Social media is a rich data source for analyzing the social impact of hazard processes and human behavior in disaster situations; it is used by rescue agencies for coordination and by local governments for the distribution of official information. In this paper, we propose a method for data mining in Twitter to retrieve messages related to an event. We describe an automated process for the collection of hashtags highly related to the event and specific only to it. We compare our method with existing keyword-based methods and prove that hashtags are good markers for the separation of similar, simultaneous incidents; therefore, the retrieved messages have higher relevancy. The method uses disaster databases to find the location of an event and to estimate the impact area. The proposed method can also be adapted to retrieve messages about other types of events with a known location, such as riots, festivals and exhibitions.

Purpose: The purpose of this study is to propose a method to retrieve data on an event based on a preliminary collection of event-specific hashtags. Design/methodology/approach: Extra knowledge, or a list of events with recorded features that can be used to characterize an event and separate it from other simultaneously occurring social phenomena, is employed. The first step involves the estimation and use of the impact area to retrieve messages from Twitter. This is followed by an extraction of hashtags from these messages. After that, the noisy hashtags would be filtered out by some heuristic rules. Finally, hashtags are used to collect relevant messages from not only Twitter but also other social media platforms. Findings: The proposed method has high selectivity and is able to collect distinct sets of hashtags even for similar simultaneous events. In addition, spatial and temporal features are sufficient to improve collecting information of disaster events. Originality/value: This work discusses a method of information retrieval of an event from cross-platform social media. The proposed method can be applied to other studies of geographically related events.

The Aral Sea is an indispensable component of the socio-economic progress of Central Asia but has undergone substantial ecological transformations over the last few decades, primarily due to global warming and human activities. Among these changes, the basin area has decreased, and water levels have dropped. This paper focuses on a comprehensive analysis of the spatial variation of key climate parameters, such as temperature, precipitation, and potential evapotranspiration over the Aral Sea. Moreover, we examined the transformation of seasonal water areas in the Aral Sea during the growing and non-growing seasons between 2002 and 2017 and the influence of climate and human factors on these changes using Landsat satellite data. Our results indicate that the western section of the Aral Sea has experienced a reduction in water area by 2.41 km² and 1.83 km² during the warm (R² = 0.789) and cold (R² = 0.744) seasons, respectively, over the investigated period. The decrease in lake water volume during the warm season can be attributed to local climate variations, as a strong negative correlation exists between seasonal water storage change and temperature (potential evapotranspiration). The correlation analysis shows that the water change in the northern part of the Aral Sea during the growing season has a significant positive correlation with temperature (R = 0.52) and an insignificant negative correlation with precipitation (R = −0.22). On the contrary, in the west and east parts of the Aral Sea, there is a significant negative correlation with temperature (R = −0.71 and −0.62) and a high positive correlation with precipitation (R = 0.71 and 0.55) during the growing season.

Social media has been a vital channel for communicating and broadcasting disaster-related information. However, the global spatiotemporal patterns of social media users’ activities, interactions, and connections after a natural disaster remain unclear. Hence, we integrated geocoding, geovisualization, and complex network methods to illustrate and analyze the online social network’s spatiotemporal evolution. Taking the super typhoon Haiyan as a case, we constructed a retweeting network and mapped this network according to the tweets’ location information. The results show that (1) the distribution of in-degree and out-degree follow power-law and retweeting networks are scale-free. (2) A local catastrophe could attract significant global interest but with strong geographical heterogeneity. The super typhoon Haiyan especially attracted attention from the United States, Europe, and Australia, in which users are more active in posting and forwarding disaster-related tweets than other regions (except the Philippines). (3) The users’ interactions and connections are also significantly different between countries and regions. Connections and interactions between the Philippines and the United States, Europe, and Australia were much closer than in other regions. Therefore, the agencies and platforms should also pay attention to other countries and regions outside the disaster area to provide more valuable information for the local people.

The Gravity Recovery and Climate Experiment (GRACE) mission, operational from 2002 to 2017, provided critical measurements of Earth’s gravity field anomalies which have been extensively used to study groundwater and terrestrial water storage (TWS) dynamics. In this research, we utilize GRACE data to identify, model, and analyze potential climate parameters contributing to the reconstruction of TWS variability in the Aral Sea Basin region (ASB). We assess the impact of climate change and anthropogenic nature management on TWS change using a quantitative method. Our analysis reveals a significant decline in the TWS at a rate of 0.44 cm year⁻¹ during the 2005–2009 period, primarily attributed to the prevailing drought conditions in the region. Notably, the estimated impact of anthropogenic influence on TWS during the same period of −1.39 cm year⁻¹ is higher than the influence of climatic variables, indicating that anthropogenic activity was the dominant factor in water resource depletion. In contrast, we observed an increase in TWS at a rate of 0.82 cm year⁻¹ during the 2013–2017 period, which can be attributed to the implementation of more effective water resource management practices in the ASB.