Speaker
Description
While urban areas remain home to most of the global population, the countryside is increasingly becoming a preferred place of residence, even in some developing nations such as China. The potential benefits and challenges for rural development posed by this counter-urbanisation trend make it essential to monitor its extend and progression. However, In China, statistical data on this phenomenon remains scarce due to the restrictions on rural property ownership by outsiders, who can only rent properties from individual rural resident, leaving no formal record. This research uses the data from the Chinese social media platform Xiaohongshu (or REDnote, similar to Instagram), where specific users share their experience renting second homes in or returning to countryside. A web crawler was developed to collect all these ‘big data’, which was subsequently cleaned and processed by ChatGPT AI model. To ensure credibility, partial human validation was also applied to the AI-processed results. Despite certain limitations, the findings provide insights into counterurbanisation trend in China, highlighting its widespread fact and identifying several hot spots area. This study demonstrates that combining social media big data with AI processing offers an effective and timely way to identify human migration flows between urban and rural areas, enabling valuable reference for policymakers and the real estate sector.
| Keywords | Big data; ChatGPT; counterurbanisation; China |
|---|---|
| Best Congress Paper Award | No |
