In my talk, I delved into the consequences of the COVID-19 pandemic, underscoring the negative impact on mental health and the economic hardships faced, especially in the context of informal economies such as those in Indonesia. These challenges manifested in public sentiment as fear and frustration, a trend we noticed through an analysis of social media posts during lockdowns. As people turned to social media for communication and information during lockdowns, it created new opportunities for data analysis. Here, I introduced the application of Social Network Analysis (SNA), an approach that can elucidate the interconnectedness and dynamics within a community. To make this analysis more practical, my talk incorporated machine learning techniques to scrutinize social media posts. Concentrating on Instagram, I dissected the popular hashtag #dirumahsaja (stay at home), which became a symbol of public sentiment during the pandemic and lockdown.