Mining Social Network WWW 2012 Lyon (France) - 16 April, 2012.

Transcription

1 A first study on video game live streaming M. Kaytoue 1, A. Silva 1, L. Cerf 1, W. Meira Jr. 1, C. Raïssi 2 Belo Horizonte Brazil 1 2 Nancy France Mining Social Network WWW 2012 Lyon (France) - 16 April, 2012.

2 Electronic Sports Watching E-Sport on internet: a new entertainment? Just like traditional sport but with video games Professional commentators, sponsors, tournaments, etc. Professional gamers streaming their games over internet Spectators prefer to watch rather than playing themselves A new Web community is growing Widely using Web media such as FaceBook, Twitter, etc. and... Live video game streaming platform gaining in popularity Very active, important frequency of events 2 / 38

3 Events and tournaments 3 / 38

4 Social TV 4 / 38

5 Contribution Starting from Twitch.tv audience data From September 29th, 2011 to January 09th, 2012 Every five minutes, get tuples of active streams (date, login, game, description, count,...) We propose a first characterization of this community Findings Quantitatively: audience, content length, etc. Qualitatively: What games? Where? etc. Early prediction of the audience Ranking most popular professional gamers Important for E-Sport actors With nice perspectives of research 5 / 38

6 Outline 1 A first characterization of the E-Sport community 2 Predicting stream popularity 3 Ranking streamers 4 Conclusion and perspectives 6 / 38

7 A first characterization of the E-Sport community Twitch data acquisition and description Data From September 29th, 2011 to January 09th, 2012 Every five minutes, get all of active streams and their audience More than 24 millions of tuples Cleaning: missing values, removing illegal streams (1.54%), etc. field date login game description count description The date of crawling of the tuple Unique identifier of a user/streamer The game or topic of the stream A text description of the stream The number of viewers/spectators watching the stream at a given time 7 / 38

8 A first characterization of the E-Sport community Dataset Summary Period of analysis Sept 29, 11 - Jan 9, 12 #timestamps 28,292 (832 missing) #logins 129,332 #games 17,749 #tuples 24,018,644 #illegal tuples 369,470 (1.54%) #sessions 1,175,589 #views 27,120,337 Length streamed years Length watched 9,622.4 years 8 / 38

9 A first characterization of the E-Sport community Views along the weeks (When?) viewers streamers avg nb of viewers avg nb of streamers Sun Mon Tue Wed Thu Fri Sat Sun / 38

10 A first characterization of the E-Sport community Geographic distribution (Where?) 10 / 38

11 A first characterization of the E-Sport community Top 20 most popular games (What?) Game Audience Release StarCraft II 35.05% July 2010 Heroes of Newerth 8.89% May 2010 League of Legends 8.19% Oct World of Warcraft 6.24% Nov Call of Duty: BO 3.88% Nov Street fighter % Apr Star Wars (TOR) 2.98% Dec 2011 The Elder Scrolls 2.36% Nov MineCraft 2.03% Nov Rage 1.98% Oct Marvel vs. Capcom % Feb Dota 2 (beta) 1.55% Sep Battlefield % Oct Warcraft III 1.22% July 2002 Halo: Reach 1.20% Sept Mario Kart % Dec Dark Souls 1.10% Oct Zelda SS 1.05% Nov 2011 Gears of War % Sept Counter-Strike S 0.89 % Nov Others 12.95% 11 / 38

12 A first characterization of the E-Sport community Local game popularity (What?) % of daily audience Heroes of Newerth Zelda World of Warcraft Warcraft III The Elder Scrolls Mario s Street Fighter Star Wars Starcraft II Rage MineCraft Marvel vs. Capcom League of Legends Halo Counter-Strike Gears of War Dota Dark Souls Call of Duty Battlefield Time (days) 12 / 38

13 A first characterization of the E-Sport community Major E-Sport events (What?) Oct. 11 Nov. 11 Dec. 11 Jan. 12 IEM N-Y MLG Orlando IGN Pro League DreamHack Winter Blizzard Cup Home Story Cup NASL S2 Finals NE League S2 Grand Finals 12 hours for charity #views 13 / 38

14 A first characterization of the E-Sport community Stream and Streamer characteristics cumulative (%) duration (min) (a) Stream cumulative (%) duration (min) (b) Streamer Duration of streams and aggregate duration of streamers 1 agregate views agregate views stream rank streamer rank (c) Stream (d) Streamer Stream and streamer audience 14 / 38

15 1 A first characterization of the E-Sport community 2 Predicting stream popularity 3 Ranking streamers 4 Conclusion and perspectives

16 Predicting stream popularity Motivation Current Twitch recommendation strategy New and interesting streams may take too long (or even never) to become visible 16 / 38

17 Predicting stream popularity Motivation Streaming sessions have a highly skewed popularity distribution, short duration, and slow popularity evolution. agregate views stream rank cumulative (%) duration (min) proportion of the overall maximal popularity 0.3 average session for the top-100 streamers hours since the beginning of a session (e) (f) Stream popularity, duration and popularity evolution (g) 17 / 38

18 Predicting stream popularity Idea Predicting popularity using initial popularity records popularity after 1 hour popularity after t i minutes popularity after 1 hour popularity after t i minutes (h) t i = 5 min. (i) t i = 30 min. Correlation between stream popularity after t i minutes and 1 hour 18 / 38

