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

爱范儿
爱范儿
MyScale Blog
MyScale Blog
Recent Announcements
Recent Announcements
N
Netflix TechBlog - Medium
GbyAI
GbyAI
Vercel News
Vercel News
The GitHub Blog
The GitHub Blog
阮一峰的网络日志
阮一峰的网络日志
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
V
Visual Studio Blog
Martin Fowler
Martin Fowler
腾讯CDC
大猫的无限游戏
大猫的无限游戏
aimingoo的专栏
aimingoo的专栏
云风的 BLOG
云风的 BLOG
J
Java Code Geeks
WordPress大学
WordPress大学
P
Proofpoint News Feed
雷峰网
雷峰网
酷 壳 – CoolShell
酷 壳 – CoolShell
有赞技术团队
有赞技术团队
人人都是产品经理
人人都是产品经理
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
Y
Y Combinator 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
Can WhatsApp Counter Misinformation by Limiting Message F...
Philipe de Freitas Melo, Carolina Coimbra Vieira, Kiran Garimell · 2019-09-19 · via cs.SI updates on arXiv.org

WhatsApp is the most popular messaging app in the world. The closed nature of the app, in addition to the ease of transferring multimedia and sharing information to large-scale groups make WhatsApp unique among other platforms, where an anonymous encrypted messages can become viral, reaching multiple users in a short period of time. The personal feeling and immediacy of messages directly delivered to the user's phone on WhatsApp was extensively abused to spread unfounded rumors and create misinformation campaigns during recent elections in Brazil and India. WhatsApp has been deploying measures to mitigate this problem, such as reducing the limit for forwarding a message to at most five users at once. Despite the welcomed effort to counter the problem, there is no evidence so far on the real effectiveness of such restrictions. In this work, we propose a methodology to evaluate the effectiveness of such measures on the spreading of misinformation circulating on WhatsApp. We use an epidemiological model and real data gathered from WhatsApp in Brazil, India and Indonesia to assess the impact of limiting virality features in this kind of network. Our results suggest that the current efforts deployed by WhatsApp can offer significant delays on the information spread, but they are ineffective in blocking the propagation of misinformation campaigns through public groups when the content has a high viral nature.