Analyzing public demands on China's online government inquiry platform: A BERTopic-Based topic modeling study

Area of Science:

• Social Sciences
• Public Administration
• Computational Social Science

Background:

• Government decision-making can be improved by analyzing public feedback.
• Online platforms facilitate direct public engagement with government services.

Purpose of the Study:

• To analyze public demands using topic modeling on government inquiry data.
• To evaluate the BERTopic algorithm's effectiveness in extracting public concerns.
• To provide insights for enhancing governmental agility and social governance.

Main Methods:

• Web scraping of public letters from Zhejiang Province, China (June 2022-May 2023).
• Descriptive statistical analysis and text mining.
• Topic modeling using the BERTopic algorithm.

Main Results:

• Public demands primarily concern livelihood security and rights protection.
• Public responses indicate sensitivity and deep concern regarding emergency events.
• The BERTopic algorithm effectively extracts diverse public demands.

Conclusions:

• Public demands provide critical insights for government emergency management.
• Leveraging topic modeling enhances understanding of public needs and concerns.
• Findings support modernizing social governance and improving public services.