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 | 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 |