In the era of digital communication, WeChat public accounts have emerged as a critical channel for disseminating financial information, aggregating market perspectives, and facilitating investor education in China. These accounts—operated by financial institutions, research teams, professional media outlets, and seasoned market practitioners—consistently produce in-depth content covering macroeconomic trends, capital markets, industry developments, and corporate research. They play an increasingly vital role in shaping market expectations, transmitting policy signals, and influencing investment decisions.
To systematically track and analyze the public opinion landscape and information flow within China's capital markets, CnOpenData has launched the Chinese Financial News WeChat Public Account Database. Through automated collection and deep parsing of massive volumes of articles from financial WeChat public accounts, this database establishes a standardized textual data repository. Core fields encompass Article Title(文章标题), Publishing Account(发布公众号), Release Time(发布时间), Full Text(正文全文), Multimedia Identifiers(多媒体标识), and Basic Statistical Information(基础统计信息). This provides a high-quality, structured micro-level data foundation for observing market hotspot evolution, conducting text sentiment analysis, studying information propagation dynamics, and gaining insights into institutional perspectives.
Data Features:
- Comprehensive Content Deep Parsing: Beyond extracting metadata such as article titles, source accounts, and publication times, the database's core value lies in its complete inclusion of article full texts and the derived "Word Count(字数)" statistical field.
- Precise Focus on Financial Vertical Fields: Data collection is strictly confined to WeChat public accounts with financial themes, ensuring industry relevance and professionalism. This effectively addresses the challenge of filtering valuable financial information from a sea of general content, providing pure, high-quality samples for research on information dissemination and influence within specific domains.
Potential Application Scenarios:
- Academic Research: Applicable to fields such as finance (market efficiency and behavioral finance), computational communication (public opinion diffusion modeling), and text analysis (natural language processing). Can be used to construct market sentiment indices, analyze network propagation paths of policy texts, or explore correlations between media coverage and asset price volatility.
- Industry Insights and Policy Evaluation: Offers panoramic observations of industry content production for financial media and research institutions. Simultaneously, provides objective data support for regulatory authorities and policymakers to assess the dissemination order of financial information and monitor market interpretations and feedback following policy releases.
CnOpenData's Chinese Financial News WeChat Public Account Data features core characteristics including domain verticality, content completeness, and temporal coherence. It systematically collects textual and media data from key financial information sources, filling a gap in quantitative research on market sentiment and information propagation based on new media platforms. This dataset not only establishes a robust foundation for text mining and computational communication research in academia but also delivers reliable alternative data support for financial institutions' public opinion monitoring, risk early-warning, trend analysis, and investment decision-making!
Time Coverage
As of 2024 (updatable as needed)
Field Display
Sample Data
相关文献
- 王晓宇,朱菲菲,杨云红，2025：《 “市场发现”与“信息监督”：自媒体在资本市场中的功能》，《经济研究》第10期。
数据更新频率
年度更新