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The COVID-19 pandemic has created complex problems that require organizations to collaborate within and across the sector line. Social media data can provide insights into how nonprofits interact for the pandemic response from both social network and geographical perspectives. This study innovatively investigated the connection and interaction patterns among 74 National Voluntary Organizations Active in Disaster (NVOAD) nonprofits and three government agencies based on structural analyses and content analyses of their Twitter communications during the long-term global COVID-19 pandemic. The daily tweeting quantities of all nonprofits were generally consistent with the pandemic severity in the United States before July 2020 and remained stable afterward. Nonprofits' tweets can reflect their purposes of sharing information, building communities, and taking actions for disaster response. Government agencies played leadership roles in providing COVID-19 guidelines and information. Human services, International and Foreign Affairs, and Public and Societal Benefit nonprofits, especially American Red Cross played central roles in the nonprofit communication network. Possible explanations include the following: (1) Geographically, connections and interactions among nonprofits are more likely to happen within the same city or in neighboring states. (2) Both mission homophily and heterophily contribute to connections and interactions among nonprofits, depending on their subsectors. The findings not only help the public better understand how nonprofits are collaboratively fighting the pandemic, but also provide guidance for nonprofits to plan for better interactions and communications in future disaster response. [ABSTRACT FROM AUTHOR]

In the waves of the ongoing coronavirus disease 2019 (COVID-19) pandemic, each U.S. state governor has been leading efforts to curb the spread of the virus within his or her state. Twitter has been widely used for crisis communications by governors. Their Twitter usage patterns can largely reflect how state governments responded to the COVID-19 crisis from both geographical and social network perspectives. Through spatial–temporal analysis, network analysis, and text mining, we identified several important usage patterns, such as (1) tweet quantities positively correlated with the pandemic severity; (2) most governors started to mention COVID-19 weeks before the first reported case in their states; (3) COVID-19-related hashtags were commonly used to organize information; (4) feedback was frequently provided by at-mentioning or retweeting other accounts; and (5) governors were networked for crisis communication, driven by demographics, pandemic severities, geographic closeness, cooperation, and party affiliations among states. The current usage patterns are generally consistent with the criteria for effective crisis communications on Twitter (listening, informing, feedback, and connections). Some actionable approaches for governors to improve Twitter crisis communications are also discussed. This exploratory study provides a guide for other agencies and officials to develop future crisis communication plans through leveraging social media for social good. [ABSTRACT FROM AUTHOR]

Massive Geo-tagged social media data provide new opportunities for disaster risk assessment, prevention, and management. This article presents a proof of concept for assessing wildfire risk using Geo-tagged social media data, by taking wildfire risk as a function of wildfire hazard and social–ecological vulnerability. The case study of the United States shows that the regions with the highest wildfire hazard are concentrated in the Western, while the most vulnerable areas are mainly distributed in the Eastern, the Western Coast, and the Southern parts of the nation. Areas with high wildfire risk are mainly located in the Northwestern and Southeastern United States. It shows that the wildfire risk level has significant linear relationship with population density. Massive and vulnerable population might result in significant increase in wildfire risk perception. We conclude that Geo-tagged social media data have great potential in disaster risk studies. [ABSTRACT FROM AUTHOR]

It has become increasingly important in spatial equity studies to understand activity spaces – where people conduct regular out-of-home activities. Big data can advance the identification of activity spaces and the understanding of spatial equity. Using the Los Angeles metropolitan area for the case study, this paper employs geotagged Twitter data to delineate activity spaces with two spatial measures: first, the average distance between users' home location and activity locations; and second, the area covered between home and activity locations. The paper also finds significant relationship between the spatial measures of activity spaces and neighborhood spatial and socioeconomic characteristics. This research enriches the literature that aims to address spatial equity in activity spaces and demonstrates the applicability of big data in urban socio-spatial research. [ABSTRACT FROM AUTHOR]

