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

酷 壳 – CoolShell
酷 壳 – CoolShell
D
DataBreaches.Net
C
Check Point Blog
雷峰网
雷峰网
小众软件
小众软件
GbyAI
GbyAI
美团技术团队
P
Proofpoint News Feed
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
罗磊的独立博客
大猫的无限游戏
大猫的无限游戏
WordPress大学
WordPress大学
MyScale Blog
MyScale Blog
The Cloudflare Blog
阮一峰的网络日志
阮一峰的网络日志
Apple Machine Learning Research
Apple Machine Learning Research
Y
Y Combinator Blog
Jina AI
Jina AI
爱范儿
爱范儿
Last Week in AI
Last Week in AI
MongoDB | Blog
MongoDB | Blog
I
InfoQ
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
博客园 - 司徒正美

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
Security Advice for Parents and Children About Content Fi...
Ran Elgedawy, John Sadik, Anuj Gautam, Trinity Bissahoyo, Christ · 2024-02-06 · via cs.SI updates on arXiv.org

In today's digital age, concerns about online security and privacy have become paramount. However, addressing these issues can be difficult, especially within the context of family relationships, wherein parents and children may have conflicting interests. In this environment, parents and children may turn to online security advice to determine how to proceed. In this paper, we examine the advice available to parents and children regarding content filtering and circumvention as found on YouTube and TikTok. In an analysis of 839 videos returned from queries on these topics, we found that half (n=399) provide relevant advice. Our results show that of these videos, roughly three-quarters are accurate, with the remaining one-fourth containing factually incorrect advice. We find that videos targeting children are both more likely to be incorrect and actionable than videos targeting parents, leaving children at increased risk of taking harmful action. Moreover, we find that while advice videos targeting parents will occasionally discuss the ethics of content filtering and device monitoring (including recommendations to respect children's autonomy) no such discussion of the ethics or risks of circumventing content filtering is given to children, leaving them unaware of any risks that may be involved with doing so. Ultimately, our research indicates that video-based social media sites are already effective sources of security advice propagation and that the public would benefit from security researchers and practitioners engaging more with these platforms, both for the creation of content and of tools designed to help with more effective filtering.