Spiral of Silence in Large Language Model Agents (2025)

Abstract: The Spiral of Silence (SoS) theory holds that individuals with minority views often refrain from speaking out for fear of social isolation, enabling majority positions to dominate public discourse. When the “agents” are large language models (LLMs), however, the classical psychological explanation is not directly applicable, since SoS was developed for human societies. This raises a central question: can SoS-like dynamics nevertheless emerge from purely statistical language generation in LLM collectives? We propose an evaluation framework for examining SoS in LLM agents. Specifically, we consider four controlled conditions that systematically vary the availability of “History” and “Persona” signals. Opinion dynamics are assessed using trend tests such as Mann–Kendall and Spearman’s rank, along with concentration measures including kurtosis and interquartile range. Experiments across open-source and closed-source models show that history and persona together produce strong majority dominance and replicate SoS patterns; history signals alone induce strong anchoring; and persona signals alone foster diverse but uncorrelated opinions, indicating that without historical anchoring, SoS dynamics cannot emerge. The work bridges computational sociology and responsible AI design, highlighting the need to monitor and mitigate emergent conformity in LLM-agent systems.

## introduction in human societies, public opinion is shaped by complex social dynamics. the spiral of silence (sos) theory @xcite posits that individuals tend to withhold opinions they perceive as unpopular due to fear of social isolation. this creates a self-reinforcing cycle in which mi-nority views gradually disappear from public discourse, making dominant opinions appear increasingly widespread. a pressing question is whether analogous dynamics can arise in populations of artificial agents. large language models (llms) are rapidly deployed in multi-agent environments where they collaborate, negotiate, or compete @xcite . unlike humans, llms do not experience emotions or social anxieties, yet recent studies show they adapt to social-like cues-displaying sycophantic tendencies toward users @xcite and reproducing grouplevel biases @xcite . if sos-like effects can emerge in such settings, this would challenge traditional emotion-based explanations and raise critical concerns about bias amplification, conformity, and opinion manipulation in llm-driven systems. to investigate this possibility, we design an evaluation framework for detecting sos dynamics in llm agents. we adopt a controlled movie-rating task, which provides a quantifiable environment for measuring opinion formation. two key signals are introduced: (i) history, the average rating of preceding agents, serving as a dynamic proxy for the collective opinion climate; (ii) persona, a role assigned to each agent that encodes predispositions and ensures diversity of initial preferences. crossing these signals allows us to disentangle the influence of collective anchoring and individual predisposition, and to test whether their interaction gives rise to sos-like convergence. we hypothesize that sos dynamics will be most evident when they drive agents to align with perceived majorities. to measure this, we combine concentration statistics such as interquartile range @xcite and kurtosis @xcite with trend diagnostics including the mann-kendall test @xcite ) and spearman's rank correlation @xcite . we evaluate a range of llm families, including the open-source qwen @xcite , deepseek @xcite , and mistral, as well as the closedsource gpt-4o-mini @xcite , enabling both cross-family comparisons and within-family scaling analyses. our findings show that in the absence of social signals, agents default to positive movie ratings; persona signals promote opinion heterogeneity; history signals exert strong anchoring effects; and sos dynamics emerge most clearly when both signals are present. our contributions are as follows: • we propose a systematic framework for testing sos in llm agents, isolating the roles of collective influence through history and individual predisposition through persona. 1 • we provide the first evidence that sos-like dynamics can arise from learned language generation mechanisms, yielding new insights into the nature of conformity. ## methodology this section describes the task simulation, presents the evaluation framework, and specifies the metrics and criteria for measuring opinion dynamics and detecting sos effects in llm agents. ## conclusion our study shows that llm-based movie rating agents exhibit a clear positivity bias by default, yet develop more diverse opinions when given distinct personas and increasingly conform to prior context when a historical collective opinion is provided. by crossing binary signals design (persona × history), we isolate the influence of each signal: persona alone induces opinion diversity, history alone imposes anchoring consistency, and only their combination triggers a pronounced sos. these results highlight that a sos can spontaneously emerge in llm agents without any emotional drive: purely from the interplay between internalized statistical biases and externally presented collective signals. this insight underscores the power of social context in shaping ai behavior and reminds us to remain alert to the social biases embedded in llms that can influence such simulations.