Understanding and detecting the intended meaning in social media is challenging because social media messages contain varieties of noise and chaos that are irrelevant to the themes of interests. For example, conventional supervised classification approaches would produce inconsistent solutions to detecting and clarifying whether any given Twitter message is really about a wildfire event. Consequently, a renovated workflow was designed and implemented. The workflow consists of four sequential procedures: (1) Apply the latent semantic analysis and cosine similarity calculation to examine the similarity between Twitter messages; (2) Apply Affinity Propagation to identify exemplars of Twitter messages; (3) Apply the cosine similarity calculation again to automatically match the exemplars to known training results, and (4) Apply accumulative exemplars to classify Twitter messages using a support vector machine approach. The overall correction ratio was over 90% when a series of ongoing and historical wildfire events were examined. [ABSTRACT FROM AUTHOR]

Housing is among the most pressing issues in urban China and has received considerable scholarly attention. Researchers have primarily concentrated on identifying the factors that influence residential property prices and how such mechanisms function. However, few studies have examined the potential factors that influence housing prices from a big data perspective. In this article, we use a big data perspective to determine the willingness of buyers to pay for various factors. The opinions and geographical preferences of individuals for places can be represented by visit frequencies given different motivations. Check-in data from the social media platform Sina Visitor System is used in this article. Here, we use kernel density estimation (KDE) to analyse the spatial patterns of check-in spots (or places of interest, POIs) and employ the Getis-Ord method to identify the hot spots for different types of POIs in Shenzhen, China. New indexes are then proposed based on the hot-spot results as measured by check-in data to analyse the effects of these locations on housing prices. This modelling is performed using the hedonic price method (HPM) and the geographically weighted regression (GWR) method. The results show that the degree of clustering of POIs has a significant influence on housing values. Meanwhile, the GWR method has a better interpretive capacity than does the HPM because of the former method’s ability to capture spatial heterogeneity. This article integrates big social media data to expand the scope (new study content) and depth (study scale) of housing price research to an unprecedented degree. [ABSTRACT FROM AUTHOR]

Social media platforms have become a critical virtual community where people share information and discuss issues. Their capabilities for fast dissemination and massive participation have placed under scrutiny the way in which they influence people's perceptions over time and space. This paper investigates how El Niño, an extreme recurring weather phenomenon, was discussed on Twitter in the United States from December 2015 to January 2016. A multiple-dimensional analysis, including spatial, social, temporal, and semantic perspectives, is conducted to comprehensively understand Twitter users' discussion of such weather phenomenon. We argue that such multi-dimensional analysis can reveal complicated patterns of Twitter users' online discussion and answers questions that cannot be addressed with a single-dimension analysis. For example, a significant increase in tweets about El Niño was noted when a series of rainstorms inundated California in January 2016. Some discussions on natural disasters were influenced by their geographical distances to the disasters and the prevailing geopolitical environment. The popular tweets generally discussing El Niño were overall negative, while tweets talking about how to prepare for the California rainstorms were more positive. [ABSTRACT FROM AUTHOR]

Social media data is increasingly being used to improve disaster resilience and response. Recent years have seen more efforts to integrate social media feeds with various demographic and socioeconomic variables to gain insight into the geographical and social disparities in social media use surrounding disasters. However, vulnerability concepts and indicators have been largely overlooked despite that they can offer aid in understanding and measuring the communities' sensitivity to natural hazards and their capability of responding to and recovering from disasters. This study addresses a research question: Are vulnerable communities digitally left behind in social responses to natural disasters? Our empirical analysis is based on Hurricane Sandy and is conducted in a pre-disaster setting with spatial regression modeling. We observe that physically vulnerable communities had more intense social responses while socially vulnerable communities were digitally left behind in pre-disaster social responses to Hurricane Sandy. • Twitter activities were used to measure social responses to Hurricane Sandy. • The relationship between vulnerability and online social responses to Hurricane Sandy was examined • Empirical analysis was conducted in a pre-disaster setting with spatial regression modeling. • Physically vulnerable communities had more intense social responses. • Socially vulnerable communities were digitally left behind. [ABSTRACT FROM AUTHOR]