Utilizing Social Media to Understand Human

Interaction with Extreme Media Events -

The Superstorm Sandy Beta Test

Arthur G. Cosby Somya D. Mohanty

National Weather ServiceOnline Webinar

Jul 16, 2013

NASA-NOAA Suomi National Polar-orbiting Partnership (NPP) satellite

Flicker Twitter

Facebook

Twitter

• Social Networking and micro-blogging service

• Created in 2006

• 140 character tweets

• 140+ million users /400 million tweets per day

• Fast information propagation

• Our Access:• Real-time Firehose – Instantaneous acquisition of tweets• Historical Track – Tweets since 2006

Extreme Events and Social Media

• Traditional Methods• Telephone Survey• Invasive information acquisition

• Twitter• 170 million active users worldwide

• 48 million in U.S.

• ~26 million geo-located “human sensors”• Passive information collection

• Use Cases• Sandy Super-Storm• Moore Tornado

Tracking Tweets

• Geographic Bounding Boxes• Hurricane or Tornado path

• Keyword Searches• Complex searches on text within tweets

• User Tracking• Tracking any tweets either made by a user or

mentioning a user (i.e. @usNWSgov – National Weather Service twitter handle)

• Hashtag Tracking• Tracking on topics (i.e. #sandy)

Advantages of Tracking Social Media

• Network Resiliency• Mobile phone service is pretty resilient - in certain use

cases traffic doubled

• Real-time Visual Monitoring • Tracking of pictures posted of the event from twitter

users via Instagram, Vine, etc.

• Identification of Sub-events • Power Outages, Flooding, Disaster recovery

• Determine Human Mobility Patterns• Ability to help disaster recovery agencies assist

before, after and during and event

Advantages of Tracking Social Media

• Development of Predictive Algorithms• Utilizing historical data to create predictive models

capable of detecting future events• Predicting the extent of damages as a result of an

disaster

• Help and Assist Information Propagation • Developing organic networks in case of an event

need real-time information feedback.

• Prevent Incorrect Information Dissemination • 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 15th – May 30th

• Utilization• Structural analysis of buildings, roads and

infrastructure using posted pictures• Modeling predictive algorithms by extracting

parameters consistent with tweets from affected areas

• NSF Rapid Response Grant

Sandy SuperStorm

• 4.8M Tweets - Oct 27th – Nov 14th 2012

• Utilization• Real-time visual monitoring of posted pictures• Traffic Analysis for Resiliency• Sub-Event Analysis – Power Outage• Topic Analysis – Keyword and Hashtag Clouds• Trend Analysis – Occurrence of events relative to others• Sentiment Analysis – Feedback of public opinion

• U.S. Department of Health and Human Services • Office of the Assistant Secretary for Preparedness and

Response• Collaboration with New Jersey Mayors office and Harvard Law

School

Hurricane Sandy Study

Social Media Tracking and Analysis System

SMTAS

Public Sentiment for Relief AgenciesFollowing Hurricane Sandy

Organic Help Networks

• Creating networks of help

• Offers to help

• Asking for help from organizations

• Asking for help from followers

Creating Networksof Help

Offersfor help

Asking for help from organizations

Asking for help from followers

SMTAS @ Innovative Data Laboratory

www.idl.ssrc.msstate.edu