Pakistan cricket spot-fixing Case

This scandal of 2010 centres on certain members of Pakistan’s national cricket team being convicted of taking bribes from a bookmaker, Mazhar Majeed, to under-perform deliberately at certain times in a Test match at Lord's Cricket Ground, London, in 2010. More specifically, some reporters videotaped the bookmaker accepting money and informing the reporters that some players would deliberately bowl no balls at specific points in an over. Three cricket players were banned and convicted for this case; Salman Butt, Mohammad Asif and Mohammad Amir.

Query Words: cricket Asif Amir fixing betting corruption bet fix scandal

Tweets per Month

Contains the count of tweets submitted every month during the researching period.

Sentiment per Month

Contains information about the scores of six basic sentiments (Anger, Disgust, Fear, Joy, Sadness & Surprise), on each month.

Infobox

2926

2926 distinct tweets

401

401 distinct Youtube comments

1371

1371 distinct users (1010 from Twitter and 361 from YouTube)

4974

4974 distinct words

31/08/2015 to 01/06/2016

The time frame of the data is 9 months

Sentiment: Fear

The overall score of our sentiment analysis indicated the main sentiment of the users was Fear.

Most Frequent Words

# Word Frequency
1 cricket 4519
3 bet 3126
2 corruption 2085
5 fix 1630
4 pakistani 1299
6 match 768
7 free 676
8 pakistan 663
9 twitter 555
10 amir 520

Contains the most frequent distinct words as extracted from the final data during the researching period.

Most Frequent Hashtags

Contains the most frequent distinct hashtags as extracted from the final data. The table shows the top10 of the hashtags along with their frequencies.

# Word Frequency
1 #cricket 481
2 #pakistan 237
3 #IPL 197
4 #WT20 133
5 #news 126
6 #IPL2016 123
7 #T20 113
8 #inplaymagic 105
9 #bet 88
10 #TENNIS 81

Most Frequent Users & Mentions / Location Map

The bubble chart contains the most frequent users that tweeted about the event, the tagcloud contains the most frequent mentions included in the tweets and the choropleth map shows the locations that interacted with the tweet.

# Word Frequency
1 @TheRealPCB 12
2 @mak_asif 12
3 @YouTube 9
4 @ICC 8
5 @iamamirofficial 6
6 @MHafeez22 6
7 @AzharAli_ 5
8 @faizanlakhani 5
9 @azkhawaja1 5
10 @karachikhatmal 5
# Word Frequency
1 @TheRealPCB 12
2 @mak_asif 12
3 @YouTube 9
4 @ICC 8
5 @iamamirofficial 6
6 @MHafeez22 6
7 @AzharAli_ 5
8 @KP24 4
9 @mirzaiqbal80 4
10 @Saj_PakPassion 4