信息级联与转发网络中协调行为影响评估的最优与启发式策略

中文摘要 AI 辅助

协调性非真实行为（CIB）已成为在线社交平台的一大主要关切，但其对信息扩散的实际影响仍知之甚少。现有研究主要集中于检测协调活动，而相对较少关注在检测到之后量化其影响。在本工作中，我们引入了两个互补框架，用于对协调账户进行事后评估。首先，我们将信息级联上的问题表述为有向树上的约束影响最大化问题，并开发了一种多项式时间动态规划算法，该算法计算协调节点的最优放置，为其可实现的影响提供上界。其次，鉴于现实平台中扩散级联的有限可用性，我们提出了一种基于网络的框架，该框架使用独立级联模型直接从转发网络估计影响，并将协调账户的观察放置与既定启发式基线进行比较。我们在2019年英国大选的Twitter/X数据以及涵盖多个国家的经过验证的国家支持的信息操作活动集合上评估了这两种方法。虽然协调账户在英国级联中表现出有限的影响，但基于网络的分析揭示了各活动之间的显著差异，其中若干操作的实现影响达到或超过了结构上中心的种子集。最后，通过从转发网络重建级联，我们表明这两个框架产生了一致的结果，这表明观察到的效应反映了协调活动的内在结构属性，而非底层方法的人为产物。

英文摘要

Coordinated Inauthentic Behavior (CIB) has become a major concern in online social platforms, yet its actual impact on information diffusion remains poorly understood. Existing research has primarily focused on detecting coordinated activity, while comparatively little attention has been devoted to quantifying its influence once detected. In this work, we introduce two complementary frameworks for the post-hoc evaluation of coordinated accounts. First, we formulate the problem on information cascades as a constrained influence maximization problem over directed trees and develop a polynomial-time dynamic programming algorithm that computes the optimal placement of coordinated nodes, providing an upper bound on their achievable influence. Second, motivated by the limited availability of diffusion cascades in real-world platforms, we propose a network-based framework that estimates influence directly from retweet networks using the independent cascade model and compares the observed placement of coordinated accounts against established heuristic baselines. We evaluate both approaches on Twitter/X data from the 2019 UK General Election and on a collection of verified state-backed information operation campaigns spanning multiple countries. While coordinated accounts exhibit limited influence in the UK cascades, the network-based analysis reveals substantial differences across campaigns, with several operations achieving influence comparable to or exceeding that of structurally central seed sets. Finally, by reconstructing cascades from the retweet networks, we show that the two frameworks produce consistent results, suggesting that the observed effects reflect intrinsic structural properties of coordinated activity rather than artifacts of the underlying methodology.

发表机构

• Uniroma1.it（罗马第一大学）
• Gunma-u.ac.jp（群马大学）

机构由 AI 辅助整理，请以论文原文为准。