YSocial Simulation Framework

• YSocial Framework is a digital twin simulation platform that uses agent-based models and LLMs to replicate realistic social media interactions.
• Its modular architecture—comprising a Platform Server, Simulation Client, and Stateless LLM Services—enables controlled simulation of user behavior, network formation, and content dissemination.
• The platform calibrates simulation results against real-world datasets, allowing researchers to evaluate digital norms, toxicity levels, and echo chamber dynamics through detailed analytical metrics.

The YSocial Framework is a digital twin simulation platform for online social media environments, centered on agent-based modeling powered by LLMs. It enables the generation, analysis, and operational validation of simulated social media activity, including interactions, network structures, content production, and the emergence of phenomena such as toxicity and echo chambers. By explicitly parameterizing agent personas, platform architectures, and interaction mechanisms, YSocial facilitates systematic experimentation, calibration against real-world datasets, and evaluation of digital norms and platform policies. Its design supports multidisciplinary research in computational social science, network analysis, NLP, and behavioral modeling.

1. Core Architecture and Simulation Components

The YSocial Framework constitutes a modular simulation environment, dividing responsibilities among three principal components:

• Platform Server: Maintains state for users, threads, posts, comments, and enforces platform rules like feed recommendation and visibility windows.
• Simulation Client: Orchestrates simulation rounds, agent instantiation, session management, population turnover (churn/new agent arrival), and schedules agent actions according to configurable activity distributions.
• Stateless LLM Services: Each agent’s behavioral output (posts, replies, reactions) is generated via concise micro-dialogues leveraging LLMs (e.g., Dolphin 3.0 based on Llama 3.1 8B) with norm-guided, context-aware prompts but without persistent agent memory across runs.

The platform mimics thread-and-feed social media environments (Twitter/Voat/Mastodon/BlueSky) by implementing core primitives: posting, commenting, replying, reading/sharing news, liking/disliking, following/unfollowing, link submission, and feed traversal. Content seeding (e.g., Voat’s catalog of technology-related URLs) and recommendation algorithms (ReverseChrono, popularity, follower-based, link prediction features) define the algorithmic backbone for information dissemination.

2. Agent Modeling and Activity Dynamics

Agents are parameterized via persona templates sampling from attribute distributions:

• Demographics: Age, education, locale
• Political Leaning: Values such as Faith & Flag Conservative, sampled to match target community (e.g., alt-right tilt on Voat)
• Interests: Drawn from curated catalogs (e.g., Big Tech, AI, cybersecurity)
• Toxicity Propensity: Levels ranging from “Absolutely No” to “Extremely,” influencing linguistic register

Action budgets—number of engagement acts per round—are allocated via truncated Zipf sampling ($P(k) \propto k^{-2.5}$, $k = 1 \dots 10$), yielding realistic heavy-tailed activity distributions observed in open platforms.

Agents participate in micro-dialogue action exchanges, receiving contextually detailed prompts (including thread history, topical focus, recent posts/comments) and producing output that reflects both persona and situation norms. No persistent agent memory is maintained; the system relies on server-side state to capture continuity.

3. Calibration, Content Generation, and Network Structures

Simulation scenarios are seeded and calibrated using real-world datasets:

• Content Catalogs: Built from domain-frequency tallies (e.g., Voat’s shared URLs spanning 30+ technology domains)
• Historical Data: MADOC dataset samples establish target metrics for posts, comments, thread depth, user engagement/churn; platform parameters are set to reproduce these activity patterns and structural features

Interaction networks are constructed with:

• Nodes: Users/agents
• Edges: Replying or mention relationships
• Network Descriptors: Degree, clustering coefficient, edge density, core-periphery block sizes, connected component analysis

Topic modeling (e.g., BERTopic) and embedding-based similarity (e.g., sentence embeddings from all-MiniLM-L6-v2) enable validation of topical alignment between simulated content and reference corpora.

