Social media sentiment exposure to climate-induced floods and risk responses to the 2022 flood in Pakistan

Floods pose a major socio-economic and environmental challenge in Pakistan, where climate-induced extremes have increasingly heightened the vulnerability of both urban and rural systems. Traditional post-disaster surveys often fail to capture the dynamic emotional and behavioral responses of affected communities in real time. This study employs an interdisciplinary social-media based analytical framework to examine public sentiment, thematic narratives, and risk responses during the 2022 Pakistan floods. A dataset of 156,842 Twitter posts was collected, of which 148,536 tweets remained after preprocessing. Sentiment analysis, Latent Dirichlet Allocation (LDA) topic modeling, and spatial visualization were applied to analyze public discourse. The results indicate that 58% of the tweets expressed negative sentiment, primarily related to loss, governmental criticism, and infrastructure failure, while 15% reflected positive sentiment associated with solidarity and humanitarian relief, and 27% were neutral, focusing mainly on information dissemination. Topic modeling identified eight dominant themes, with relief efforts (32%), property damage (27%), and recovery campaigns (21%) collectively accounting for 80% of the discourse. Spatial analysis revealed that discussion intensity was concentrated in the most flood-affected provinces, including Sindh, Punjab, and Khyber Pakhtunkhwa. By integrating textual and spatial analytics, the study provides insights into community-level perceptions and risk responses during climate-induced flood events. The findings demonstrate the potential of social media data as a complementary tool for disaster assessment, early situational awareness, and flood-risk analysis in climate-prone regions.