Utilizing Social Media to Understand Human Interaction with Extreme Media Events - The Superstorm Sandy Beta Test Arthur G. Cosby Somya D. Mohanty National Weather Service Online Webinar Jul 16, 2013
NASA-NOAA Suomi National Polar- orbiting Partnership (NPP) satellite
Flicker Twitter
Twitter Social Networking and micro-blogging service Social Networking and micro-blogging service Created in 2006 Created in character tweets 140 character tweets 140+ million users /400 million tweets per day 140+ million users /400 million tweets per day Fast information propagation Fast information propagation Our Access: Our Access: Real-time Firehose – Instantaneous acquisition of tweets Real-time Firehose – Instantaneous acquisition of tweets Historical Track – Tweets since 2006 Historical Track – Tweets since 2006
Extreme Events and Social Media Traditional Methods Traditional Methods Telephone Survey Telephone Survey Invasive information acquisition Invasive information acquisition Twitter Twitter 170 million active users worldwide 170 million active users worldwide 48 million in U.S. 48 million in U.S. ~26 million geo-located “ human sensors” ~26 million geo-located “ human sensors” Passive information collection Passive information collection Use Cases Use Cases Sandy Super-Storm Sandy Super-Storm Moore Tornado Moore Tornado
Tracking Tweets Geographic Bounding Boxes Geographic Bounding Boxes Hurricane or Tornado path Hurricane or Tornado path Keyword Searches Keyword Searches Complex searches on text within tweets Complex searches on text within tweets User Tracking User Tracking Tracking any tweets either made by a user or mentioning a user – National Weather Service twitter handle) Tracking any tweets either made by a user or mentioning a user – National Weather Service twitter handle) Hashtag Tracking Hashtag Tracking Tracking on topics (i.e. #sandy) Tracking on topics (i.e. #sandy)
Advantages of Tracking Social Media Network Resiliency Network Resiliency Mobile phone service is pretty resilient - in certain use cases traffic doubled Mobile phone service is pretty resilient - in certain use cases traffic doubled Real-time Visual Monitoring Real-time Visual Monitoring Tracking of pictures posted of the event from twitter users via Instagram, Vine, etc. Tracking of pictures posted of the event from twitter users via Instagram, Vine, etc. Identification of Sub-events Identification of Sub-events Power Outages, Flooding, Disaster recovery Power Outages, Flooding, Disaster recovery Determine Human Mobility Patterns Determine Human Mobility Patterns Ability to help disaster recovery agencies assist before, after and during and event Ability to help disaster recovery agencies assist before, after and during and event
Advantages of Tracking Social Media Development of Predictive Algorithms Development of Predictive Algorithms Utilizing historical data to create predictive models capable of detecting future events Utilizing historical data to create predictive models capable of detecting future events Predicting the extent of damages as a result of an disaster Predicting the extent of damages as a result of an disaster Help and Assist Information Propagation Help and Assist Information Propagation Developing organic networks in case of an event need real-time information feedback. Developing organic networks in case of an event need real-time information feedback. Prevent Incorrect Information Dissemination Prevent Incorrect Information Dissemination Analyzing the information disseminated by the users of the network for their validity in context to an event Analyzing the information disseminated by the users of the network for their validity in context to an event
Moore Tornado (OK) 138K geo-coded tweets – May 15 th – May 30 th 138K geo-coded tweets – May 15 th – May 30 th Utilization Utilization Structural analysis of buildings, roads and infrastructure using posted pictures Structural analysis of buildings, roads and infrastructure using posted pictures Modeling predictive algorithms by extracting parameters consistent with tweets from affected areas Modeling predictive algorithms by extracting parameters consistent with tweets from affected areas NSF Rapid Response Grant NSF Rapid Response Grant
Sandy SuperStorm 4.8M Tweets - Oct 27 th – Nov 14 th M Tweets - Oct 27 th – Nov 14 th 2012 Utilization Utilization Real-time visual monitoring of posted pictures Real-time visual monitoring of posted pictures Traffic Analysis for Resiliency Traffic Analysis for Resiliency Sub-Event Analysis – Power Outage Sub-Event Analysis – Power Outage Topic Analysis – Keyword and Hashtag Clouds Topic Analysis – Keyword and Hashtag Clouds Trend Analysis – Occurrence of events relative to others Trend Analysis – Occurrence of events relative to others Sentiment Analysis – Feedback of public opinion Sentiment Analysis – Feedback of public opinion U.S. Department of Health and Human Services U.S. Department of Health and Human Services Office of the Assistant Secretary for Preparedness and Response Office of the Assistant Secretary for Preparedness and Response Collaboration with New Jersey Mayors office and Harvard Law School Collaboration with New Jersey Mayors office and Harvard Law School
Hurricane Sandy Study Social Media Tracking and Analysis System SMTAS
Public Sentiment for Relief Agencies Following Hurricane Sandy
Organic Help Networks Creating networks of help Creating networks of help Offers to help Offers to help Asking for help from organizations Asking for help from organizations Asking for help from followers Asking for help from followers
Creating Networks of Help
Offers for help
Asking for help from organizations
Asking for help from followers
Innovative Data Laboratory