Social and geographical disparities in Twitter use during Hurricane Harvey

ABSTRACT Social media such as Twitter is increasingly being used as an effective platform to observe human behaviors in disastrous events. However, uneven social media use among different groups of population in different regions could lead to biased consequences and affect disaster resilience. This paper studies the Twitter use during 2017 Hurricane Harvey in 76 counties in Texas and Louisiana. We seek to answer a fundamental question: did social-geographical disparities of Twitter use exist during the three phases of emergency management (preparedness, response, recovery)? We employed a Twitter data mining framework to process the data and calculate two indexes: Ratio and Sentiment. Regression analyses between the Ratio indexes and the social-geographical characteristics of the counties at the three phrases reveal significant social and geographical disparities in Twitter use during Hurricane Harvey. Communities with higher disaster-related Twitter use in Harvey generally were communities having better social and geographical conditions. These results of Twitter use patterns can be used to compare with future similar studies to see whether the Twitter use disparities have increased or decreased. Future research is also needed to examine the effects of Twitter use disparities on disaster resilience and to test whether Twitter use can predict community resilience.

[1]  J. Fowler,et al.  Rapid assessment of disaster damage using social media activity , 2016, Science Advances.

[2]  Lei Zou,et al.  Evaluating Land Subsidence Rates and Their Implications for Land Loss in the Lower Mississippi River Basin , 2015 .

[3]  W. Pothisiri ePubWU Institutional Repository , 2017 .

[4]  M. Goodchild,et al.  Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr , 2013 .

[5]  Ioana Popescu,et al.  Citizen observations contributing to flood modelling: opportunities and challenges , 2017 .

[6]  Helbert Arenas,et al.  Mapping and assessing coastal resilience in the Caribbean region , 2015 .

[7]  M. Tsou,et al.  Research challenges and opportunities in mapping social media and Big Data , 2015 .

[8]  M. Borowitz 7 US National Oceanic and Atmospheric Administration , 2017 .

[9]  Ming-Hsiang Tsou,et al.  Visualization of social media: seeing a mirage or a message? , 2013 .

[10]  M. Zoback,et al.  Disaster Resilience: A National Imperative , 2013 .

[11]  S. Cutter,et al.  Leveraging Twitter to gauge evacuation compliance: Spatiotemporal analysis of Hurricane Matthew , 2017, PloS one.

[12]  Ângela Guimarães Pereira,et al.  Building a resilient community through social network: Ethical considerations about the 2011 Genoa floods , 2014, ISCRAM.

[13]  Qi Wang,et al.  Quantifying Human Mobility Perturbation and Resilience in Hurricane Sandy , 2014, PloS one.

[14]  Jie Yin,et al.  Using Social Media to Enhance Emergency Situation Awareness , 2012, IEEE Intelligent Systems.

[15]  Matthew Zook,et al.  Mapping the Data Shadows of Hurricane Sandy: Uncovering the Sociospatial Dimensions of ‘Big Data’ , 2014 .

[16]  Pascal Van Hentenryck,et al.  Performance of Social Network Sensors during Hurricane Sandy , 2014, PloS one.

[17]  B. Lindsay Social Media and Disasters: Current Uses, Future Options, and Policy Considerations , 2011 .

[18]  Bandana Kar,et al.  Assessing relevance of tweets for risk communication , 2018, Int. J. Digit. Earth.

[19]  N. Lam,et al.  Measuring Community Resilience to Coastal Hazards along the Northern Gulf of Mexico. , 2016, Natural hazards review.

[20]  Jyoti Ramteke,et al.  Election result prediction using Twitter sentiment analysis , 2016, 2016 International Conference on Inventive Computation Technologies (ICICT).

[21]  B. Merz,et al.  Coping with floods: preparedness, response and recovery of flood-affected residents in Germany in 2002 , 2007 .

[22]  Lei Zou,et al.  Mining Twitter Data for Improved Understanding of Disaster Resilience , 2018 .

[23]  Lei Zou,et al.  A cyberinfrastructure for community resilience assessment and visualization , 2015 .

[24]  A. Culotta,et al.  A Demographic Analysis of Online Sentiment during Hurricane Irene , 2012 .

[25]  Eric Gilbert,et al.  VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text , 2014, ICWSM.

[26]  Lei Zou,et al.  Assessing Community Resilience to Coastal Hazards in the Lower Mississippi River Basin , 2016 .

[27]  Osmar R. Zaïane,et al.  Sentiment Analysis of Breast Cancer Screening in the United States using Twitter , 2016, KDIR.

[28]  N. Dufty Using social media to build community disaster resilience , 2012 .

[29]  A. Kirilenko,et al.  People as sensors: Mass media and local temperature influence climate change discussion on Twitter , 2014 .

[30]  Maximilian Walther,et al.  Geo-spatial Event Detection in the Twitter Stream , 2013, ECIR.

[31]  R. Merchant,et al.  Integrating social media into emergency-preparedness efforts. , 2011, The New England journal of medicine.

[32]  Xiangyang Guan,et al.  Using social media data to understand and assess disasters , 2014, Natural Hazards.

[33]  Diansheng Guo,et al.  A novel approach to leveraging social media for rapid flood mapping: a case study of the 2015 South Carolina floods , 2018 .

[34]  Martinho Guimaraes Pires Pereira Angela,et al.  Building a resilient community through social network: ethical considerations about the 2011 Genoa floods , 2014 .

[35]  S. Singh Evaluating two freely available geocoding tools for geographical inconsistencies and geocoding errors , 2017, Open Geospatial Data, Software and Standards.

[36]  Jaishree Beedasy,et al.  Long-term Recovery From Hurricane Sandy: Evidence From a Survey in New York City , 2017, Disaster Medicine and Public Health Preparedness.

[37]  Lei Zou,et al.  Community Resilience to Drought Hazard in the South-Central United States , 2018 .

[38]  Christopher M. Danforth,et al.  Climate Change Sentiment on Twitter: An Unsolicited Public Opinion Poll , 2015, PloS one.

[39]  A. Kirilenko,et al.  Public microblogging on climate change: One year of Twitter worldwide , 2014 .

[40]  N. Lam,et al.  Measuring Capacity for Resilience among Coastal Counties of the US Northern Gulf of Mexico Region. , 2012, American journal of climate change.

[41]  Johan Bollen,et al.  Twitter mood predicts the stock market , 2010, J. Comput. Sci..

[42]  Eric S. Blake,et al.  National Hurricane Center Tropical Cyclone Report: Hurricane Harvey (17 August - 1 September 2017) , 2018 .

[43]  Michelle R. Guy,et al.  Twitter earthquake detection: earthquake monitoring in a social world , 2012 .

[44]  J. Kent,et al.  Spatial patterns and demographic indicators of effective social media content during theHorsethief Canyon fire of 2012 , 2013 .

[45]  Lei Zou,et al.  Modeling the Dynamics of Community Resilience to Coastal Hazards Using a Bayesian Network , 2018 .

[46]  M. Williams,et al.  Who Tweets? Deriving the Demographic Characteristics of Age, Occupation and Social Class from Twitter User Meta-Data , 2015, PloS one.

[47]  Qunshan Zhao,et al.  Community Resilience in Maricopa County, Arizona, USA: The Analysis of Indoor Heat-Related Death and Urban Thermal Environment , 2019 .