Static and Dynamic Strategies for Influencing Opinions in Social Networks

2026-05-14

AI Summary

This study investigates how a small number of coordinated actors can manipulate collective opinion in social networks through either static strategies—maintaining fixed extreme views—or dynamic strategies—gradually shifting from moderate to extreme positions—thereby threatening the fairness and integrity of public discourse. Building upon the Hegselmann–Krause bounded-confidence model, the authors conduct dynamical simulations on weighted LFR networks with community structure, selecting intervention nodes using various centrality measures (e.g., degree, PageRank, k-core). The results demonstrate that dynamic strategies substantially outperform static ones by effectively leveraging the bounded-confidence mechanism to incrementally recruit moderate individuals and expand influence. Moreover, dynamic interventions exhibit strong robustness to node selection: even random or simplistic targeting yields high efficacy, whereas static interventions often trigger early opinion fragmentation and achieve limited impact.

Abstract

The ability of a small set of coordinated actors to manipulate opinions in online social networks poses a serious challenge to the fairness and integrity of public debate. We investigate this problem by studying how targeted stubborn agents can shift the average opinion of a network governed by the Hegselmann-Krause bounded-confidence dynamics. Experiments are conducted on weighted LFR benchmark networks with community structure, using multiple node-selection strategies based on degree, strength, PageRank, betweenness, k-coreness, s-coreness, and salience. We compare static interventions, in which stubborn agents keep a fixed extreme opinion, with dynamic interventions, in which their opinion gradually evolves from moderate to extreme values. Results show that dynamic strategies are substantially more effective than static ones, as they exploit bounded-confidence dynamics to progressively recruit intermediate agents and extend influence across the network. In contrast, static strategies tend to create early opinion separation and therefore have a more limited reach. We also find that while some centrality measures offer advantages in static settings, dynamic interventions can achieve strong performance even with simple or random node selection. Overall, the study clarifies how intervention design and target selection interact in shaping collective opinions, with implications for understanding and countering manipulation in social networks.

Problem

• opinion manipulation
• social networks
• bounded-confidence dynamics
• stubborn agents
• collective opinion

Innovation

• dynamic intervention
• opinion dynamics
• bounded-confidence model
• stubborn agents
• social influence