计算机科学
动力学(音乐)
情绪分析
社会动力
数据科学
人工智能
社会学
教育学
出处
期刊:Journal of Complex Networks
[Oxford University Press]
日期:2024-06-24
卷期号:12 (4)
被引量:1
标识
DOI:10.1093/comnet/cnae028
摘要
Abstract In the contemporary social network landscape, opinion polarization has emerged as a prominent challenge, sparking concerns about the effective guidance of news sentiment and mitigation of opposing opinions. This is particularly pertinent in the intricate web of social networks, where complexity reigns supreme. Addressing this pivotal issue, this article introduces a news opinion guidance approach grounded in motif recognition. To accurately mirror real-world social networks, we have crafted an agent-based model that simulates polarized news propagation. This model encompasses diverse media agents and user agents, meticulously replicating the news dissemination process within the network. In our quest to unveil the underlying structures of social networks, we have enhanced the Augmented Multiresolution Network approach, incorporating multi-dimensional node attributes for more nuanced clustering and network mapping. This refinement enables us to pinpoint potential motif regions with greater precision. Leveraging these insights, we introduce a triangular motif-based opinion guidance strategy aimed at shaping opinion distribution by bolstering the influence of nodes within these motifs. Once the pertinent motifs are identified, we undertake simulation experiments that reveal the remarkable efficacy of our motif recognition-driven guidance strategy. Notably, it reduces opinion polarization by a substantial 74% compared to scenarios without guidance strategies. This research offers a fresh perspective on crafting personalized and targeted news sentiment guidance strategies. It presents a versatile and potent computational framework for understanding and managing polarization phenomena in social networks, carrying profound theoretical and practical ramifications.
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