Combining Social Network Analysis to Explore Rural Textile Consumer Preferences and Communication Paths
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Abstract
In rural areas, textile purchase decisions are strongly influenced by neighborhood reputation, cultural identity, and regional aesthetics and exhibit significant social communication characteristics. Traditional market research methods struggle to capture the dynamic evolution of preferences in rural textile consumption scenarios, and existing social network models often lack the multidimensional integrated modeling capabilities. This paper proposed a multidimensional modeling method that integrates social network analysis, natural language processing and a graph neural network. On the basis of the user behavior logs of rural e-commerce platforms, a weighted social network (3,685 nodes, 19,243 edges) was constructed, and seven consumer groups with geographical affiliation characteristics were identified by combining the improved Bidirectional Encoder Representations from Transformers (BERT) model and the Louvain algorithm. The GAT model was used to integrate historical purchase and sentiment features to achieve preference trend prediction at the community level, and the improved SIR model was introduced to simulate the negative word-of-mouth communication mechanism by integrating node influence and trust parameters. The experimental results show that the model constructed in this paper performs well in terms of preference classification, community division and communication path prediction. The preference emotion classification accuracy is 92.4%, and the modularity index is 0.43. This study provides a theoretical basis and practical support for the modeling of rural market consumption preferences and precise intervention strategies based on community structure.
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