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arXiv 1706.02676cs.SI
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基于Agent的在线社交媒体中情绪传染与竞争模型

An agent-based model for emotion contagion and competition in online social media

Rui Fan, Ke Xu, Jichang Zhao

摘要:

最近的研究表明,人类的情绪不仅在现实世界的社区中传播,而且在在线社交媒体中传播。越来越多的研究揭示了情绪传染之外的机制,包括表明情绪影响的情绪相关性以及情绪扩散与网络结构(如联系强度)的耦合。此外,不同的情绪甚至可能在塑造公众舆论方面相互竞争。然而,一个全面的模型,考虑到最新的研究结果,以复制在线社交媒体中的情绪传染模式仍然缺失。在本文中,为了弥合这一重要差距,我们提出了一个基于Agent的情感传染模型,结合情感影响力和关系强度偏好的特点在传播过程中。仿真结果表明,愤怒主导的用户比快乐主导的用户具有更高的生命力,并且愤怒比快乐更倾向于扩散较弱的联系,从而更容易在在线群体之间传播。此外,愤怒的高度影响力使其具有竞争力,很容易支配社区,特别是在发生负面公共事件时。研究还意外地发现,随着愤怒与喜悦的比例差距不到10%,愤怒的推文和用户最终将主导在线社交媒体,并在网络空间中达到集体愤怒。这里披露的关键差距确实可以在在线社交媒体的愤怒控制的早期阶段发出警告信号。模型的所有参数都可以很容易地从经验观察中估计出来,并且它们的历史值可以帮助重现不同情况下的情绪传染。我们的模型将为研究诸如用计算机模拟预测情绪传染等多个问题提供启发。

英文摘要:

Recent studies suggest that human emotions diffuse in not only real-world communities but also online social media. More and more mechanisms beyond emotion contagion are revealed, including emotion correlations which indicate their influence and the coupling of emotion diffusion and network structure such as tie strength. Besides, different emotions might even compete in shaping the public opinion. However, a comprehensive model that considers up-to-date findings to replicate the patterns of emotion contagion in online social media is still missing. In this paper, to bridge this vital gap, we propose an agent-based emotion contagion model which combines features of emotion influence and tie strength preference in the dissemination process. The simulation results indicate that anger-dominated users have higher vitality than joy-dominated ones, and anger prefers weaker ties than joy in diffusion, which could make it easier to spread between online groups. Moreover, anger's high influence makes it competitive and easily to dominate the community, especially when negative public events occur. It is also surprisingly revealed that as the ratio of anger approaches joy with a gap less than 10%, angry tweets and users will eventually dominate the online social media and arrives the collective outrage in the cyber space. The critical gap disclosed here can be indeed warning signals at early stages for outrage controlling in online social media. All the parameters of the presented model can be easily estimated from the empirical observations and their values from historical data could help reproduce the emotion contagion of different circumstances. Our model would shed lights on the study of multiple issues like forecasting of emotion contagion in terms of computer simulations.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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