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Submitted by: Janki Adhvaryu (pt200614)
Mayank Singh Sakla (pt200814)
Using social media
data for analysis of
EBOLA
Introduction
Ebola viral disease (EVD),also known as Ebola hemorrhagic
fever(EHF)
 It is a serious, infectious viral hemorrhagic fever (VHF)
 Humans, monkeys,gorillas, chimpanzee suffers by the this virus
of filoviridae family .
 It is deadly virus with 90%deaths of all infected people.
 Symptoms of Ebola:
 poor liver function
 fever & severe weakness
 headache & Muscle pain
 After 8-10 days condition of symptoms becomes severe:
Bleeding, low white blood cells, low platelet counts, impaired
kidney
Objective:
To study the geo-linked information of Ebola in social
media:
 Identifying Ebola hotspot based on location.
 Information propagation in crisis situation
 Integrating the social media data for analysing
geographical or spatial distribution of diseases.
• Twitter
• Facebook
• World health organization
• Ministry of health
• Google news
 Our study is totally based
on Twitter as a source
Data Source
Data
collection
Data extraction
Data analysis
Desired output
Human
Situation
analysis
Data at stream rate Comparable rate Data consolidation
Methodology
From data source (Query feed.net)
Comparable data extracted
Prepared relational database
Maps showing different aspects
Behavioral pattern throughout world
Our search criteria in twitter are
• #Ebola victims
• #Ebola cause
• #Ebola outbreak
• #Ebola
Relational Database includes:
Table Attribute
Location City , country
Platform Twitter
Post Description of post
User Username
For our analysis and output we have run query on this database
We have also used ArcGIS as a tool
Output
Output map is showing the countries which are in
relation with Ebola worldwide
Output
 Among 81 countries which are in relation with
Ebola tweets; we have obtained 75 countries with
single tweets.
 Liberia ,sierra leone, Guinea are having higher
percent of tweets.
Output
Output map is showing the location of the cities.
By analyzing spatial distribution-clustering and
scattering of tweets can be understood.
Output
Search criteria is EBOLA OUTBREAK
We have got location of cities.
Analysis shows- The geographical spread of Ebola
outbreak city is in proximity of 5000km from
highly affected countries.
Output
From post content we extracted number or tweets
per states.
The output map is showing the concentration of
tweets.
Output
After reading post content of India and
Philippines.
it results into how the travel of Ebola to that
country.
Source: Google news and twitter post
contents.
Reference:
http://www.queryfeed.net/
http://news.google.co.in/
Software used:ArcGIS10.2.2
Ms Access
Thank you

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Mayank jankifnl

  • 1. Submitted by: Janki Adhvaryu (pt200614) Mayank Singh Sakla (pt200814) Using social media data for analysis of EBOLA
  • 2. Introduction Ebola viral disease (EVD),also known as Ebola hemorrhagic fever(EHF)  It is a serious, infectious viral hemorrhagic fever (VHF)  Humans, monkeys,gorillas, chimpanzee suffers by the this virus of filoviridae family .  It is deadly virus with 90%deaths of all infected people.  Symptoms of Ebola:  poor liver function  fever & severe weakness  headache & Muscle pain  After 8-10 days condition of symptoms becomes severe: Bleeding, low white blood cells, low platelet counts, impaired kidney
  • 3. Objective: To study the geo-linked information of Ebola in social media:  Identifying Ebola hotspot based on location.  Information propagation in crisis situation  Integrating the social media data for analysing geographical or spatial distribution of diseases.
  • 4. • Twitter • Facebook • World health organization • Ministry of health • Google news  Our study is totally based on Twitter as a source Data Source
  • 5. Data collection Data extraction Data analysis Desired output Human Situation analysis Data at stream rate Comparable rate Data consolidation Methodology From data source (Query feed.net) Comparable data extracted Prepared relational database Maps showing different aspects Behavioral pattern throughout world Our search criteria in twitter are • #Ebola victims • #Ebola cause • #Ebola outbreak • #Ebola
  • 6. Relational Database includes: Table Attribute Location City , country Platform Twitter Post Description of post User Username For our analysis and output we have run query on this database We have also used ArcGIS as a tool
  • 7. Output Output map is showing the countries which are in relation with Ebola worldwide
  • 8.
  • 9. Output  Among 81 countries which are in relation with Ebola tweets; we have obtained 75 countries with single tweets.  Liberia ,sierra leone, Guinea are having higher percent of tweets.
  • 10.
  • 11. Output Output map is showing the location of the cities. By analyzing spatial distribution-clustering and scattering of tweets can be understood.
  • 12.
  • 13.
  • 14. Output Search criteria is EBOLA OUTBREAK We have got location of cities. Analysis shows- The geographical spread of Ebola outbreak city is in proximity of 5000km from highly affected countries.
  • 15.
  • 16. Output From post content we extracted number or tweets per states. The output map is showing the concentration of tweets.
  • 17.
  • 18. Output After reading post content of India and Philippines. it results into how the travel of Ebola to that country. Source: Google news and twitter post contents.
  • 19.
  • 20.
  • 21.