AI 中文总结
本研究针对回音室检测缺乏严谨算法基础的问题，提出基于Jaccard同质性与种子扩展的JECHO算法，可高效检测结构孤立的回音室，性能优于现有最优方法且运行时间大幅缩短。
AI 中文摘要
检测回音室对于理解和限制在线极化、错误信息传播、阴谋论扩散等负面社会现象至关重要，但回音室检测（ECD）问题目前缺乏严谨的算法基础。我们基于三个原则——内部观点同质性、观点极端性和结构孤立性，正式定义了回音室的统一概念，在此定义下证明ECD问题是NP难的，通过归约到 conductance最小化问题完成该证明。为规避这一计算障碍，我们推导了有效回音室内节点基于Jaccard的同质性（JHO）的理论下界，该保证启发了JECHO算法，这是一种通过局部种子扩展而非全局枚举检测回音室的新算法。JECHO首先识别出超过JHO阈值的种子，然后应用基于分数的扩展来优化结构孤立性。在真实网络和合成网络上的大量实验表明，我们的理论导向方法比现有最优方法能检测到更多结构孤立的回音室，同时运行时间减少了数个数量级。
英文摘要
Detecting echo chambers is critical for understanding and limiting negative social phenomena, such as online polarization, misinformation, and conspiracy theory diffusion. However, the echo chamber detection (ECD) problem yet lacks a rigorous algorithmic foundation. We address this gap by formalizing a unified definition of echo chambers based on three principles: internal opinion homogeneity, opinion extremism, and structural isolation. Under such a definition, we establish the theoretical hardness of the ECD problem, proving it is NP-hard via a reduction from the conductance minimization problem. To circumvent this computational barrier, we derive a theoretical lower bound on the Jaccard-based homophily (JHO) of nodes that reside within valid echo chambers. This guarantee motivates JECHO, a novel algorithm that detects echo chambers via local seed expansion rather than global enumeration. JECHO first identifies seeds that exceed the JHO threshold and then applies a score-based expansion to optimize structural isolation. Extensive experiments on real-world and synthetic networks demonstrate that our theory-guided approach detects more structurally isolated echo chambers than state-of-the-art methods while reducing runtime by orders of magnitude.
CommentsAccepted to CIKM 2026
