Tracking Disease Outbreaks using Geotargeted Social Media and Big Data

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1 2016 ESRI User Conference Paper #: 298, Session Title: GIS in Social Media Date: Thursday, June 30, 2016, Time: 8:30 AM - 9:45 AM, Room: Room 28 B Tracking Disease Outbreaks using Geotargeted Social Media and Big Data Dr. Ming-Hsiang Tsou Director of the Center for Human Dynamics in the Mobile Age Professor, Department of Geography, San Diego State University

2 The Center for Human Dynamics in the Mobile Age at San Diego State University Advancing Interdisciplinary Research on Big Data, Human Dynamics, and the Social Web

3 HDMA Center has been hosting two large NSF projects (CDI and IBSS): 1. Mapping Ideas from Cyberspace to Realspace. Funded by NSF Cyber-Enabled Discovery and Innovation (CDI) program. Award # $1.3 million ( ) 2. Spatiotemporal Modeling of Human Dynamics Across Social Media and Social Networks. Funded by Interdisciplinary Behavioral and Social Science Research (IBSS) program. Award#: $1 million ( ). Principal Investigator: Dr. Ming-Hsiang Tsou (Geography), Co-PIs: Dr. Dipak K Gupta (Political Science), Dr. Jean Marc Gawron (Linguistic), Dr. Brian Spitzberg (Communication), Dr. Li An (Geography). Dr. Jay Lee (Kent State, Geography), Dr. Ruoming Jin (Kent State, Computer Science), Dr. Xinyue Ye (Kent State, Geography, Dr. Heather Corliss (Public Health, SDSU), Dr. Xuan Shi (Geoscience, U of Arkansas). San Diego State University, Kent State University, University of Arkansas, USA.

4 Geography (place and time) is the KEY for Understanding and Integrating Big Data Big Data (information) Time Place (Tsou and Leitner, 2013) KDC (Knowledge Discovery in Cyberspace) framework Tsou, M. H. and Leitner, M. (2013). Editorial: Visualization of Social Media: Seeing a Mirage or a Message? In Special Content Issue: "Mapping Cyberspace and Social Media". Cartography and Geographic Information Science. 40(2), pp DOI: /

5 Geo-Targeted Social Media (Twitter) Analytics for Tracking Flu Outbreaks in U.S.

6 Data Filtering, Mining, and Visualization Application Programming Interfaces (API) Geo-Targeting Data Collection (Twitter APIs) Filter Machine Learning Trend Analysis Spatial Analysis Analysis SMART Dashboard Visualization

7 What we can get from Twitter data? Where to find geospatial information? Example: Use Twitter Search API to search for keyword HIV test or HIV testing Only 1% - 7% of Tweets have X, Y GEO-coordinates (from GPS or Geo-tagged). But 50% - 60% Tweets have city-level locations provided by their user profiles. 90% Tweets have Time Zone (limited spatial meaning)

8 The HDMA Center has built our own Internal GeoCoder Engine for User Location Profile: using GeoNames.org gazetteers (Creative Commons Data).+ User defined rules. Enable Flexible or Self-defined Geo-Target Boundaries (California, Santa Barbara, Los Angeles, San Diego bounding boxes, or State boundaries) GeoCoding Engine for Social Media

9 Collect Tweets from Top 31 U.S. Cities (17 miles radius) 31 different cities across the United States (chosen based on their population sizes): Atlanta, Austin, Baltimore, Boston, Chicago, Cleveland, Columbus, Dallas, Denver, Detroit, El Paso, Fort Worth, Houston, Indianapolis, Jacksonville, Los Angeles, Memphis, Milwaukee, Nashville-Davidson, New Orleans, New York, Oklahoma City, Philadelphia, Phoenix, Portland, San Antonio, San Diego, San Francisco, San Jose, Seattle, and Washington, D.C. Human Dynamic in the Mobile Age (HDMA)

