Mayank jankifnl

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 consolidationMethodology

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 databaseWe 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.

OutputFrom post content we extracted number or tweets per states.

The output map is showing the concentration of tweets.

OutputAfter 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