Affective understanding of language is an important research focus in artificial intelligence. The large-scale annotated datasets of Chinese textual affective structure (CTAS) are the foundation for subsequent higher-level analysis of documents. However, there are very few published datasets for CTAS. This paper introduces a new benchmark dataset for the task of CTAS to promote development in this research direction. Specifically, our benchmark is a CTAS dataset with the following advantages: (a) it is Weibo-based, which is the most popular Chinese social media platform used by the public to express their opinions; (b) it includes the most comprehensive affective structure labels at present; and (c) we propose a maximum entropy Markov model that incorporates neural network features and experimentally demonstrate that it outperforms the two baseline models.

Compared with ordinary news, fake news has the characteristics of faster dissemination, lower production cost and causing great social harm, so the question of how to efficiently and accurately detect fake news has attracted a lot of interest from researchers. We propose a Two-Round Inconsistencybased Multi-modal fusion Network (TRIMOON) for fake news detection, which consists of three main components: the multi-modal feature extraction module, the multi-modal feature fusion module and the classification module. To filter the noise generated in the fusion process, we perform an inconsistency detection not only before, but also after the fusion process, and the experimental results prove this to be effective. Our proposed TRIMOON is evaluated on both the Chinese and the English datasets, and our model outperforms the state-of-the-art approach on several classification evaluation metrics.