Fancy Bears Case
The officials of International Association of Athletics Federations (IAAF) stated in April 2017 that its servers had been hacked by the "Fancy Bear" group. The attack was detected by cybersecurity firm Context Information Security which identified that an unauthorised remote access to IAAF's servers had taken place on February 21. IAAF stated that the hackers had accessed the Therapeutic Use Exemption applications, needed to use medications prohibited by WADA.
Query Words: fancy bears therapeutic use exemption
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
9521
9521 distinct tweets
0
0 distinct Youtube comments
6966
6966 distinct users
13026
13026 distinct words
01/09/2016 to 30/09/2017
The time frame of the data is 13 months
Sentiment: Fear
The overall score of our sentiment analysis indicated the main sentiment of the users was Fear.
Most Frequent Words
Contains the most frequent distinct words as extracted from the final data. The table shows the top-10 words along with their frequencies.
| # | Word | Frequency |
|---|---|---|
| 1 | fancy | 9277 |
| 2 | bears | 9220 |
| 3 | wada | 1280 |
| 4 | de | 1120 |
| 5 | hackers | 1024 |
| 6 | 784 | |
| 7 | hack | 760 |
| 8 | doping | 641 |
| 9 | athletes | 551 |
| 10 | leak | 514 |
Most Frequent Hashtags
Contains the most frequent distinct hashtags as extracted from the final data. The table shows the top-10 hashtags along with their frequencies.
| # | Hashtag | Frequency |
|---|---|---|
| 1 | #WADA | 84 |
| 2 | #news | 66 |
| 3 | #Bears | 52 |
| 4 | #trndnl | 50 |
| 5 | #FancyBears | 44 |
| 6 | #hacking | 43 |
| 7 | #cybersecurity | 41 |
| 8 | #Fancy | 39 |
| 9 | #infosec | 33 |
| 10 | #News | 33 |
Most Frequent Users
Contains the most frequent Twitter users as extracted from the final data. The table shows the top-10 users along with their frequencies.
| # | User | Frequency |
|---|---|---|
| 1 | @netsecu | 46 |
| 2 | @HackAlertNews | 35 |
| 3 | @liveeventsus | 30 |
| 4 | @trendinaliaGB @trendinaliaGB | 29 |
| 5 | @Rus_Eng_News | 29 |
| 6 | @worldnewsday | 28 |
| 7 | @RT_russian | 20 |
| 8 | @ringsau | 19 |
| 9 | @TazSpinZone | 15 |
| 10 | @mjshrimper | 15 |
Most Frequent Mentions
Contains the most frequent Twitter mentions as extracted from the final data. The table shows the top-10 mentions along with their frequencies.
| # | Mention | Frequency |
|---|---|---|
| 1 | @lequipe | 30 |
| 2 | @wada_ama | 27 |
| 3 | @SputnikInt | 20 |
| 4 | @FancyBears | 16 |
| 5 | @BBCNews | 16 |
| 6 | @YouTube | 14 |
| 7 | @fancy | 13 |
| 8 | @marca | 12 |
| 9 | @c0nvey | 11 |
| 10 | @RafaelNadal | 9 |