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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
"Gettr-ing" Deep Insights from the Social Network Gettr
Filipo Sharevski, Peter Jachim, Emma Pieroni, Amy Devine · 2022-04-08 · via cs.SI updates on arXiv.org

As yet another alternative social network, Gettr positions itself as the "marketplace of ideas" where users should expect the truth to emerge without any administrative censorship. We looked deep inside the platform by analyzing it's structure, a sample of 6.8 million posts, and the responses from a sample of 124 Gettr users we interviewed to see if this actually is the case. Administratively, Gettr makes a deliberate attempt to stifle any external evaluation of the platform as collecting data is marred with unpredictable and abrupt changes in their API. Content-wise, Gettr notably hosts pro-Trump content mixed with conspiracy theories and attacks on the perceived "left." It's social network structure is asymmetric and centered around prominent right-thought leaders, which is characteristic for all alt-platforms. While right-leaning users joined Gettr as a result of a perceived freedom of speech infringement by the mainstream platforms, left-leaning users followed them in numbers as to "keep up with the misinformation." We contextualize these findings by looking into the Gettr's user interface design to provide a comprehensive insight into the incentive structure for joining and competing for the truth on Gettr.