Difference between revisions of "Twitter Dataset For Influence"

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(Created page with ' This [[Category::Dataset|dataset]] consists of Singapore-based twitter users during the month of April 2009. The friends and followers network of top-1000 Singapore users is cr…')
 
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The following figure shows the distribution of the tweets.
 
The following figure shows the distribution of the tweets.
  
[[File:Examp.jpg]]
+
[[File:Example1.jpg]]
  
 
The figure shows that after a time period, the popularity of Twitter among the Singapore users increases substantially with time.
 
The figure shows that after a time period, the popularity of Twitter among the Singapore users increases substantially with time.
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The following figure shows the distribution of tweets per twitter user.
 
The following figure shows the distribution of tweets per twitter user.
  
[[File:Example.jpg]]
+
[[File:Example2.jpg]]
  
 
The following graphs depicts the relationship between number of friends and number of followers.
 
The following graphs depicts the relationship between number of friends and number of followers.
  
[[File:Example.jpg]]
+
[[File:Example3.jpg]]
  
 
The graph shows that the x and y axis are highly correlated.
 
The graph shows that the x and y axis are highly correlated.

Revision as of 07:18, 4 October 2012

This dataset consists of Singapore-based twitter users during the month of April 2009. The friends and followers network of top-1000 Singapore users is crawled along with their tweets. The dataset consists of number of tweets |T| = 1,021,039 and number of users |S| = 6748.

The following figure shows the distribution of the tweets.

File:Example1.jpg

The figure shows that after a time period, the popularity of Twitter among the Singapore users increases substantially with time.

The following figure shows the distribution of tweets per twitter user.

File:Example2.jpg

The following graphs depicts the relationship between number of friends and number of followers.

File:Example3.jpg

The graph shows that the x and y axis are highly correlated.