Abstract:

[Objective] In the context of big data from mobile internet, social media data with tags such as posting time and location has received widespread attention for its critical role in natural disaster response. In China, research on social attention and online public opinion regarding drought events remains limited, especially for the analysis of spatiotemporal and thematic characteristics of extreme drought events at the river basin scale, with no relevant reports yet. [Methods] This study used the 2022 extreme drought in China’s Yangtze River Basin as a representative case. Utilizing texts from Weibo, a mainstream social media platform in China, as data sources, this study used machine learning and artificial intelligence algorithms to collect Weibo text data throughout the drought progression process. The Latent Dirichlet Allocation (LDA) topic model was employed to perform term clustering and thematic characterization. Through this methodology, an in-depth mining of spatiotemporal and thematic characteristics of drought-related public opinion was conducted, along with sentiment analysis. [Results] (1) The temporal evolution of attention levels on social media was relatively synchronized with the progression of the drought event, with peak drought stage particularly prone to attracting heightened public attention. Across the entire Yangtze River Basin, drought-related discussions on social media remained relatively low in July 2022, rose dramatically in early August, peaked in mid-to-late August, gradually declined in mid-September, and returned to zero in early December. In terms of drought progression, an inverse correlation between the temporal variation characteristics of Weibo discussion level in severely affected provinces and municipalities including Sichuan, Chongqing, and Jiangxi and local hydrological flow data was observed. (2) The spatial characteristics of attention levels on social media basically matched the distribution of drought severity. The proportion of Weibo discussions in high-attention provinces and municipalities (e.g., Sichuan, Chongqing, and Jiangxi) exceeded 50%, reflecting widespread public concern about the drought and indirectly indicating severe socioeconomic impacts caused by the drought in these regions. In contrast, provinces and municipalities such as Yunnan, Tibet, Shanghai, and Qinghai showed relatively low levels of Weibo discussions. (3) The thematic characteristics of drought-related content on social media showed significant regional differences, with public attention levels being closely related to the severity of drought impacts. In Jiangxi and Hunan, key terms related to the drought were “shrinking of Poyang Lake” and “declining water levels” In Sichuan and Chongqing, key terms were secondary disasters such as “wildfires”, “earthquakes”, as well as drought-induced issues such as “reduced crop production by farmers” and “electricity supply shortages”. Other provinces primarily focused on “continuous high-temperature weather” and “meteorological drought”. As the drought progressed, the sentiment of public opinion on drought gradually transitioned from negative to positive. [Conclusion] Weibo texts serve as an effective data source for online public opinion analysis of sudden-onset disasters. The research findings can provide technical support for drought tracking analysis and mobilization efforts of the public for drought relief in river basins.

Key words: Weibo posts on internet, Yangtze River Basin, level of attention on drought, spatiotemporal characteristics, data mining