Spatial biases in crowdsourced data: Social media content attention concentrates on populous areas in disasters

Fan, Chao; Esparza, Miguel; Dargin, Jennifer; Wu, Fangsheng; Oztekin, Bora; Mostafavi, Ali

doi:10.1016/j.compenvurbsys.2020.101514

Cited by 54 publications

(33 citation statements)

References 29 publications

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“…Understanding where the information has been posted and analyzing related contents created in a particular time window can provide a valuable resource to feed into early warning systems [28]. Multiple efforts capitalize on the analysis of textual content, information dissemination, or sentiment patterns to signal early warning for an emergent event [29]- [31]. A typical example tracked the sentiment changes based on users' geolocation information, aiming to facilitate a more robust early warning system for hurricane disasters [29].…”

Section: B Social Media In Early Warning and Disease Surveillancementioning

confidence: 99%

Can Social Media Data Be Utilized to Enhance Early Warning: Retrospective Analysis of the U.S. Covid-19 Pandemic

Gao

Zhou

et al. 2021

Preprint

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The U.S. needs early warning systems to help it contain the spread of infectious diseases. Conventional early warning systems use lab-test results or dynamic records to signal early warning signs. New early warning systems can supplement these data with indicators of public awareness like news articles and search queries. This study aims to explore the potential of utilizing social media data to enhance early warning of the COVID-19 outbreak. To demonstrate the feasibility, this study conducts a retrospective analysis and investigates more than 14 million related Twitter postings in the date range from January 20 to March 10, 2020. With the aid of natural language processing tools and machine learning classifiers, this study classifies each of these tweets into either a signal or a non-signal. In this study, a 'signal' tweet implies that the user recognized the COVID-19 outbreak risk in the U.S. This study then proposes a parameter 'signal ratio' to signal warning signs of the COVID-19 pandemic over periods. Results reveal that social media data and the signal ratio can detect the hazards ahead of the COVID-19 outbreak. This claim has been validated with a leading time of 16 days through the comparison to other referenced methods based on Google trends or media news.

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Section: B Social Media In Early Warning and Disease Surveillancementioning

confidence: 99%

Can Social Media Data Be Utilized to Enhance Early Warning: Retrospective Analysis of the U.S. Covid-19 Pandemic

Gao

Zhou

et al. 2021

Preprint

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“…Such disparities could have a serious impact on emergency management and disaster resilience [35]. The findings of disparities in social media use can inform emergency managers and public officials to effectively use social media data for resource allocation, action prioritization [41], and public opinion response.…”

Section: Disparities In Social Mediamentioning

confidence: 99%

Temporal, Spatial, and Socioeconomic Dynamics in Social Media Thematic Emphases during Typhoon Mangkhut

Zhu

Liu

2021

Sustainability

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Disaster-related social media data often consist of several themes, and each theme allows people to understand and communicate from a certain perspective. It is necessary to take into consideration the dynamics of thematic emphases on social media in order to understand the nature of such data and to use them appropriately. This paper proposes a framework to analyze the temporal, spatial, and socioeconomic disparities in thematic emphases on social media during Typhoon Mangkhut. First, the themes were identified through a latent Dirichlet allocation model during Typhoon Mangkhut. Then, we adopted a quantitative method of indexing the themes to represent the dynamics of the thematic emphases. Spearman correlation analyses between the index and eight socioeconomic variables were conducted to identify the socioeconomic disparities in thematic emphases. The main research findings are revealing. From the perspective of time evolution, Theme 1 (general response) and Theme 2 (urban transportation) hold the principal position throughout the disaster. In the early hours of the disaster, Theme 3 (typhoon status and impact) was the most popular theme, but its popularity fell sharply soon after. From the perspective of spatial distribution, people in severely affected areas were more concerned about urban transportation (Theme 2), while people in moderately affected areas were more concerned about typhoon status and impact (Theme 3) and animals and humorous news (Theme 4). The results of the correlation analyses show that there are differences in thematic emphases across disparate socioeconomic groups. Women preferred to post about typhoon status and impact (Theme 3) and animals and humorous news (Theme 4), while people with higher income paid less attention to these two themes during Typhoon Mangkhut. These findings can help government agencies and other stakeholders address public needs effectively and accurately in disaster responses.

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“…Social media data, however, can overlook certain demographic groups based on user preferences [ 44 ]. In addition, it can be difficult to track the dynamic spatial behaviours of users, as only 1–2% of Twitter data contains location information [ 44 ]. Considering these limitations of survey and social media data, this study has elected to incorporate a different and relatively new approach to quantifying data for community resilience by building upon the current knowledgebase of the accessibility and need for essential services during the disaster setting.…”

Section: Introductionmentioning

confidence: 99%

“…Social media data have been applied to capturing societal disruptions [35], conducting rapid damage assessment [40][41][42], and sensing the dynamic situation of infrastructure services [43]. Social media data, however, can overlook certain demographic groups based on user preferences [44]. In addition, it can be difficult to track the dynamic spatial behaviours of users, as only 1-2% of Twitter data contains location information [44].…”

Section: Introductionmentioning

confidence: 99%

Quantifying community resilience based on fluctuations in visits to points-of-interest derived from digital trace data

Podesta

Coleman

Esmalian

et al. 2021

J. R. Soc. Interface.

Self Cite

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This research establishes a methodological framework for quantifying community resilience based on fluctuations in a population's activity during a natural disaster. Visits to points-of-interests (POIs) over time serve as a proxy for activities to capture the combined effects of perturbations in lifestyles, the built environment and the status of business. This study used digital trace data related to unique visits to POIs in the Houston metropolitan area during Hurricane Harvey in 2017. Resilience metrics in the form of systemic impact, duration of impact, and general resilience (GR) values were examined for the region along with their spatial distributions. The results show that certain categories, such as religious organizations and building material and supplies dealers had better resilience metrics—low systemic impact, short duration of impact, and high GR. Other categories such as medical facilities and entertainment had worse resilience metrics—high systemic impact, long duration of impact and low GR. Spatial analyses revealed that areas in the community with lower levels of resilience metrics also experienced extensive flooding. This insight demonstrates the validity of the approach proposed in this study for quantifying and analysing data for community resilience patterns using digital trace/location-intelligence data related to population activities. While this study focused on the Houston metropolitan area and only analysed one natural hazard, the same approach could be applied to other communities and disaster contexts. Such resilience metrics bring valuable insight into prioritizing resource allocation in the recovery process.

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Spatial biases in crowdsourced data: Social media content attention concentrates on populous areas in disasters

Cited by 54 publications

References 29 publications

Can Social Media Data Be Utilized to Enhance Early Warning: Retrospective Analysis of the U.S. Covid-19 Pandemic

Can Social Media Data Be Utilized to Enhance Early Warning: Retrospective Analysis of the U.S. Covid-19 Pandemic

Temporal, Spatial, and Socioeconomic Dynamics in Social Media Thematic Emphases during Typhoon Mangkhut

Quantifying community resilience based on fluctuations in visits to points-of-interest derived from digital trace data

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