Credibility analysis of water environment complaint report based on deep cross domain network

Netizens’ complaint reports are the key to the early detection and treatment of environmental water pollution events, but there are often reports that are malicious, contain exaggerations, and deliberately exaggerate the facts. The manual analysis of complaint reports is complicated, and the lack of sufficient labeled data does not allow for an effective credibility classification model. This paper proposes a deep cross domain network, which learns knowledge from the source domain (microblog texts) and applies it to the target domain (complaint report texts) to improve the complaint report reliability classification model’s performance. First, the long short term memory extracts the domain-shared features of the source and target domains, the source-private features, and the target-private features. Then, the self-attention mechanism fuses the domain-shared features and the domain-private features. Finally, the multilayer perceptron outputs the classification result, while the multi-kernel maximum mean difference is used to calculate the distance between the source and target domains as part of the loss function. Experiments on complaint report texts and microblog texts show that our proposed method consistently outperforms other nonknowledge transfer models and completes the credibility analysis of water environment complaint reports well. This paper provides an innovative method for the credibility analysis of water environment complaint reports.