Geo-Tagged Social Media Data-Based Analytical Approach for Perceiving Impacts of Social Events

Abstract

Studying the impact of social events is important for the sustainable development of society. Given the growing popularity of social media applications, social sensing networks with users acting as smart social sensors provide a unique channel for understanding social events. Current research on social events through geo-tagged social media is mainly focused on the extraction of information about when, where, and what happened, i.e., event detection. There is a trend towards the machine learning of more complex events from even larger input data. This research work will undoubtedly lead to a better understanding of big geo-data. In this study, however, we start from known or detected events, raising further questions on how they happened, how they affect people’s lives, and for how long. By combining machine learning, natural language processing, and visualization methods in a generic analytical framework, we attempt to interpret the impact of known social events from the dimensions of time, space, and semantics based on geo-tagged social media data. The whole analysis process consists of four parts: (1) preprocessing; (2) extraction of event-related information; (3) analysis of event impact; and (4) visualization. We conducted a case study on the “2014 Shanghai Stampede” event on the basis of Chinese Sina Weibo data. The results are visualized in various ways, thus ensuring the feasibility and effectiveness of our proposed framework. Both the methods and the case study can serve as decision references for situational awareness and city management.

Using Social Media to Mine and Analyze Public Sentiment during a Disaster: A Case Study of the 2018 Shouguang City Flood in China

“…The results of the sentiment identification quantitatively provided more qualitatively rich human-centric information to be used for disaster decision-making than previous studies. For example, Wang investigated seven topics by identifying and clustering important terms [20], whereas Zhu identified public sentiments from Weibo texts as positive, neutral, or negative [7]. In contrast, this study explicitly divided public sentiments during the flood into nine concrete sentiments.…”

“…To reduce the impact of natural disasters on humanity, disaster management requires more human-centric information in addition to objective disaster information. Since disaster management demands a large amount of information in the face of low availability [3], social media (e.g., Twitter, Facebook, or Sina-Weibo) is an additional information source that is gaining increasing attention from geographic information scientists and disaster researchers [4][5][6] is not only a platform for sharing people's personal lives but can also be used to examine public opinion and perceptions, which may be comparable to the public comments collected by traditional approaches(e.g., questionnaires) [7][8][9]. Combined with spatial-temporal information collected from social media, the public opinions and feelings on a disaster mined from social media can assist government decision-making and help people better understand the state of disaster events [8,10].…”

“…Wang divided Weibo texts about the 2012 Beijing Rainstorm into five topics to investigate the distribution pattern of Weibo [22]. Zhu identified public sentimental attitudes on the "2014 Shanghai Stampede" as either positive, neutral, or negative from geo-tagged Weibo [7]. However, the extant literature on mining topics from disaster-relevant social media texts only extracts important terms or roughly divides the content into different topics and does not identify public opinions in depth.…”

Social Sensing of the Imbalance of Urban and Regional Development in China Through the Population Migration Network around Spring Festival

“…Crowd-sourced geographical data (e.g., mobile check-in data, cellular signaling data, and taxi trajectory data) are rich in information, low cost, and abundant [14]. Such data have been widely used in sensing the geographical environment [15], recognizing urban structure and functional areas [16], planning urban development [17], assisting sustainable economic development [18], perceiving geographical events [19,20], and crowdmapping [21]. Therefore, social sensing based on crowd-sourced geographical data provides a practical approach to explore the spatial behavior of the public and reveal geographical features of the socioeconomy [22].…”

The role of crowdsourcing and social media in crisis mapping: a case study of a wildfire reaching Croatian City of Split

“…Crowdsourcing engages communities around the world in emergency response and disaster management for natural hazards: Fires and wildfires (Becken and Hughey, 2013;Daly and Thom, 2016;De Longueville et al, 2009;Nayebi et al, 2017), earthquakes (Alexander, 2014;Han and Wang, 2019;Hewitt, 2014;Xu and Nyerges, 2017;Xu et al, 2013;Zook et al, 2010), and floods (Begg et al, 2015;Bird et al, 2012;Chan, 2015;Copernicus EMS, 2018;Eilander et al, 2016;Hossain, 2020;Merz et al, 2010;Schanze, 2006;Tingsanchali, 2012). A variety of theories and practical implementations have been developed, which differ in the following areas: technical background and data collection from social networks (Ryabchenko et al, 2016;Xu et al, 2015), classification of social media messages (Mitigation, Prevention, Response and Recovery) (Xiao et al, 2015), analytical models from various sources such as videos (To et al, 2015), geographic approach to social media analysis to indicate the usefulness of messages (de Albuquerque et al, 2015), real-time data mining tools (Zhong et al, 2016;Zhu et al, 2019) or predictions based on Twitter events belonging to geographic analysis of spatiotemporal Big Data (Shi et al, 2016).…”