论文标题
Midmod-OSN：在线社交网络的微观级信息扩散模型
MIDMod-OSN: A Microscopic-level Information Diffusion Model for Online Social Networks
论文作者
论文摘要
由于在线社交网络继续被用于向公众传播信息，因此了解控制信息扩散的现象对于许多安全和安全有关的应用至关重要，例如在危机和自然灾害期间最大程度地提高信息传播和误解信息。在这项研究中，我们假设有助于在线社交网络中信息扩散的功能受到所研究事件类型的显着影响。我们将Twitter事件分类为信息性或趋势，然后探索与信息传播相关的节点对节点影响动态。我们建立了一个基于贝叶斯逻辑回归的模型，用于学习和预测以及特征选择的随机森林。实际数据集的实验结果表明，所提出的模型优于最先进的扩散预测模型，在信息事件中达到了93％的准确性，而在趋势事件中达到了86％。我们观察到，在扩散过程和控制扩散的用户特征中，信息和趋势事件的模型都有很大差异。我们的发现表明，关注者在扩散过程中起着重要作用，并且可以使用用户的扩散和OSN行为来预测消息的趋势特征，而无需计算反应的数量。
As online social networks continue to be commonly used for the dissemination of information to the public, understanding the phenomena that govern information diffusion is crucial for many security and safety-related applications, such as maximizing information spread and misinformation containment during crises and natural disasters. In this study, we hypothesize that the features that contribute to information diffusion in online social networks are significantly influenced by the type of event being studied. We classify Twitter events as either informative or trending and then explore the node-to-node influence dynamics associated with information spread. We build a model based on Bayesian Logistic Regression for learning and prediction and Random Forests for feature selection. Experimental results from real-world data sets show that the proposed model outperforms state-of-the-art diffusion prediction models, achieving 93% accuracy in informative events and 86% in trending events. We observed that the models for informative and trending events differ significantly, both in the diffusion process and in the user features that govern the diffusion. Our findings show that followers play an important role in the diffusion process and it is possible to use the diffusion and OSN behavior of users for predicting the trending character of a message without having to count the number of reactions.
