With the rapid popularity of agent technology, public opinion early warning agent is attracted wide attention. Furthermore, deep learning model can make agent more automatic and efficient. Therefore, for the agency of public opinion early warning task, the deep learning model is very suitable for completing the tasks such as popularity prediction or emergency outbreak.In this context, improving the ability to automatically analyze and predict the virality of information cascades is one of the tasks that deep learning model approaches address. However, most of the existing studies sought to address this task by analyzing cascade underlying network structure. Recent studies proposed cascade virality prediction for agnostic-networks(without network structure), but did not consider the fusion of more effective features. In this paper, we propose an innovative cascade virus prediction model named CasWarn. It can be quickly deployed in intelligent agents to effectively predict the virality of public opinion information for different industries. Inspired by the agnostic-network model, this model extracts the key features (independent of the underlying network structure) of an information cascade, including dissemination scale, emotional polarity ratio, and semantic evolution. We use two improved neural network frameworks to embedding these features, and then apply the classification task to predict the cascade virality. We conduct comprehensive experiments on two real large social network data sets. Furthermore, the experimental results prove that CasWarn can make timely and effective cascade virality predictions and verify that each feature model of CasWarn is beneficial to improve performance.

中文翻译：

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舆情预警代理模型：基于多特征融合的深度学习级联病毒式传播预测模型


随着代理技术的迅速普及，舆情预警代理受到广泛关注。此外，深度学习模型可以使智能体更加自动化和高效。因此，对于舆情预警任务的代理机构来说，深度学习模型非常适合完成人气预测或者突发事件爆发等任务。在此背景下，提高自动分析和预测信息级联病毒式传播的能力是其中之一。深度学习模型方法解决的任务。然而，大多数现有研究试图通过分析级联底层网络结构来解决这一任务。最近的研究提出了对不可知网络（无网络结构）的级联病毒式预测，但没有考虑更有效特征的融合。在本文中，我们提出了一种创新的级联病毒预测模型，名为 CasWarn。可以快速部署在智能代理中，有效预测不同行业舆情信息的病毒性。受不可知网络模型的启发，该模型提取信息级联的关键特征（独立于底层网络结构），包括传播规模、情感极性比和语义演化。我们使用两个改进的神经网络框架来嵌入这些特征，然后应用分类任务来预测级联病毒式传播。我们在两个真实的大型社交网络数据集上进行了综合实验。此外，实验结果证明CasWarn可以做出及时有效的级联病毒式传播预测，并验证CasWarn的每个特征模型都有利于性能的提升。