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Linguistic Analysis of Toxic Behavior 
in an Online Video Game 
Haewoon Kwak* Jeremy Blackburn+ 
*Qatar Computing Research...
Cyberbullying 
is the use of information technology to 
repeatedly harm or harass other people in 
a deliberate manner 
ht...
4 
Easy solution: player reports 
Additional effort is required to check whether 
the report is true or not
5 
The LoL Tribunal (2011-2014)
6 
Experienced player casts a vote for 
pardon or punish 
As of March 2013, 
105M votes are casted
Decision (Punish or pardon) 
Agreement (Majority, SM, OM) 
Outcome (Win or lose) 
User reports 
Chat logs 
In-game perform...
8 
5 vs 5 team competition game
Nexus 
Nexus
Data collected from three servers 
EUW NA KR All 
Reported players 649,419 590,311 220,614 1,460,344 
Matches 2,841,906 2,...
Our previous work on this dataset 
• “STFU NOOB! Predicting crowdsourced decisions on 
toxic behavior in online games” (WW...
Prevalent chat-related toxic behavior! 
12
http://elohell.net/chill/248678/chat-behaviour
• Explore the linguistic component of chat-related toxic 
behavior based on the large-scale Tribunal data 
14 
Research go...
15 
Begin with basic statistics
• 3.139 uni-grams (toxic players) vs. 2.732 uni-grams (typical 
players) are used per message. 
• However, typical players...
Non-uniform communication during the match 
17
Three phases of the game: Early-mid-end game 
early mid end 
“gl hf” “gg” 
18
At some point, toxic-players begin to chat more 
19
20 
Do they use different words?
Top 1,000 uni- and bi-grams of typical and toxic players 
• 867 uni-grams and 748 bi-grams are common! 
• (1000-867) = 133...
22 
Discriminative uni- and bi-grams
23 
Strategy vs. bad words
24 
Various emoticons
Some variations of the fxxx by toxic players 
(fxxx itself is also widely used by typical players) 
25
26 
When toxic bi-grams are 
frequently used?
Three different temporal patterns of bi-grams 
(based on when its peak comes) 
27 
early-bigrams mid-bigrams late-bigrams
80% of toxic bigrams are late-bigrams 
• Most of chat-related toxic playing occur at the late stage 
of the game! 
• Verba...
Different temporal patterns of common words 
• For toxic and typical players, we extract top 30 uni-grams 
at each time (0...
Differently used common uni-grams (△>30) 
30
31 
How to read the table? 
Toxic players stop actively using ‘gj’ at (T-31) 
but typical players still say it at T.
32 
Smile emoticons & apologies are (almost) 
never used by toxic players
But…appreciation is used by toxic players until 
some point 
33
Also, some words for strategic team maneuvers 
are the same 
34
Toxic players behave the same as normal 
players during the early stage of the match! 
• At some point they change their b...
• Chat are not uniform during the match 
• Discriminative uni- and bi-grams used by typical and toxic 
players as signatur...
Linguistic Analysis of Toxic Behavior in an Online Video Game
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Linguistic Analysis of Toxic Behavior in an Online Video Game

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Presentation at the 1st EGG (Exploration on Games and Gamers) Workshop collocated with SocInfo'14.

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Linguistic Analysis of Toxic Behavior in an Online Video Game