4. Analytical Metrics and Operational Validation

YSocial evaluates simulation fidelity using:

• Activity Measures: Daily volume of posts, comments, active agents (calibrated to mirror modest traffic and turnover typical of niche communities such as v/technology)
• Interaction Network Analysis: Sparse, low-clustering, core-periphery structured graphs; comparison via stochastic block models to real-world data
• Content and Toxicity Evaluation: Continuous toxicity scoring using RoBERTa-based models trained on ToxiGen. Stratification of toxicity across posts and comments, comparison to empirical distributions (e.g., simulation frequently displays elevated root-post toxicity, differing from comment-dominated toxicity in Voat)
• Short-Range Convergence Entropy: Token-level cosine similarity:

$H(x; y) = -\sum_{i} \exp(\ell_i) \cdot \ell_i$

where $\ell_i$ denotes log-probabilities from maximum similarity between tokens in utterance $x$ and context $y$, measuring stylistic accommodation and decay as conversational lag increases.

Combined, these metrics assess whether simulated agent interactions replicate aggregate behavior, participation distributions, network structure, and linguistic properties of the reference platform.

5. Findings, Limitations, and Model Improvements

Simulation results using the YSocial Framework demonstrate operational regularity:

• Aggregate Behavior: Activity rhythms, thread length, heavy-tailed participation match historical Voat samples; realism in daily cycles and conversation brevity is achieved by calibrated agent action budgets and churn.
• Network Structure: Both simulated and real interaction graphs exhibit sparse connectivity, low clustering, and core-periphery hub formation; simulation cores may be more diffuse under simplified feedback/visibility rules.
• Content Alignment: Topic and embedding analysis confirm proximity to real-world discourse themes (privacy, AI ethics, Big Tech), with high cosine similarity in topical structure.
• Toxicity: Simulation shows higher baseline toxicity, especially in root posts, partially closing gaps typically observed between posts and comments in real communities. This supports testing moderation strategies in controlled environments but calls for further calibration.

Identified limitations include:

• Agent Memory: Agent processes are stateless within threads; lack of long-term adaptation and continuity impedes modeling of evolving identity and style.
• Single Run External Validity: Only one 30-day simulation is reported, hindering variance estimation and robust external validation.
• Feed Architecture: Simplified recommendation and visibility mechanisms may explain observed differences in network core structure and content reach.

Further work is proposed on multi-run uncertainty quantification, expanded feed architectures (popularity/controversiality-driven), heavier-tailed participation models, and thread-specific agent memory for extended adaptation.

6. Multidisciplinary Research and Policy Evaluation

YSocial’s digital twin capabilities offer substantive utility for computational social science, algorithmic governance, and behavioral analysis:

• Systematic Experimentation: Parameter manipulation (e.g., agent values, content curation, algorithmic bias) and controlled seeding enable the paper of “what-if” normative interventions and network evolution under varied conditions.
• Policy Design: Simulated environments facilitate ethical and practical assessment of moderation strategies, echo chamber dynamics, and bias amplification—tools crucial for real-world platform regulation.
• Content and Behavioral Analytics: Researchers may extract labeled linguistic and interaction datasets, supporting NLP tasks such as stance detection, sentiment modeling, and argument mining.

7. Technical Models and Mathematical Formalism

The YSocial Framework explicitly encodes:

• Truncated Zipf action budget sampling: $P(B = k) \propto k^{-s}$, $k = 1, \ldots, 10$, for $s = 2.5$
• Convergence entropy: $H(x; y) = -\sum_{i} \exp(\ell_i) \cdot \ell_i$, for token-level, cosine similarity-based calculation of linguistic alignment
• Core-periphery classification: Two-block stochastic block model partitioning for hub identification and engagement mapping

All architectural and operational claims, statistical models, calibration procedures, and evaluation results are specified in primary research sources (Rossetti et al., 1 Aug 2024, Tomašević et al., 29 Aug 2025).

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YSocial Framework represents a paradigm for generative and operationally validated social media simulation, combining sophisticated agent modeling, LLM-powered dialogue, platform emulation, and rigorous network and linguistic analytics. Its systematic approach enables precise calibration, experimental manipulation, and utility for both empirical research and policy exploration.