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

V
V2EX
aimingoo的专栏
aimingoo的专栏
S
SegmentFault 最新的问题
博客园_首页
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
IT之家
IT之家
博客园 - 【当耐特】
月光博客
月光博客
C
Check Point Blog
T
The Blog of Author Tim Ferriss
罗磊的独立博客
博客园 - Franky
MongoDB | Blog
MongoDB | Blog
H
Help Net Security
Microsoft Security Blog
Microsoft Security Blog
B
Blog
阮一峰的网络日志
阮一峰的网络日志
腾讯CDC
美团技术团队
N
Netflix TechBlog - Medium
Stack Overflow Blog
Stack Overflow Blog
Y
Y Combinator Blog
L
LangChain Blog
The Cloudflare Blog

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
Examining Factors Associated with Twitter Account Suspens...
Farhan Asif Chowdhury, Dheeman Saha, Md Rashidul Hasan, Koustuv · 2021-01-24 · via cs.SI updates on arXiv.org

Online social media enables mass-level, transparent, and democratized discussion on numerous socio-political issues. Due to such openness, these platforms often endure manipulation and misinformation - leading to negative impacts. To prevent such harmful activities, platform moderators employ countermeasures to safeguard against actors violating their rules. However, the correlation between publicly outlined policies and employed action is less clear to general people. In this work, we examine violations and subsequent moderation related to the 2020 U.S. President Election discussion on Twitter, a popular micro-blogging site. We focus on quantifying plausible reasons for the suspension, drawing on Twitter's rules and policies by identifying suspended users (Case) and comparing their activities and properties with (yet) non-suspended (Control) users. Using a dataset of 240M election-related tweets made by 21M unique users, we observe that Suspended users violate Twitter's rules at a higher rate (statistically significant) than Control users across all the considered aspects - hate speech, offensiveness, spamming, and civic integrity. Moreover, through the lens of Twitter's suspension mechanism, we qualitatively examine the targeted topics for manipulation.