Using geotagged tweets to track population movements to and from Puerto Rico after Hurricane Maria

Martín, Yago; Cutter, Susan L.; Li, Zhenlong; Emrich, Christopher T.; Mitchell, Jerry T.

doi:10.1007/s11111-020-00338-6

Cited by 64 publications

(37 citation statements)

References 76 publications

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“…(Twitter, 2020) All tweets were geotagged (embedded with a geolocation) within the continental United States. Following our previous work, (Martin, Cutter, Li, Emrich, & Mitchell, 2020) we filtered out tweets automatically posted by bots such as weather reports and job offers by checking from which application a tweet was posted (the source of a tweet). After data cleaning, 297,354,262 geotagged tweets posted by over 3,520,692 Twitter users were left for further analysis to identify tweets relevant to the research aims.…”

Section: Methodsmentioning

confidence: 99%

Risk perception of COVID-19 and its socioeconomic correlates in the United States: A social media analysis

Qiao

Liang

et al. 2021

Preprint

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Social media analysis provides a new approach to monitoring and understanding risk perceptions regarding COVID-19 over time. Our current understandings of risk perceptions regarding COVID-19 do not disentangle the three dimensions of risk perceptions (perceived susceptibility, perceived severity, and negative emotion) over a long enough timeframe to cover different pandemic phases. The impact of social determinants of health factors on COVID-19-related risk perceptions over time is also not clear. To address these two knowledge gaps, we extracted tweets regarding COVID-19-related risk perceptions and developed index indicators for three dimensions of risk perceptions based on over 297 million geotagged tweets posted by over 3.5 million Twitter users from January to October 2020 in the United States. We also examined correlations between index indicator scores and county-level social determinants of health factors. The three domains of risk perceptions demonstrate different trajectories. Perceived severity kept climbing throughout the whole study period. Perceived susceptibility and negative emotion declined and remained stable at a lower level after peaking on March 11 (WHO named COVID-19 a global pandemic). Attention on risk perceptions was not exactly in accordance with epidemic trends of COVID-19 (cases, deaths). Users from socioeconomically vulnerable counties showed lower attention on perceived severity and susceptibility of COVID-19 than those from wealthier counties. Examination of trends in tweets regarding the multiple domains of risk perceptions throughout stages of the COVID-19 pandemic can help policy makers frame in-time, tailored, and appropriate responses to prevent viral spread and encourage preventive behavior uptake in United States.

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Section: Methodsmentioning

confidence: 99%

Risk perception of COVID-19 and its socioeconomic correlates in the United States: A social media analysis

Qiao

Liang

et al. 2021

Preprint

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“…Geotagged Twitter data have been used in human mobility studies (Martín, Li, and Cutter 2017;Martín et al 2020;Hu, Li, and Ye 2020b). Figure 6 shows the global population flows for six selected weekends and cross-day average daily travel distance derived from geotagged tweets from 1 February 2020, to 31 July 2020 (for the calculation of cross-day distance, please refer to Huang et al 2020).…”

Section: Human Movement Patterns In the Shadow Of Covid-19mentioning

confidence: 99%

Taking the pulse of COVID-19: a spatiotemporal perspective

Yang

Sha

Liu

et al. 2020

International Journal of Digital Earth

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106

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“…The advantages of social media with respect to the aforementioned sources of digital information are that they are extensive (covering large spatial areas), easily accessible, with less privacy concern, and at low cost [25][26][27][28]. Extracting useful information from social media is not new, as the valuable geospatial insights from social media have been explored in a wide range of fields, including hazard mitigation [29][30][31], evacuation monitoring [27,32,33], urban analytics [34][35][36][37], and public health [38,39], to list a few. Despite the existing applications, the potential of human mobility derived from social media data has not been fully explored.…”

Section: Introductionmentioning

confidence: 99%

Twitter reveals human mobility dynamics during the COVID-19 pandemic

et al. 2020

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The current COVID-19 pandemic raises concerns worldwide, leading to serious health, economic, and social challenges. The rapid spread of the virus at a global scale highlights the need for a more harmonized, less privacy-concerning, easily accessible approach to monitoring the human mobility that has proven to be associated with viral transmission. In this study, we analyzed over 580 million tweets worldwide to see how global collaborative efforts in reducing human mobility are reflected from the user-generated information at the global, country, and U.S. state scale. Considering the multifaceted nature of mobility, we propose two types of distance: the single-day distance and the cross-day distance. To quantify the responsiveness in certain geographic regions, we further propose a mobility-based responsive index (MRI) that captures the overall degree of mobility changes within a time window. The results suggest that mobility patterns obtained from Twitter data are amenable to quantitatively reflect the mobility dynamics. Globally, the proposed two distances had greatly deviated from their baselines after March 11, 2020, when WHO declared COVID-19 as a pandemic. The considerably less periodicity after the declaration suggests that the protection measures have obviously affected people’s travel routines. The country scale comparisons reveal the discrepancies in responsiveness, evidenced by the contrasting mobility patterns in different epidemic phases. We find that the triggers of mobility changes correspond well with the national announcements of mitigation measures, proving that Twitter-based mobility implies the effectiveness of those measures. In the U.S., the influence of the COVID-19 pandemic on mobility is distinct. However, the impacts vary substantially among states.

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Using geotagged tweets to track population movements to and from Puerto Rico after Hurricane Maria

Cited by 64 publications

References 76 publications

Risk perception of COVID-19 and its socioeconomic correlates in the United States: A social media analysis

Risk perception of COVID-19 and its socioeconomic correlates in the United States: A social media analysis

Taking the pulse of COVID-19: a spatiotemporal perspective

Twitter reveals human mobility dynamics during the COVID-19 pandemic

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