Understanding how the topic evolves is an important and challenging task. A topic usually consists of multiple related events, and the accurate identification of event evolution relationship plays an important role in topic evolution analysis. Existing research has used the traditional vector space model to represent the event, which cannot be used to accurately compute the semantic similarity between events. This has led to poor performance in identifying event evolution relationship. This paper suggests constructing a semantic aspect-based vector space model to represent the event: First, use hierarchical Dirichlet process to mine the semantic aspects. Then, construct a semantic aspect-based vector space model according to these aspects. Finally, represent each event as a point and measure the semantic relatedness between events in the space. According to our evaluation experiments, the performance of our proposed technique is promising and significantly outperforms the baseline methods.

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Stance detection, which focuses on users' deep attitudes, is an important way to understand the online public opinion. This paper presents an overview of stance detection. First, we present a general framework for stance detection, and the main steps of the framework are introduced in detail. The state‐of‐the‐art stance detection methods are categorized into three classes: feature‐based methods, deep learning‐based methods, and ensemble learning‐based methods. Moreover, the advantages and limitations of the existing methods are analyzed. The survey findings show that hybrid‐neural network‐based methods are superior to the other methods. In addition, existing methods still need to pay more attention to the sentiment information, user‐interaction, and attempt to merge more external knowledge to help improve the effect of stance detection.