Information dissemination has changed rapidly in recent years with the emergence of social media which provides online platforms for people worldwide to share their thoughts, activities, emotions, and build social relationships. Hence, modeling information diffusion has become an important area of research in the field of network analysis. It involves the mathematical modeling of the movement of information and study the information spread pattern. In this paper, we attempt to model information propagation in online social networks using a nature-inspired approach based on a modified forest-fire model. A slight spark can start a wildfire in a forest, and the spread of this fire depends on vegetation, weather, and topography, which may act as fuel. On similar lines, we labeled users who haven’t joined the network yet as Empty, existing users as Tree, and information as Fire. The spread of information across online social networks depends upon users-followers relationships, the significance of the topic, and other such features. We introduce a novel Burnt state to the traditional forest-fire model to represent non-spreaders in the network. We validate our method on six real-world data-sets extracted from Twitter and conclude that the proposed model performs reasonably well in predicting information diffusion.

中文翻译：

---

使用改进的森林火灾模型对在线社交网络中的信息扩散进行建模


近年来，随着社交媒体的出现，信息传播发生了迅速变化，社交媒体为世界各地的人们提供了分享思想、活动、情感和建立社会关系的在线平台。因此，信息扩散建模已成为网络分析领域的一个重要研究领域。它涉及信息运动的数学建模并研究信息传播模式。在本文中，我们尝试使用基于改进的森林火灾模型的自然启发方法对在线社交网络中的信息传播进行建模。轻微的火花就可以在森林中引发野火，而火灾的蔓延取决于植被、天气和地形，这些都可能成为燃料。同样，我们将尚未加入网络的用户标记为“空”，将现有用户标记为“树”，将信息标记为“火”。信息在在线社交网络上的传播取决于用户与关注者的关系、主题的重要性以及其他此类特征。我们在传统的森林火灾模型中引入了一种新颖的燃烧状态来代表网络中的非传播者。我们在从 Twitter 中提取的六个真实世界数据集上验证了我们的方法，并得出结论，所提出的模型在预测信息扩散方面表现相当良好。