Australia’s Southern Stars Case
On 15 September 2013 Victoria Police arrested up to ten people, including Southern Stars players David Obaze, Nick McKoy and Joe Woolley and coach, Zaya Younan. Later in September criminal charges were laid via the Victoria Police and the FFA handed Noel, Woolley, David Obaze, Nicholas McKoy and coach Zia Younan initial suspensions which prevented them from being involved in any football-related activity in Australia while the investigation was ongoing. Those bans were also extended worldwide by FIFA. Two British footballers, Reiss Noel and Joe Woolley received life-time bans from FIFA due to the incident. Gerry Subramaniam, a Malaysian national and the Australian ring-leader of the betting syndicate behind the match fixing, was sentenced to three years jail in Australia, to be followed by deportation to Malaysia.
Query Words: Southern Stars fixing betting corruption scam gambling fraud illegal suspicious manipulation integrity
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
204
204 distinct tweets
197
197 distinct users
869
869 distinct words
02/07/2011 to 07/05/2016
The time frame of the data is 4 years and 10 months
Sentiment: Disgust
The overall score of our sentiment analysis indicated the main sentiment of the users was Disgust.
Most Frequent Words
| # | Word | Frequency |
|---|---|---|
| 1 | stars | 177 |
| 2 | southern | 175 |
| 3 | fixing | 137 |
| 4 | match | 125 |
| 5 | betting | 39 |
| 6 | scandal | 34 |
| 7 | players | 33 |
| 8 | vpl | 29 |
| 9 | football | 22 |
| 10 | fix | 21 |
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 | #VPL | 20 |
| 2 | #SouthernStars | 12 |
| 3 | #football | 7 |
| 4 | #Australia | 6 |
| 5 | #matchfixing | 4 |
| 6 | #HorseRacing | 4 |
| 7 | #VictorianPremierLeague | 2 |
| 8 | #Victoria | 2 |
| 9 | #betting | 2 |
| 10 | #VictoriaCrimesAct | 2 |
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 | @SouthernStars | 10 |
| 2 | @_SouthernStars | 6 |
| 3 | @abcnews | 4 |
| 4 | @tennewsmelb | 3 |
| 5 | @theage | 3 |
| 6 | @westindies | 3 |
| 7 | @2ser | 2 |
| 8 | @2 | 2 |
| 9 | @FFV_VPL | 2 |
| 10 | @7NewsMelbourne | 1 |
| # | Word | Frequency |
|---|---|---|
| 1 | @SouthernStars | 10 |
| 2 | @_SouthernStars | 6 |
| 3 | @abcnews | 4 |
| 4 | @theage | 3 |
| 5 | @tennewsmelb | 3 |
| 6 | @westindies | 3 |
| 7 | @2ser | 2 |
| 8 | @FFV_VPL | 2 |