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

宝玉的分享
宝玉的分享
J
Java Code Geeks
S
SegmentFault 最新的问题
L
LangChain Blog
M
MIT News - Artificial intelligence
Stack Overflow Blog
Stack Overflow Blog
IT之家
IT之家
量子位
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
雷峰网
雷峰网
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
The Cloudflare Blog
MongoDB | Blog
MongoDB | Blog
Microsoft Security Blog
Microsoft Security Blog
腾讯CDC
H
Help Net Security
阮一峰的网络日志
阮一峰的网络日志
Jina AI
Jina AI
N
Netflix TechBlog - Medium
A
About on SuperTechFans
博客园 - 叶小钗
美团技术团队
人人都是产品经理
人人都是产品经理
D
DataBreaches.Net

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
Personal Information Privacy Settings of Online Social Ne...
Nahier Aldhafferi, Charles Watson, A. S. M Sajeev · 2013-05-13 · via cs.SI updates on arXiv.org

Protecting personal information privacy has become a controversial issue among online social network providers and users. Most social network providers have developed several techniques to decrease threats and risks to the users privacy. These risks include the misuse of personal information which may lead to illegal acts such as identity theft. This study aims to measure the awareness of users on protecting their personal information privacy, as well as the suitability of the privacy systems which they use to modify privacy settings. Survey results show high percentage of the use of smart phones for web services but the current privacy settings for online social networks need to be improved to support different type of mobile phones screens. Because most users use their mobile phones for Internet services, privacy settings that are compatible with mobile phones need to be developed. The method of selecting privacy settings should also be simplified to provide users with a clear picture of the data that will be shared with others. Results of this study can be used to develop a new privacy system which will help users control their personal information easily from different devices, including mobile Internet devices and computers.