The Djokovic Case
On January the 18th 2016, Djokovic revealed that some years ago he was approached indirectly with a £100,000 offer in order to lose a match. The revealed scandal provoked a storm of reactions from the other tennis players, news agencies as well as the social media, where there was an burst of users expressing their opinions regarding Djokovic, match fixing and other related topics.
Query Words: Djokovic Novak fixing betting corruption scam gambling fraud illegal suspicious manipulation integrity
Tweets per Day
Contains the count of tweets submitted every day 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
105.188
105.188 distinct tweets
198
198 distinct Youtube comments
1.901
1.901 distinct users
2.639
2.639 distinct words
18/01/2016 to 30/04/2016
The time frame of the data is 4 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 | djokovic | 68231 |
| 2 | novak | 45063 |
| 3 | federer | 11697 |
| 4 | open | 10444 |
| 5 | de | 9593 |
| 6 | murray | 8795 |
| 7 | tennis | 8477 |
| 8 | australian | 8368 |
| 9 | ausopen | 7839 |
| 10 | en | 7273 |
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 | #AusOpen | 7602 |
| 2 | #Djokovic | 4817 |
| 3 | #tennis | 3055 |
| 4 | #AustralianOpen | 1383 |
| 5 | #Federer | 829 |
| 6 | #BATB | 588 |
| 7 | #NovakDjokovic | 564 |
| 8 | #Tenis | 538 |
| 9 | #AusOpenpic | 520 |
| 10 | #News | 501 |
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 | @knovak832_novak | 1468 |
| 2 | @smiley2410 | 1032 |
| 3 | @LeeRock | 992 |
| 4 | @AustralianOpen | 897 |
| 5 | @DjokerNole | 846 |
| 6 | @beastieaw | 814 |
| 7 | @YouTube | 788 |
| 8 | @Annepappas22 | 644 |
| 9 | @monachris | 597 |
| 10 | @zelly309 | 563 |
| # | Word | Frequency |
|---|---|---|
| 1 | @world_tennis | 36 |
| 2 | @Bettingbonus4u | 17 |
| 3 | @l5iza | 14 |
| 4 | @bet_seek | 12 |
| 5 | @Media_Novak_ | 12 |
| 6 | @livetennis | 11 |
| 7 | @UK_Tennis_News | 10 |
| 8 | @metanoik | 8 |
| 9 | @CorbettSports | 7 |
| 10 | @aunewse | 7 |