Disaster loss and social media: Can online information increase flood resilience?

Abstract

When confronted with natural disasters, individuals around the world increasingly use online resources to become informed of forecasted conditions and advisable actions. This study tests the effectiveness of online information and social media in enabling households to reduce disaster losses. The 2011 Bangkok flood is utilized as a case study since it was one of the first major disasters to affect a substantial population connected to social media. The role of online information is investigated with a mixed methods approach. Both quantitative (propensity score matching) and qualitative (in‐depth interviews) techniques are employed. The study relies on two data sources—survey responses from 469 Bangkok households and in‐depth interviews with internet users who were a subset of the survey participants. Propensity score matching indicates that social media enabled households to reduce flood losses by an average of 37% (USD 3708 per household), using a nearest neighbor estimator. This reduction is substantial when considering that household flood losses for the matched sample averaged USD 8278. Social media offered information not available from other sources, such as localized and nearly real‐time updates of flood location and depth. With this knowledge, households could move belongings to higher ground before floodwaters arrived. These findings suggest that utilizing social media users as sensors could better inform populations during disasters. Overall, the study reveals that online information can enable effective disaster preparedness and reduce losses.

A systematic review of human behaviour in and around floodwater

“…Studies from Thailand have shown that internet-based information sources, particularly social media, may be beneficial in providing accurate and localised information (Allaire, 2016;Kaewkitipong, Chen, & Ractham, 2016). Bangkok, Thailand residents, for example, put more trust in information sought via social media compared to other online sources (87% vs. 66%) and showed a 37% reduction in flood losses per household compared to those not using social media as a source of information.…”

“…Furthermore, findings from the current review were also too scant to draw definitive conclusions on those factors that might affect knowledge sharing. Despite this, it seems important to note that flood knowledge information and awareness and appropriate flood evacuation actions are needed at the community face-to-face level to have greater impact, although non-traditional flood information sources (such as social media) should not be dismissed and these new technologies as a source of information provision are emerging as being effective in providing accurate information (Aisha et al, 2015;Allaire, 2016;Bird et al, 2012;Kaewkitipong et al, 2016;Ruin et al, 2017;Tim et al, 2017). Regardless of the method of information delivery, future research should draw on behavioural psychology methods to design a set of consistent messages shown to be effective in communicating knowledge about flood risk and the need to evacuate.…”

Quantifying Flood Vulnerability Reduction via Private Precaution

“…For instance, Aldrich () used five different methods of matching on propensity scores, that is, kernel, radius, nearest neighbor, nearest neighbor with replacement, and Mahalanobis matching, to investigate the influence of social capital on the pace of population recovery following the 1923 Tokyo earthquake. Allaire () tested the effectiveness of online information and social media in enabling households to reduce disaster losses using propensity score matching (PSM); that is, nearest neighbor and kernel matching was undertaken followed by a postmatching regression analyses. That is, the average treatment effect (ATE) was estimated using the matched sample to run postmatching regression of the outcome on covariates that are associated with flood losses, but not necessarily the likelihood of using social media.…”

Calibrating Human Attention as Indicator Monitoring #drought in the Twittersphere

“…Sakaki et al (2010) observed that their Twitter-based system for detecting earthquakes was faster than the Japanese Meteorological Agency, and that each tweet represented sensory information. In Bangkok in 2011, information on social media reduced flood loss by an average of 37% by giving enough warning time to move belongings to higher ground; this warning information was not available through other sources (Allaire 2016). Near-real-time flood maps for Jakarta could be created from tweets, where a high proportion of the population uses Twitter (Eilander et al 2016).…”