## 1 Introduction  
With the advancement of information technology and the rapid development of the Internet, the emergence of social media platforms has brought unprecedented changes to people's lifestyles and ways of thinking. Social media platforms have a large number of users. Additionally, social media has become an important source for people to understand current affairs, connect with the world, and obtain real-time information [1]. Many users love to describe their daily lives and participate in discussions on hot topics on social media platforms [2]. Hot topics refer to a major social event, a public policy that has attracted the attention of the whole people, or a country's elections and other hot events, and so on. Itoften refer to hot issues that the people are most concerned about within a certain time and within a certain range. At the same time, with outbreaks of incidents, different users have different perspectives on hot events, and their stances are also different. Different stances often lead to an explosion of public opinion. Therefore, research on stance detection tasks is very necessary. Stance detection is a sub-branch of sentiment analysis. Sentiment analysis aims to automatically determine the sentiment tendency of a user's tweet toward a specific object. The significant difference is that in the stance detection task, the purpose is to judge whether the user viewpoint towards a given target is positive (support, pro), negative (oppose, con), or neutral [3]. In real-life applications, stance detection plays a vital role in many specific tasks, such as analyzing political debates on online forums [4], fake news and false rumors [5], political stance trends [6], etc. Due to the critical influence of stance detection in online social networks, an increasing number of people have studied stance detection in recent years. For example, [PERSON] et al. [7] proposed a deep attention CNN-LSTM model that uses user useful relationships features available in additional social media such as friendship to improve performance. [PERSON] [8] proposed a stance detection model. The model uses and explores the features based on the sequence of event and emotional information. With the rapid development of external knowledge engineering, we have discovered that external knowledge plays a vital role in the application of natural language processing (NLP). For example, short text classification [9] and sentiment analysis [10] combined with external knowledge can achieve better performance. Moreover, [PERSON] et al. [11] found that people's emotions are conducive to the stance classification of tweets. Therefore, we extract emotional words by using external knowledge to improve the performance of stance detection. Although in the previous works, the research on user stance detection achieved great success, we find that all have a common problem: ignoring the difference between stance and viewpoint. Regarding our daily life and the information, we find that stance is an affirmation of viewpoint and that their viewpoints are only one of the reasons for the users to confirm their stance. To better understand stance detection, we assume that user tweets are sent in chronological order, the user's viewpoint is related to the topic, and the user's viewpoint may change with the spread of time and events. Therefore, through the summary of the previous work, we first define the user's stance and propose a method based on integrating external commonsense knowledge and environmental information (ECKEI) to detect the stance of user-level tweets in social networks. Here, we collectively refer to the impact of topic information and neighborhood information as environmental information. User-level stance detection aims to use all tweets of a user in a period of time to analyse user stance, while tweet-level stance detection only uses one tweet to analyse user stance.