19 Predicting stream popularity Correlation Varying t i Correlation between popularity after t i minutes and 1 hour correlation corr. 9 ε t i (min) mean squared error 19 / 38

20 Predicting stream popularity Prediction Model Model log(pop(t f )) = β 0 + β 1 log(pop(t i )) + ɛ Predicted vs. actual (based on popularity after t i minutes) actual popularity after 1 hour predicted popularity after 1 hour (j) t i = 5 min. actual popularity after 1 hour predicted popularity after 1 hour (k) t i = 30 min. 20 / 38

21 Predicting stream popularity MSE Varying t i MSE for different values of t i (minutes) correlation corr. 9 ε t i (min) mean squared error 21 / 38

22 1 A first characterization of the E-Sport community 2 Predicting stream popularity 3 Ranking streamers 4 Conclusion and perspectives

23 Ranking streamers Why rank streamers? Interesting for Spectators: Who to watch? Sponsors: Who to support? Teams: Who to recruit? Gamers: Is my rival doing better? Game editors: Is my game more popular than my concurrents? 23 / 38

24 Ranking streamers Comparing two streamers Audience depends of other streams active at the same time Comparison of two streamers when they broadcast together Example On Nov :00, WhiteRa is preferred to EG.IdrA. They are not comparable with Mill.Stephano. crawl time Oct :30 Oct :35 Nov :00 EG.IdrA Mill.Stephano WhiteRa / 38

25 Ranking streamers Challenge Difficulty Raw audience is not a good measure of popularity because of: daily/weekly variations of the number of viewers and sessions; variations of the number of viewers along a session. Idea for aggregating the preferences Consider the streamers as candidates, the crawl points as voters and apply a Condorcet method that is known to be good for ranking: Maximum Majority Voting. 25 / 38

26 Ranking streamers Ranking the pairs of streamers Three criteria with the following precedence: c 1 How often the first streamer is preferred to the second; c 2 How often they have the exact same popularity; c 3 How often they broadcast at the same time. c 1 c 2 c 3 (EG.IdrA,WhiteRa) (EG.IdrA,Mill.Stephano) (WhiteRa,Mill.Stephano) / 38

27 Ranking streamers Building an acyclic directed graph Until all ranked pairs are processed: 1 Add all tied pairs as edges; 2 For every newly added edge, decide the existence of a cycle involving it; 3 Remove those involved in a cycle; 4 Go to / 38

28 Ranking streamers Resulting graph 28 / 38

29 Ranking streamers Results with Top-100 streamers Focusing on eight StarCraft II players Web poll (# votes) Simple ranking (pos.) Condorcet WhiteRa (11,112) EG.IdrA (20) EG.IdrA Mill.Stephano (9,192) WhiteRa (21) Mill.Stephano EG.IdrA (6,746) Liquid Ret (31) EG.HuK EG.HuK (5,050) EG.HuK (32) WhiteRa Liquid HerO (2,160) Mill.Stephano (33) Liquid HerO Liquid Sheth (846) Liquid HerO (53) QxG.SaSe QxG.SaSe (833) Liquid Sheth (72) Liquid Sheth Liquid Ret (684) QxG.SaSe (91) Liquid Ret 29 / 38

30 1 A first characterization of the E-Sport community 2 Predicting stream popularity 3 Ranking streamers 4 Conclusion and perspectives

31 Conclusion and perspectives Conclusion Characterization of a new Web community Gathered around social TV (Twitch.tv) Quantitative and qualitative characterization Popular tournaments and releases translate into audience Early prediction of future audience of a stream Ranking popular players via a Condorcet method A particular interest For the actors of this community (spectators, pro-gamers, sponsors, game publishers, etc.) For the research community (social network, data-mining, social sciences, etc.) 31 / 38

32 Conclusion and perspectives Going further into the characterization A community per se accommodated with Web technologies, intensively using Web media like Facebook, Twitter, YouTube, and very active, making it an interesting study case for researchers. Further work A better characterization, including other media/data Formally define entities, relations, dimensions, etc 32 / 38

33 Conclusion and perspectives Examples Propagation Data: Facebook and Twitter streaming announcements Question: How does it propagate into audience? Network dynamics & Popularity Data: List of IRC users logged in and watching a stream Question: are spectators structured into (evolving) sub-communities? Question: Can we translate spectator moving from a stream to another into popularity? 33 / 38

34 Conclusion and perspectives Examples Popularity: a point of view, depends on several factors Data: Twitch audience, chat session (sentiment analysis) Data: Forum fan-club, e.g. TeamLiquid.net Data: Official season ranking Data: Records of ladder games, e.g. A won against B on day C Question: How/can Skylines determine best players? Question: Can we early predict rising/dying stars? Personal recommendation Data: Twitch data Question: How to recommend an interesting and unknown stream for a spectator? 34 / 38

35 Conclusion and perspectives Examples Facebook, tweets, IRC events, etc. NLP, sentiment analysis (each game has a specific vocabulary) Graph-mining, network analysis 35 / 38

36 Conclusion and perspectives Examples Artificial Intelligence Abstracting (very!) noisy series of events, without knowing the game state that remains to be approximated 36 / 38

37 Conclusion and perspectives Examples 37 / 38

38 Conclusion and perspectives Thank you! All datasets used for this article are available Other datasets 38 / 38