General Case
This first case is used to examine the Social Media presence of the "Sports Fixing" topic, understand the crowd that is engaged with the topic and determine some basic set of words and hashtags that are frequently used in such posts. The query for this case was consisted of plain words, without studying any specific event.
Query Words: match fixing fix betting bet 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
62.319
62.319 distinct tweets
1228
1228 distinct Youtube comments
34.227
34.227 distinct users
53.526
53.526 distinct words
01/01/2016 to 30/06/2016
The time frame of the data is 6 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 | match | 52474 |
| 2 | bet | 36311 |
| 3 | fix | 32004 |
| 4 | tips | 6046 |
| 5 | goal | 5302 |
| 6 | 4616 | |
| 7 | win | 3762 |
| 8 | free | 3629 |
| 9 | today | 3351 |
| 10 | odds | 3164 |
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 | #betting | 3501 |
| 2 | #prediction | 1744 |
| 3 | #tip | 1586 |
| 4 | #1X2 | 1335 |
| 5 | #livescore | 899 |
| 6 | #inplay_betting | 897 |
| 7 | #inplay | 757 |
| 8 | #FSTINPLAY | 554 |
| 9 | #Euro2016 | 554 |
| 10 | #soccerbets | 411 |
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 | @YouTube | 159 |
| 2 | @bet365 | 134 |
| 3 | @realDonaldTrump | 115 |
| 4 | @NaseemNsm1 | 101 |
| 5 | @DavidVonderhaar | 89 |
| 6 | @Treyarch | 75 |
| 7 | @FootyAccums | 73 |
| 8 | @ManUtd | 59 |
| 9 | @SkyBet | 58 |
| 10 | @FootySuperTips | 54 |
| # | Word | Frequency |
|---|---|---|
| 1 | @IQ_Bet | 1742 |
| 2 | @Tips4Bet | 1319 |
| 3 | @inplay_betting | 1263 |
| 4 | @FootySuperTips | 1006 |
| 5 | @Oddsmeter | 721 |
| 6 | @livetennis | 636 |
| 7 | @Free_Bet_Club | 470 |
| 8 | @Tipsteric | 427 |
| 9 | @Mrfootball_Bet_ | 351 |
| 10 | @ApolloBet | 277 |