyd with the lens of stance prediction and aspect-based sentiment analysis (ABSA). First, this study found the stance of the tweet and users do change over the course of the protest. Furthermore, there are more users who shifted the stance compared to those who maintained the stance. Second, both supporters and opponents expressed negative sentiment more on nine extracted aspects. This indicates that there was no significant difference of sentiment among supporters and opponents and raise a caution in predicting stance based on the sentiment. The contribution of the study is two-fold. First, ABSA was explored in the context of computational social science and second, stance prediction was first attempted at scale.