Intervening in Negative Emotion Contagion on Social Networks Using Reinforcement Learning
Qin Deng, Xiaoliang Chen, Peng Lu, Yajun Du, Xianyong Li
Abstract
Recent advancements in online social networks (e.g., Facebook, Twitter, Weibo) highlight the need for effective interventions in combating the rapid spread of negative emotions triggered by adverse public events. Existing studies on emotional contagion have explored factors like network structure and individual susceptibility but lack a systematic understanding of intervention measures and their efficacy. This article addresses this gap as the negative emotion intervention (NEI) problem. While current strategies, such as blocking users, struggle with accurately interpreting subtle shifts in contagion patterns, our study focuses on leveraging reinforcement learning with deep Q-networks (DQN) to develop dynamic blocking strategies. We introduce the novel negative emotion contagion model (NECM) that considers spreaders’ capability, receivers’ sensitivity, and event popularity. Our study pioneers the optimized dynamic blocking approach, NECM-RLDB, which outperforms heuristic methods (out-degree, betweenness centrality, PageRank) in experimental tests on artificial and real-world datasets.