On January 18, in efforts to understand and manage extreme weather events, McGill University announced a new interdisciplinary project to combine deep learning (DL) with social network analysis (SNA). The team based their study on Twitter data from the March 2019 Nebraska floods in the United States, which caused over $1 billion in damage and widespread evacuations of residents. In total, over 1,200 tweets were analyzed and classified. Researchers found that by using a noise reduction mechanism, valuable information could be filtered from social media to better assess trouble spots and assess users’ reactions vis-à-vis extreme weather events.
“We reduced the noise by finding out who was being listened to, and which were authoritative sources,” explains Renee Sieber, Associate Professor in McGill’s Department of Geography and lead author of this study. “The vast amount of social media data the public contributes about weather suggests it can provide critical information in crises, such as snowstorms, floods, and ice storms. We are currently exploring transferring this model to different types of weather crises and addressing the shortcomings of existing supervised approaches by combining these with other methods.” (Source)
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