Liu Xingyi
School of Photography, Communication University of China Nanjing, Nanjing, 210000, PR China.
Heliyon. 2024 Aug 30;10(18):e36658. doi: 10.1016/j.heliyon.2024.e36658. eCollection 2024 Sep 30.
The rapid progress of science and technology has revolutionized the dissemination of information, with the Internet playing a crucial role. While it has enhanced the ease of sharing information, it has also hastened the transmission of emotions on social media, sometimes resulting in unintended negative outcomes. This research seeks to tackle this issue by suggesting an innovative technique for analyzing social network influence using the Susceptible Hesitant Infected Removed (SHIR) and Susceptible Latent Propagative Removal (SLPR) propagation models. Through the development of an emotional communication model, we take into account the effects of news and public opinion on the rate of emotional communication among individuals. Furthermore, we investigate the impact of various network structures on user behavior. The findings from experiments demonstrate a notable relationship between changes in the density of emotion spreaders and hesitants and the influence of nodes in different network configurations. Specifically, the analysis reveals that the peaks of hesitators and disseminators were lower when the node influence was reduced. Additionally, we verified the precision and dependability of our model by examining data from the Baidu Index, a tool for big data analysis. The margin of error between the model and the actual data was minimal, underscoring the efficacy of our approach. In essence, the study highlights a direct correlation between the speed and extent of emotional propagation in social networks and the degree of nodes. The results showed that the density changes of emotion spreaders and hesitants were significantly correlated with the influence of nodes in different network settings. In the case of node influence of 0.86, the highest peaks of hesitator H and disseminator I were 0.101 and 0.109 lower than those of influence of 1.25. The data analysis of the Baidu Index showed that the maximum peak error of the model was only 0.04, which verified the accuracy and reliability of the model. This investigation carries significant implications for efficiently managing and steering the dissemination of emotions on social media, thereby promoting a healthier online environment.
科学技术的飞速发展彻底改变了信息传播方式，互联网在其中发挥了关键作用。虽然它提高了信息共享的便利性，但也加速了社交媒体上情绪的传播，有时会导致意想不到的负面结果。本研究旨在通过提出一种创新技术来解决这一问题，该技术利用易感犹豫感染移除（SHIR）和易感潜伏传播移除（SLPR）传播模型分析社交网络影响。通过开发情感传播模型，我们考虑了新闻和公众舆论对个体间情感传播速度的影响。此外，我们还研究了各种网络结构对用户行为的影响。实验结果表明，情绪传播者和犹豫者密度的变化与不同网络配置中节点的影响之间存在显著关系。具体而言，分析表明，当节点影响力降低时，犹豫者和传播者的峰值较低。此外，我们通过分析百度指数（一种大数据分析工具）的数据，验证了模型的准确性和可靠性。模型与实际数据之间的误差幅度极小，突出了我们方法的有效性。本质上，该研究强调了社交网络中情绪传播的速度和程度与节点程度之间的直接相关性。结果表明，情绪传播者和犹豫者的密度变化与不同网络环境中节点的影响显著相关。在节点影响力为0.86的情况下，犹豫者H和传播者I的最高峰分别比影响力为1.25时低0.101和0.109。百度指数的数据分析表明，模型的最大峰值误差仅为0.04，验证了模型的准确性和可靠性。这项调查对于有效管理和引导社交媒体上的情绪传播具有重要意义，从而促进更健康的网络环境。
