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From the perspective of news topic modeling, this paper investigated how the Communist Youth League of China (CYLC) uses organizational information communication to serve organizational goals-"Keep the Party Assured and the Youth Satisfied" (", "). Using the Latent Dirichlet allocation (LDA) algorithm, we performed a topic analysis on 1898 news articles published on the CYLC website. We discovered that nearly all of the CYLC's news centered on the achievement of its organizational goals, reflecting the characteristics of information dissemination that is highly supportive of organizational objectives. We discovered distinct differences in the dissemination of organizational information between the central, provincial, municipal, county, and school league committees through cluster analysis. The various league organizations have distinct positioning and distinguishing characteristics. In addition, correlation analysis reveals that higher-level league organizations prioritize the dissemination of "Keep the Party Assured" information. While lower-level organizations gradually implement "Keep the Youth Satisfied" initiatives. This paper fills a gap in research on mass organizations in the field of information dissemination and serves as a resource for other political organizations involved in public information dissemination.

AbstractSocial media-based investor sentiment proxies provide a brand new solution to recognize and measure investor sentiment. Aggregating individual social media text sentiments into public sentiments through a specific aggregation method is a necessary part of constructing an investor sentiment index for social media. The choice or design of the aggregation method directly affects whether or not the researcher can capture the sentiment of the market. This study provides the first systematic review of mainstream methods for aggregating investor sentiment from social media. In addition, we systematically discuss some of the key issues of historical researchers in aggregating investor sentiment, such as neutral sentiment text, simple aggregation of directly aggregated text, etc. The findings suggest that the aggregation method used by researchers directly affects the reliability of investor sentiment indices. Therefore, scholars should carefully choose sentiment aggregation algorithms based on the combination of datasets and sentiment tracking tools and articulate their rationale. This study provides important references for behavioral finance, social media mining, and microinvestor sentiment metrics.

AbstractResearch based on investor sentiment in social media has been a hot topic of research in behavioral finance, and the reliability of investor sentiment mined from social media is a potential condition for the reliability of the results of these studies. In the past, scholars have often focused on using more reliable tools to track investor sentiment in order to get more reliable investor sentiment. However, less attention has been paid to another key factor affecting the reliability of investor sentiment on social media: the selection and collection of data. In this study, we systematically investigate the process of data selection and collection in relation to the construction of investor sentiment on social media. Our findings suggest that the process of creating a dataset from social media is a process that starts and ends with a research question. In this process, we need to overcome various obstacles to end up with an imperfect dataset. The researchers must take a series of steps to get close to the best dataset and acknowledge some of the shortcomings and limitations. We emphasize that the absence of accepted, reliable standards makes it particularly important to follow basic principles. This study is an important reference for social media-based behavioral finance research.

AbstractThis study uses micro‐level panel data from Chinese manufacturing firms to investigate the impact of spatial agglomeration on firm productivity, taking a firm's engagement in international trade into consideration. Embracing firm heterogeneity in trade status, we find that non‐exporters benefit from urban agglomeration through manufacturing specialization, whereas little effect of local specialization on productivity is found among exporters. The findings are driven mainly by processing exporters involved in straightforward assembly. These findings increase the understanding of heterogeneous productivity gains from urban agglomeration and the spatial economy in China.