Identification of Spatial Conflicts between Tourists and Local Residents: Based on “Nanjing Travel Pitfall Avoidance” Posts on Rednote

Abstract

This study identifies spatial conflicts between tourists and local residents in Nanjing using Rednote “Nanjing Travel Pitfalls” posts, applying AI-based natural language processing and spatial analysis. Findings show: (1) Conflicts are “localised concentration and overall dispersion”, with high-intensity zones in multifunctional areas like Laomendong and Xinjiekou; (2) Conflicts fall into three types: High-Intensity Clustered Type, Intersecting and Compressed Type, and Low-Frequency, High-Sensitivity Type; (3) Residents’ perceptions focus on old town living spaces, tourists’ are more dispersed, yet both align on key areas; (4) Social media amplifies conflicts via algorithmic recommendation and emotional contagion. The study suggests shifting from conflict control to spatial regeneration, promoting coexistence through time-sharing and community negotiation. It contributes theoretical and methodological insights to digital-era urban tourism governance.