  1. 1. Linguistic Analysis of Toxic Behavior in an Online Video Game Haewoon Kwak* Jeremy Blackburn+ *Qatar Computing Research Institute +Telefonica Research EGG (Exploration on Games and Gamers) workshop November 10th 2014
  2. 2. Cyberbullying is the use of information technology to repeatedly harm or harass other people in a deliberate manner http://en.wikipedia.org/wiki/Cyberbullying
  3. 3. 4 Easy solution: player reports Additional effort is required to check whether the report is true or not
  4. 4. 5 The LoL Tribunal (2011-2014)
  5. 5. 6 Experienced player casts a vote for pardon or punish As of March 2013, 105M votes are casted
  6. 6. Decision (Punish or pardon) Agreement (Majority, SM, OM) Outcome (Win or lose) User reports Chat logs In-game performance per match (up to 5) 7
  7. 7. 8 5 vs 5 team competition game
  8. 8. Nexus Nexus
  9. 9. Data collected from three servers EUW NA KR All Reported players 649,419 590,311 220,614 1,460,344 Matches 2,841,906 2,107,522 1,066,618 6,016,046 Player reports 5,559,968 3,441,557 1,893,433 10,898,958 * KR Tribunal starts from November 2012, while other two Tribunals start from May 2011. 10
  10. 10. Our previous work on this dataset • “STFU NOOB! Predicting crowdsourced decisions on toxic behavior in online games” (WWW’14) • “Exploring Cyberbullying and Other Toxic Behavior in Team Competition Online Games” (under review) 11
  11. 11. Prevalent chat-related toxic behavior! 12
  12. 12. http://elohell.net/chill/248678/chat-behaviour
  13. 13. • Explore the linguistic component of chat-related toxic behavior based on the large-scale Tribunal data 14 Research goal
  14. 14. 15 Begin with basic statistics
  15. 15. • 3.139 uni-grams (toxic players) vs. 2.732 uni-grams (typical players) are used per message. • However, typical players send 38% more messages than toxic players per game. 16 Longer messages of toxic players
  16. 16. Non-uniform communication during the match 17
  17. 17. Three phases of the game: Early-mid-end game early mid end “gl hf” “gg” 18
  18. 18. At some point, toxic-players begin to chat more 19
  19. 19. 20 Do they use different words?
  20. 20. Top 1,000 uni- and bi-grams of typical and toxic players • 867 uni-grams and 748 bi-grams are common! • (1000-867) = 133 uni-grams and (1000-748) = 252 bi-grams are exclusively used by toxic players • We call them toxic uni- and bi-grams. • Then, what are toxic uni- and bi-grams? 21
  21. 21. 22 Discriminative uni- and bi-grams
  22. 22. 23 Strategy vs. bad words
  23. 23. 24 Various emoticons
  24. 24. Some variations of the fxxx by toxic players (fxxx itself is also widely used by typical players) 25
  25. 25. 26 When toxic bi-grams are frequently used?
  26. 26. Three different temporal patterns of bi-grams (based on when its peak comes) 27 early-bigrams mid-bigrams late-bigrams
  27. 27. 80% of toxic bigrams are late-bigrams • Most of chat-related toxic playing occur at the late stage of the game! • Verbal abuse is most likely a response to losing a game • Through manual inspection of bi-grams containing ‘bot’, • early-bigrams is non-aggressive, • mid-bigrams are cursing, • and the late-bigrams are blaming. 28
  28. 28. Different temporal patterns of common words • For toxic and typical players, we extract top 30 uni-grams at each time (0-100) • We get unique 80 uni-grams for toxic players and 91 uni-grams for typical players (top 30 uni-grams are stable) • We compute the normalized time of last use by toxic players and normal players, respectively. • Finally, we compute the difference of the last used time between toxic and normal players for common uni-grams. 29
  29. 29. Differently used common uni-grams (△>30) 30
  30. 30. 31 How to read the table? Toxic players stop actively using ‘gj’ at (T-31) but typical players still say it at T.
  31. 31. 32 Smile emoticons & apologies are (almost) never used by toxic players
  32. 32. But…appreciation is used by toxic players until some point 33
  33. 33. Also, some words for strategic team maneuvers are the same 34
  34. 34. Toxic players behave the same as normal players during the early stage of the match! • At some point they change their behavior like a phase transition • Utter neither apologies, praise, …etc. • Stop strategic communications ✓We show not just how different toxic players are, but when they become different as well 35
  35. 35. • Chat are not uniform during the match • Discriminative uni- and bi-grams used by typical and toxic players as signatures of them • Most of toxic bi-grams are found at the end of the game • Toxic players express their toxicity at some point, while they behave the same as typical players in early game • Implication: help to develop a pre-warning system to detect toxic playing 36 Summary - our findings

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