Très bien Cyril!
Congratulations to Cyril Beyney upon a successful completion of his Masters of Science in Industrial and Systems Engineering from the University of Oklahoma.
On December 10, 2015, Cyril presented and successfully defended his thesis entitled, “Quantitative analysis of social media sensitivity to natural disasters”. The committee members, Dr. Charles Nicholson, Dr. Hank Grant, and Dr. Kash Barker, unanimously agree that the work and novel contribution meet or exceed the standards for Master-level research. His research is a first step in a nearly untouched area of social media analysis, namely how and under what circumstances can social media be used to inform decision makers during and after disastrous events to improve society’s resilience.
His work offers a quantitative analysis of the sensitivity of Twitter as a sensor to detect and measure social effects under different natural hazards including tornadoes (an all too common event in Oklahoma!), major winter storms, and forest fires.
Quantitative analysis of social media sensitivity to natural disasters
Social media platforms such as Facebook or Twitter have become a major tool for people to communicate, and for news media to relay their content. In parallel with this growth, researchers have started to use social media in various domains of study, including the analysis of people’s behavior during natural disasters. Diverse aspects of this behavior have been studied in the literature, including the detection of a disaster, the variations of the sentiment expressed, the differences regarding the distance to a disaster, or even the improvements of relevant content labeling. To the best of our knowledge, no study has been conducted that considers Twitter as a sensor with different sensitivity levels to different types of natural disasters under various circumstances.
We select 5 natural disasters of different types occurring in recent years, and for each of them we detect the shifts in behavior of the Twitter data in order to compare and contrast before, during and after the disaster the variations of tweet frequency, the proximity to the center of the disaster, the variations of sentiment expressed and the variations of tweet frequency by level of social vulnerability.
The results obtained in the empirical analysis demonstrate that Twitter is indeed a social sensor with different sensitivity levels to natural disasters. As a matter of fact we observe, depending on the type of disaster, different patterns of tweet frequency and proximity-to-disaster; negative sentiment tweets also tend to cluster closer to the disaster during the disrupted period, and finally areas with high level of social vulnerability are generally more sensitive compared to the others. The lack of available data can sometimes be an issue, but this work is an important finding to define Twitter as a social sensor to natural disasters.
Besides the tremendous work Cyril completed to be able to capture, process, clean, and filter the vast amount of available data, his contribution also includes a novel lexicon building approach to help isolate disaster related informative content.
Congratulations again to Cyril on a job well done.