An important component of research in cultural geography involves the exploration and analysis of the laws of regional cultural differences. This topic has considerable significance in the discovery of distinctive cultures, protection of regional cultures, and in-depth understanding of cultural differences. In recent years, with the “spatial turn” of sociology, scholars have focused increasing attention to implicit spatial information in social media data, as well as the social phenomena and laws they reflect. Grasping sociocultural phenomena and their spatial distribution characteristics through texts is an important aspect. Using machine learning methods, such as the popular natural language processing (NLP) approach, this study extracts hotspot cultural elements from text data and accurately detects the spatial interaction patterns of specific cultures, as well as the characteristics of emotions toward non-native cultures. Through NLP, this study examines cultural differences among people from South and North China by analyzing 6128 answers to the question, “What are the differences between South and North China that you ever know?” posted on the Zhihu Q&A platform. Moreover, this study probes individuals’ emotions and cognition of cultural differences between South and North China in three aspects, namely, spatial interaction patterns of hotspot cultural elements, components of hotspot cultures, and emotional characteristics under the influence of cultural differences between the two regions. Results reveal that: (1) people from North and South China exhibit considerable differences in recognizing each other’s culture; (2) among numerous cultural differences, food culture is the most popular; and (3) people tend to have a negative attitude toward food cultures that differ from their own. These factors can shed light on regional cultural differences and help address cultural conflicts. In addition, this study provides effective solutions from a macro perspective, which has been challenging for new cultural geography.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited