Mapping Human Dynamics with Social Media for Disaster Alerts

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1 Mapping Human Dynamics with Social Media for Disaster Alerts Dr. Ming-Hsiang Tsou Professor of Geography, Director of the Center for Human Dynamics in the Mobile Age, San Diego State University Dr. Chin-Te (Calvin) Jung, Chief Data Scientist, the Center for Human Dynamics in the Mobile Age, SDSU.

2 What is Big Data? Image source: (definition from IBM, and WIPRO) Is this a good definition of Big Data?

3 Big Data is Human-Centered Data Big Data is dynamic datasets created by or derived from human activities, communications, movements, and behaviors (Tsou, 2015). The term, big data, should refer to big ideas, big impacts, and big changes for our society rather than only focusing on big volume.

4 Big Data Category (Tsou, 2015). Social life data include popular social media services (Twitter, Flickr, Snapchat, YouTube, Foursquare, etc.), online forums, online video games, and web blogs. Health data include electronic medical records (EMR) from hospitals and health centers, cancer registry data from state and local communities, official disease outbreak tracking and epidemiology data Business and commercial data include credit card transactions, online business reviews (such as Yelp and Amazon reviews), supermarket membership records, shopping mall transaction records per store, credit card fraud examination data, enterprise management data, and marketing analysis data. Transportation and traffic data include GPS tracks (from taxi, buses, Uber, bike sharing programs, and mobile phones), traffic censor data (from subways, trolleys, buses, bike lanes, highways), social media data (from check-ins, Waze, and other social media platforms), and mobile phone data (from data transmission records and cellular network data). Scientific research data include earthquakes sensors, weather sensors, satellite images, crowd sourcing data for biodiversity research, volunteered geographic information, and census data.

5 Value of Big Data: Integration (Data Fusion) Explore their spatiotemporal relationships in both network space and geographical space. Disaster Data Layer Image provided by Dr. Atshushi Nara (Associate Director of HDMA Center).

6 Geography (place and time) is the KEY for Understanding and Integrating Big Data (Tsou and Lietner, 2013) Big Data (information) Time Place

7 NSF project website Human Dynamic in the Mobile Age (HDMA)

8 Two Main Goals: 1. Improve the Alert Warning Effectiveness in Multiple Social Media Channels 2. Monitor Social Media Messages and Potential Help Requests/Ground Truth Observation.

9 NSF IBSS Award ( ) This project will enable the collaboration between SDSU and San Diego OES to build and test an multi-channel, geotargeted disaster warning system: 1. Alert Broadcaster to effectively disseminate official alerts and warnings messages from OES staff via social media channels. 2. Social Media Monitor (SMART Dashboard) to monitor the diffusion of the official alerts in terms of mentions, re-tweets, and followers. 3. Volunteer Collaborator to ensure each OES volunteer will retweet the official announcement from OES. The platform will identify and recruit 1000 social media volunteers in San Diego based on their social network influences 4. Social Media Analytic Viewer (GEO Viewer) for OES staff to query specific keywords in social media and map the relevant geotagged social media messages.

10 Web-based Real-time GeoViewer Tool v.2.2 (Video demo) EC2: (Live) New Functions and Improvement: 1. Online Tutorial and YouTube video 2. Add new map layers from OES and NWS CAP maps. 3. Login-User labeling functions. 4. Save Search result function. 5. Dynamic cluster texts and hot spot radius change visualization.

11 Use ESRI ArcGIS Online Basemap Layers (Light Gray Map, Satellite, Street, National Geographic, NASA Night View, Open Street Map)

12 How to find out critical information from thousands of GPS-tagged tweets or hundreds of thousands of Non-GPStagged tweets? Nepal Earthquake Example: (keyword search: trap ) One Possible Solution: Manual labeling (first 1000 tweets by volunteers) + Machine Learning Classification (built-in).

13 Digital Volunteers may help us identify and select important Tweets (for machine learning) during and after the disaster events. Need Some programming and design help from OES, RedCross, and 211: 1. How to combine multiple volunteers Inputs and Integration Systems (ranking system). 2. Which category and color schemes/labels should we use for each types of disasters (flooding, wildfires, earthquake, hurricanes). 3. Which tags might be useful? 4. Who are the target users? What kinds of Output system should we create? (for OES staff? For RedCross staff?) 5. Other suggestions?

14 SMART Dashboard for Nepal Earthquake Social Media Analytic and Research Testbed

15 Design the Alert Broadcaster (to disseminate OES @UTsandiego Recruit 500 top influencer and 500 selected sample group representatives Create the Alert Broadcaster for them to retweet and re-send alerts. Analyzing the Spreads (Speed, Scale, and Range) of Social Media Messages in Different Social Networks. (following, retweets, and mentions relationships)

16 @SanDiegoCounty Twitter Account: All Followers Analysis (31,000: 2014, 40,500: 2015) Most influential followers (using followers_count) are not from San Twitter Account: Newly Added Followers (350 - Changed between 5/16 to 5/18, 2014, During the Carlsbad wildfire) Change in Followers - 05/16-05/17) Most new followers are from San Diego. Followers followgain followloss followchange Human Dynamic in the Mobile Age (HDMA)

17 Situation Awareness San Diego County: Office of Emergency Services (OES)

18 Spatiotemporal Modeling of Human Dynamics Across Social Media and Social Networks Interdisciplinary Behavioral and Social Science Research, # National Science Foundation Thank You! Dr. Ming-Hsiang Tsou San Diego State University

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