With the continuous enrichment of social network applications, such as TikTok, Weibo, Twitter, and others, social media have become an indispensable part of our lives. Web users can participate in their favorite events or pay attention to people they like. The “heterogeneous” influence between events and users can be effectively modeled, and users’ potential future behaviors can be predicted, so as to facilitate applications such as recommendations and online advertising. For example, a user’s favorite live streaming host (user) recommends certain products (event), can we predict whether the user will buy these products in the future? The majority of studies are based on a homogeneous graph neural network to model the influence between users. However, these studies ignore the impact of events on users in reality. For instance, when users purchase commodities through live streaming channels, in addition to the factors of the host, the commodity is also a key factor that influences the behavior of users. This study designs an influence prediction model based on a heterogeneous neural network HetInf. Specifically, we first constructed the heterogeneous social influence network according to the relationship between event nodes and user nodes, then sampled the user heterogeneous subgraph for each user, extracted the relevant node features, and finally predicted the probability of user behavior through the heterogeneous neural network model. We conducted comprehensive experiments on two large social network datasets. Furthermore, the experimental results show that HetInf is significantly superior to the previous homogeneous neural network methods.

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With the rapid popularity of agent technology, a public opinion early warning agent has attracted wide attention. Furthermore, a deep learning model can make the agent more automatic and efficient. Therefore, for the agency of a public opinion early warning task, the deep learning model is very suitable for completing 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 embed these features, and then apply the classification task to predict the cascade virality. We conduct comprehensive experiments on two large social network datasets. 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.

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