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

Hugging Face - Blog
Hugging Face - Blog
V
Visual Studio Blog
Last Week in AI
Last Week in AI
Stack Overflow Blog
Stack Overflow Blog
The GitHub Blog
The GitHub Blog
Recent Announcements
Recent Announcements
博客园 - Franky
D
DataBreaches.Net
B
Blog
Y
Y Combinator Blog
T
The Blog of Author Tim Ferriss
Microsoft Azure Blog
Microsoft Azure Blog
人人都是产品经理
人人都是产品经理
WordPress大学
WordPress大学
P
Proofpoint News Feed
J
Java Code Geeks
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Martin Fowler
Martin Fowler
月光博客
月光博客
宝玉的分享
宝玉的分享
Engineering at Meta
Engineering at Meta
阮一峰的网络日志
阮一峰的网络日志
F
Fortinet All Blogs
博客园 - 【当耐特】

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
Exit Stories: Using Reddit Self-Disclosures to Understand...
Shruti Phadke · 2025-08-19 · via cs.SI updates on arXiv.org

Online platforms like Reddit are increasingly becoming popular for individuals sharing personal experiences of leaving behind social, ideological, and political groups. Specifically, a series of "ex-" subreddits on Reddit allow users to recount their departures from commitments such as religious affiliations, manosphere communities, conspiracy theories or political beliefs, and lifestyle choices. Understanding the natural process through which users exit, especially from problematic groups such as conspiracy theory communities and the manosphere, can provide valuable insights for designing interventions targeting disengagement from harmful ideologies. This paper presents an in-depth exploration of 15K exit stories across 131 subreddits, focusing on five key areas: religion, manosphere, conspiracy theories, politics, and lifestyle. Using a transdisciplinary framework that incorporates theories from social psychology, organizational behavior, and violent extremism studies, this work identifies a range of factors contributing to disengagement. The results describe how disengagement from problematic groups, such as conspiracy theories and the manosphere, is a multi-faceted process that is qualitatively different than disengaging from more established social structures, such as religions or political ideologies. This research further highlights the need for moving beyond interventions that treat conspiracy theorizing solely as an information problem and contributes insights for future research focusing on offering mental health interventions and support in exit communities.