The lack of community-relevant flood informational resources and tools often results in inadequate and divergent understandings of flood risk and can impede communities' ability to function cohesively in the face of increasing flood threats. The current study reports on a set of workshops that the authors conducted with various groups (citizens, city engineers and planners, realtors and builders, and media representatives) within a flood prone community to evaluate how novel hydroinformatic tools that include hydrodynamic modeling, geospatial visualization, and socioeconomic analysis can enhance understanding of flood risk and engagement in flood risk mitigation among diverse community members. The workshops were designed to help identify stakeholder preferences regarding key functionality needed for integrated hydroinformatic technologies and socioeconomic analyses for flood risk reduction. Workshop participants were asked to use and comment on examples of prototype flood risk informational tools, such as: (1) flood damage estimation tool, (2) drivability and emergency accessibility tool, and (3) community-scale social and economic metrics tool. Data gathered from workshops were analyzed using qualitative analysis based on a grounded-theory approach. Data were coded by hand based on themes identified by the research team and incorporated deviant case analysis to ensure minority opinions was represented. The study results are focused on the following main themes and how flood tools can address them: (1) improving the understanding of flood risk and engagement in flood risk mitigation, (2) reducing the gap between individual and community risk, (3) challenges in communicating flood risk information, (4) enhancing relevance to and engagement of the community, and (5) enabling actionable information. Our research demonstrates the need for community-anchored tools and technologies that can illustrate local context, include local historical and simulated events at multiple levels of community impact, enable analyses by flood professionals while also providing simplified tools of use by citizens, and allow individuals to expand their knowledge beyond their homes, businesses, and places of work.
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Tipping the scales: how geographical scale affects the interpretation of social media behavior in crisis research
Our relationship with technology is constantly evolving, and how we use technology in
disasters has evolved even faster. Understanding how to utilize human interactions with
technology and the limitations of those interactions will be a crucial building block to contextualizing
crisis data. The impact of geographic scale on behavioral change analyses is an
unexplored facet of our ability to identify relative severities of crisis situations, magnitudes
of localized crises, and total durations of disaster impacts. Within this paper, we aggregate
Twitter and hurricane damage data across a wide range of geographic scales and assess the
impact of increasing scale on both the recognition of extreme behaviors and the correlation
between activity and damage. The power-law relationships identified between many
of these variables indicate a direct, definable scalar dependence of social media aggregation
analyses, and these relationships can be used to inform more intelligent, equitable, and
actionable social media usage in emergency response.
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- Award ID(s):
- 1837021
- NSF-PAR ID:
- 10467704
- Publisher / Repository:
- Springer Nature
- Date Published:
- Journal Name:
- Natural Hazards
- Volume:
- 112
- Issue:
- 1
- ISSN:
- 0921-030X
- Page Range / eLocation ID:
- 545 to 564
- Subject(s) / Keyword(s):
- ["Crisis response","Crisis informatics","Social media","Vulnerability","Hurricanes"]
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)Background The COVID-19 pandemic has caused several disruptions in personal and collective lives worldwide. The uncertainties surrounding the pandemic have also led to multifaceted mental health concerns, which can be exacerbated with precautionary measures such as social distancing and self-quarantining, as well as societal impacts such as economic downturn and job loss. Despite noting this as a “mental health tsunami”, the psychological effects of the COVID-19 crisis remain unexplored at scale. Consequently, public health stakeholders are currently limited in identifying ways to provide timely and tailored support during these circumstances. Objective Our study aims to provide insights regarding people’s psychosocial concerns during the COVID-19 pandemic by leveraging social media data. We aim to study the temporal and linguistic changes in symptomatic mental health and support expressions in the pandemic context. Methods We obtained about 60 million Twitter streaming posts originating from the United States from March 24 to May 24, 2020, and compared these with about 40 million posts from a comparable period in 2019 to attribute the effect of COVID-19 on people’s social media self-disclosure. Using these data sets, we studied people’s self-disclosure on social media in terms of symptomatic mental health concerns and expressions of support. We employed transfer learning classifiers that identified the social media language indicative of mental health outcomes (anxiety, depression, stress, and suicidal ideation) and support (emotional and informational support). We then examined the changes in psychosocial expressions over time and language, comparing the 2020 and 2019 data sets. Results We found that all of the examined psychosocial expressions have significantly increased during the COVID-19 crisis—mental health symptomatic expressions have increased by about 14%, and support expressions have increased by about 5%, both thematically related to COVID-19. We also observed a steady decline and eventual plateauing in these expressions during the COVID-19 pandemic, which may have been due to habituation or due to supportive policy measures enacted during this period. Our language analyses highlighted that people express concerns that are specific to and contextually related to the COVID-19 crisis. Conclusions We studied the psychosocial effects of the COVID-19 crisis by using social media data from 2020, finding that people’s mental health symptomatic and support expressions significantly increased during the COVID-19 period as compared to similar data from 2019. However, this effect gradually lessened over time, suggesting that people adapted to the circumstances and their “new normal.” Our linguistic analyses revealed that people expressed mental health concerns regarding personal and professional challenges, health care and precautionary measures, and pandemic-related awareness. This study shows the potential to provide insights to mental health care and stakeholders and policy makers in planning and implementing measures to mitigate mental health risks amid the health crisis.more » « less
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Abstract Susceptibility to infectious diseases such as COVID-19 depends on how those diseases spread. Many studies have examined the decrease in COVID-19 spread due to reduction in travel. However, less is known about how much functional geographic regions, which capture natural movements and social interactions, limit the spread of COVID-19. To determine boundaries between functional regions, we apply community-detection algorithms to large networks of mobility and social-media connections to construct geographic regions that reflect natural human movement and relationships at the county level in the coterminous United States. We measure COVID-19 case counts, case rates, and case-rate variations across adjacent counties and examine how often COVID-19 crosses the boundaries of these functional regions. We find that regions that we construct using GPS-trace networks and especially commute networks have the lowest COVID-19 case rates along the boundaries, so these regions may reflect natural partitions in COVID-19 transmission. Conversely, regions that we construct from geolocated Facebook friendships and Twitter connections yield less effective partitions. Our analysis reveals that regions that are derived from movement flows are more appropriate geographic units than states for making policy decisions about opening areas for activity, assessing vulnerability of populations, and allocating resources. Our insights are also relevant for policy decisions and public messaging in future emergency situations.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s11069-021-05193-8
