Using Twitter to Track Unplanned School Closures: Georgia Public Schools, 2015-17

Exploring Social Media Network Connections to Assist During Public Health Emergency Response: A Retrospective Case-Study of Hurricane Matthew and Twitter Users in Georgia, USA

OBJECTIVE

To assist communities who suffered from hurricane-inflicted damages, emergency responders may monitor social media messages. We present a case-study using the event of Hurricane Matthew to analyze the results of an imputation method for the location of Twitter users who follow school and school districts in Georgia, USA.

METHODS

Tweets related to Hurricane Matthew were analyzed by content analysis with latent Dirichlet allocation models and sentiment analysis to identify needs and sentiment changes over time. A hurdle regression model was applied to study the association between retweet frequency and content analysis topics.

RESULTS

Users residing in counties affected by Hurricane Matthew posted tweets related to preparedness (n = 171; 16%), awareness (n = 407; 38%), call-for-action or help (n = 206; 19%), and evacuations (n = 93; 9%), with mostly a negative sentiment during the preparedness and response phase. Tweets posted in the hurricane path during the preparedness and response phase were less likely to be retweeted than those outside the path (adjusted odds ratio: 0.95; 95% confidence interval: 0.75, 1.19).

CONCLUSIONS

Social media data can be used to detect and evaluate damages of communities affected by natural disasters and identify users' needs in at-risk areas before the event takes place to aid during the preparedness phases.

Causes, characteristics, and patterns of prolonged unplanned school closures prior to the COVID-19 pandemic—United States, 2011–2019

Introduction

Outside of pandemics, there is little information about occurrence of prolonged unplanned K-12 school closures (PUSC). We describe here the reasons, characteristics, and patterns of PUSC in the United States during 8 consecutive inter-pandemic academic years, 2011–2019.

Methods

From August 1, 2011 through June 30, 2019, daily systematic online searches were conducted to collect data on publicly announced unplanned school closures lasting ≥1 school days in the United States. Closures were categorized as prolonged when schools were closed for ≥5 unplanned days (approximating one full workweek), excluding weekends and scheduled days off per school calendars.

Results

During the eight academic years, a total of 22,112 PUSCs were identified, affecting over 800,000 teachers and 13 million students that resulted in 91.5 million student-days lost. A median of 62.9% of students in PUSC-affected schools were eligible for subsidized school meals. Most affected schools were in cities (35%) and suburban areas (33%). Natural disasters (47%), adverse weather conditions (35%), and budget/teacher strikes (15%) were the most frequently cited reasons for PUSC; illness accounted for 1%, and building/facility issues, environmental issues and violence together accounted for the remaining 2%. The highest number of PUSCs occurred in Health and Human Services Regions 2, 3, 4, and 6 encompassing areas that are frequently in the path of hurricanes and tropical storms. The majority of PUSCs in these regions were attributed to a handful of hurricanes during the fall season, including hurricanes Sandy, Irma, Harvey, Florence, and Matthew.

Conclusions

PUSCs occur annually in the United States due to a variety of causes and are associated with a substantive loss of student-days for in-school learning. Both these prior experiences with PUSCs and those during the current COVID-19 pandemic illustrate a need for creating sustainable solutions for high-quality distance learning and innovative supplemental feeding programs nationwide, especially in disaster-prone areas.

Winter Storms and Unplanned School Closure Announcements on Twitter: Comparison Between the States of Massachusetts and Georgia, 2017-2018

OBJECTIVE

This project aimed to quantify and compare Massachusetts and Georgia public school districts' 2017-2018 winter-storm-related Twitter unplanned school closure announcements (USCA).

METHODS

Public school district Twitter handles and National Center for Education Statistics data were obtained for Georgia and Massachusetts. Tweets were retrieved using Twitter application programming interface. Descriptive statistics and regression analyses were conducted to compare the rates of winter-storm-related USCA.

RESULTS

Massachusetts had more winter storms than Georgia during the 2017-2018 winter season, but Massachusetts school districts posted winter-storm-related USCA at a 60% lower rate per affected day (adjusted rate ratio, aRR = 0.40, 95% confidence intervals, CI: 0.30, 0.52) than Georgia school districts after controlling for the student enrollments and Twitter followers count per Twitter account. A 10-fold increase in followers count was correlated with a 118% increase in USCA rate per affected day (aRR = 2.18; 95% CI: 1.74, 2.75). Georgia school districts had a higher average USCA tweet rate per winter-storm-affected day than Massachusetts school districts. A higher number of Twitter followers was associated with a higher number of USCA tweets per winter-storm-affected day.

CONCLUSION

Twitter accounts of school districts in Massachusetts had a lower tweet rate for USCA per winter-storm-affected days than those in Georgia.

Monitoring Different Social Media Platforms to Report Unplanned School Closures Due to Wildfires in California, October and December 2017

OBJECTIVE

Researchers at the Centers for Disease Control and Prevention monitor unplanned school closure (USC) reports through online systematic searches (OSS) to assist public health emergency responses. We counted the additional reports identified through social media along with OSS to improve USC monitoring.

METHODS

Facebook and Twitter data of public-school districts and private schools in counties affected by California wildfires in October and December of 2017 and January of 2018 were retrieved. We computed descriptive statistics and performed multivariable logistic regression for both OSS and social media data.

RESULTS

Among the 362 public-school districts in wildfire-affected counties, USCs were identified for 115 (32%) districts, of which OSS identified 104 (90%), Facebook, 59 (52%), and Twitter, 37 (32%). These data correspond to 4622 public schools, among which USCs were identified for 888 (19.2%) schools, of which OSS identified 722 (81.3%), Facebook, 496 (55.9%), and Twitter, 312 (35.1%). Among 1289 private schools, USCs were identified for 104 schools, of which OSS identified 47 (45.2%), Facebook, 67 (64.4%), and Twitter, 29 (27.9%). USC announcements identified via social media, in addition to those via OSS, were 11 public school districts, 166 public schools, and 57 private schools.

CONCLUSION

Social media complements OSS as additional resources for USC monitoring during disasters.