Following the implementation of new tariff policies after the January 20, 2025, U.S. presidential inauguration, this research examines how shared grievances emerge and form networks in social media discourse. The fundamental premise underlying this study is that people experiencing similar concerns naturally gravitate toward one another, forming clusters of shared understanding that develop into coherent grievance networks. Using data collected from January 20 to August 1, 2025, we employed clustering algorithms and large language models within a multistage analytical framework to investigate this phenomenon. Keywords were categorized into consumer grievances and economic grievances based on expert consultation and news analysis, followed by LLM and Network Analysis. Our findings demonstrate that shared grievances create different network formations with varying temporal activation patterns, providing empirical evidence that individuals with similar tariff-related grievances form coherent communication communities. Consumer grievances exhibited broader, overlapping communities, while economic grievances formed more specialized clusters. Complementary temporal analysis revealed coordinated activation periods, suggesting potential precursors to collective action. This research contributes to understanding pre-mobilization phases of social movement development and offers practical tools for early detection of emerging collective action.