## 3 Research Methods
### Data Acquisition and Preprocessing  
Data acquisition involves searching for and retrieving relevant texts from specific online platforms based on predetermined keywords. This process includes downloading textual data while adhering to legal and ethical standards, and capturing additional information such as the publisher, publication time, location, public comments, and forwarding volume tailored to the research needs. Data preprocessing encompasses correcting and refining text data to enhance its quality and applicability. This includes removing HTML tags, special characters, and extraneous spaces, eliminating stop words, standardizing data formats, removing duplicate entries, and processing noisy text. We utilized the Octoparse data collector with the keyword \"common prosperity\" to collect related Weibo posts from 1 January 2021 to 31 December 2023. The selection of this timeframe is grounded in China's accomplishment of eradicating absolute poverty and achieving a moderately prosperous society by the conclusion of 2020. Consequently, from 2021, China commenced its promotion of common prosperity, stimulating widespread discussions on the topic across Weibo. It is essential to acknowledge that China's Sina Weibo serves as a platform where the public can freely express their opinions. Bloggers post content with the intention of allowing public access and encouraging reposts, and the Sina Weibo platform does not restrict data scraping. Numerous studies based on Weibo posts have been published in journals and are permitted by the government, Sina Weibo, and the bloggers themselves. Therefore, this research complies with ethical standards. Initially, a total of 256,233 Weibo posts were collected, including information such as the publisher, publication time, repost volume, and like volume. However, the original dataset is plagued by noise, including irrelevant text, duplicate entries, overly succinct content, and meaningless symbols. Thus, the dataset underwent cleaning using Excel, re  
Figure 3: Research framework. sulting in a refined corpus comprising 190,741 posts. The statistics of the dataset, along with the search query words used to retrieve it, are presented in Table 1.  
Given the substantial volume of data, we used the Chinese Academy of Sciences tokenizer (i.e., the NLPIR big data semantic intelligence analysis platform) for Chinese word segmentation and stop word removal. Leveraging the Hidden Markov Model algorithm, this tokenizer facilitates functions such as new word discovery and batch intelligent word segmentation. Furthermore, a custom dictionary related to common prosperity was integrated into the segmentation dictionary to enhance the segmentation accuracy. Utilizing these tools, the original data underwent segmentation, automatically identifying unregistered words such as personal names, place names, and organization names, while also executing new word tagging and part-of-speech tagging. In particular, part-of-speech tagging was conducted using the open-source version of the NLP toolkit HANLP. This process categorized words into nouns, verbs, adjectives, pronouns, adverbs, etc., with a focus on selecting nouns, verbs, and adjectives as the primary parts of speech, while also filtering out single Chinese characters to effectively remove stop words. An example of text before and after Chinese word segmentation and stop word removal is shown in Table 2. It is necessary to note that the Weibo text is published in Chinese, and the English version is translated by us. The English word segmentation is arranged in Chinese word order. Furthermore, it is important to elucidate the method of generating the custom dictionary. The topic of common prosperity frequently intersects with everyday life, resulting in a predominantly common vocabulary. However, certain terms specifically pertain to common prosperity. For instance, while \"rural\" and \"revitalization\" are separate terms, \"rural revitalization\" refers specifically to one of China's strategies to promote common prosperity.