用用户偏见动态调整，更准识别社交网络中的信息回音室。
Gravity Well Echo Chamber Modeling With An LLM-Based Confirmation Bias Model
- 引入用户偏见变量，动态调节信息吸引强度。
- 在19个Reddit社区验证，检测效果显著提升。
- 适合研究虚假信息传播与社群健康评估的人看。
社交媒体回音室在虚假信息传播中起核心作用，但现有模型常忽略个体确认偏误的影响。现有回音室模型之一的“引力井”模型，将回音室类比为空间引力井。本文通过引入动态确认偏误变量，根据用户对多元观点内容的回应与其发帖历史的对比，动态调整其受信念强化内容吸引的强度，构建了融合确认偏误的引力井模型。该模型能更准确识别回音室，并揭示信息健康的群体级指标。在19个Reddit社区上验证表明，该方法提升了回音室检测能力。本研究提出了一种系统捕捉确认偏误在在线群体行为中作用的框架，有助于在虚假信息最易放大处及时预警，支持遏制其传播。
原文摘要 · Abstract (English)
Social media echo chambers play a central role in the spread of misinformation, yet existing models often overlook the influence of individual confirmation bias. An existing model of echo chambers is the "gravity well" model, which creates an analog between echo chambers and spatial gravity wells. We extend this established model by introducing a dynamic confirmation bias variable that adjusts the strength of pull based on a user's susceptibility to belief-reinforcing content. This variable is calculated for each user through comparisons between their posting history and their responses to posts of a wide range of viewpoints. Incorporating this factor produces a confirmation-bias-integrated gravity well model that more accurately identifies echo chambers and reveals community-level markers of information health. We validated the approach on nineteen Reddit communities, demonstrating improved detection of echo chambers. Our contribution is a framework for systematically capturing the role of confirmation bias in online group dynamics, enabling more effective identification of echo chambers. By flagging these high-risk environments, the model supports efforts to curb the spread of misinformation at its most common points of amplification.
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