10 Real-Time Monitoring of Flu Outbreaks in U.S. (National Scale combined 31 Cities), flu season RED Line: National ILI data (Influenza-like illness) (provided by CDC) Purple Line: Weekly Tweeting Rate (two weeks earlier than CDC data) ILI: Influenza-like Illness (R) value =

11 Trend Analysis at the Municipal Scale (San Diego) with the Lab-tested confirmed flu cases San Diego: Lab confirmed Flu Cases vs Tweeting Rate: (R) value =

12 Filter and Refine Big Data (Remove Noises) Number of tweets 10,678 5,398 4,947 4, Machine Learning Total Flu tweets collected: 307,070. Final valid flu tweets: 88,979.

13 Two research papers in the Journal of Medical Internet Research Human Dynamic in the Mobile Age (HDMA)

14 SMART Dashboard Social Media Analytic and Research Testbed YouTube Video for 3 Mins Real-time social media analytics (Trend Analysis, Word Clouds, Top URL, web pages, Top Hashtags/Mentions/Stories).

15 Tracking Flu Outbreaks in 2014/2015 Flu Season # of Filtered ILI Tweets, Top 30 US Cities, as of February 9, 2015 (from SMART dashboard) Only 1% -4% tweets has Geo-tagged coordinates. Problems!!! Twitter broke its Search APIs on 11/20/2014 and only returned Geo-tagged tweets only. (Reduce 90% -95% of tweets collected) CDC Influenza Positive Tests, National Data Summary, through Weeks 40-3, Season

16 Comparison between ILI and Geo-tagged-only Tweets (4%) among 30 U.S. Cities Human Dynamic in the Mobile Age (HDMA)

17 2016 Flu Tweets (31 cities) vs CDC ILI data? The comparison between National ILI Rate and the 32 Cities Tweeting Rate, with prediction up to Week 15. Red National ILI, Purple Tweet Rate for

18 Next Step: Syndromic Surveillance (Underdevelopment) (tracking multiple Symptoms: fever, cold, cough, vomiting, etc. ) Designed for Early Detection of unknown disease outbreaks, such as Swine Flu and SARS

19 The Limitations and Challenges of Social Media and Big Data Research

20 Social Media User Profiles Social Media messages can NOT represent all population, but it can provide warning signals and real-time updates Survey (Business Insider) Twitter Users are Young (60% are between years old). More Urban residents than rural Higher adoption% in African Americans Many Journalists and Mass Media staff. 20% are not real human beings (robots): many advertisement and marketing activities. Using Different Keywords can get different demographic groups: #Healthcare: include more senior people (Very few teenagers will tweet about healthcare ). (We need more background study). Keywords could be used as a sampling tool for social media users.

21 Who are they? How they post the messages? Are They Mummies and Ghosts (Zombie)?

22 Who are these accounts? Why Say the exactly same words? Use SMART dashboard to track E-cigarette topics High Peak on Feb 11, 2016 (Why?)

23 1,553 Twitter Accounts Said the Exact Sentence! In One Day (2/11/2016), From to = 1553 (Mummy or Ghost Twitter Accounts?) for Advertisement?

24 User Privacy Issue Concerns about Big Brother. Although all the tweets collected from APIs are public tweets (everyone can search them and retrieve them). Some content of tweets may contain personal private information (real names, locations of homes, offices, private conversations, medical situations, etc.) * HDMA center conceals tweet locations by randomly selecting a coordinate in a 100m radius of the original location to protect Twitter users' privacy.

25 Thank You Q & A Director: Dr. Ming-Hsiang (Ming) Tsou Funded by NSF Cyber-Enabled Discovery and Innovation (CDI) program. Award # ( ) NSF Interdisciplinary Behavioral and Social Science (IBSS) Program, Award # ( ): Spatiotemporal Modeling of Human Dynamics Across Social Media and Social Networks. Human Dynamic in the Mobile Age (HDMA)

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