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Watch me playing, I am a professionalA first study on video game live streaming
M. Kaytoue1, A. Silva1, L. Cerf1, W. Meira Jr.1, C. Raıssi2
1 2
Belo Horizonte – Brazil Nancy – France
Mining Social Network Dynamics @ WWW 2012Lyon (France) - 16 April, 2012.
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
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Events and tournaments
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Social TV
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ContributionStarting 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
Quantitatively: audience, content length, etc.
Qualitatively: What games? Where? etc.
Early prediction of the audience
Ranking most popular professional gamers
Findings
Important for E-Sport actors – With nice perspectives of research
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Outline
1 A first characterization of the E-Sport community
2 Predicting stream popularity
3 Ranking streamers
4 Conclusion and perspectives
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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 descriptiondate The date of crawling of the tuplelogin Unique identifier of a user/streamergame The game or topic of the stream
description A text description of the streamcount The number of viewers/spectators
watching the stream at a given time
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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 27,120,337Length streamed 215.3 yearsLength watched 9,622.4 years
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A first characterization of the E-Sport community
Views along the weeks (When?)
10000
20000
30000
40000
50000
60000
70000
Sun Mon Tue Wed Thu Fri Sat Sun 400
600
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1400
1600
avg n
b o
f vie
wers
avg n
b o
f str
eam
ers
viewersstreamers
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A first characterization of the E-Sport community
Geographic distribution (Where?)
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A first characterization of the E-Sport community
Top 20 most popular games (What?)
Game Audience ReleaseStarCraft II 35.05% July 2010
Heroes of Newerth 8.89% May 2010League of Legends 8.19% Oct. 2009World of Warcraft 6.24% Nov. 2004Call of Duty: BO 3.88% Nov. 2010Street fighter 4 3.26% Apr. 2010
Star Wars (TOR) 2.98% Dec 2011The Elder Scrolls 2.36% Nov. 2011
MineCraft 2.03% Nov. 2011Rage 1.98% Oct. 2011
Marvel vs. Capcom 3 1.67% Feb. 2011Dota 2 (beta) 1.55% Sep. 2011Battlefield 3 1.39% Oct. 2011Warcraft III 1.22% July 2002Halo: Reach 1.20% Sept. 2010Mario Kart 7 1.18% Dec. 2011Dark Souls 1.10% Oct. 2011Zelda SS 1.05% Nov 2011
Gears of War 3 0.93% Sept. 2011Counter-Strike S 0.89 % Nov. 2004
Others 12.95%
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A first characterization of the E-Sport community
Local game popularity (What?)%
of daily
audie
nce
Time (days)
BattlefieldCall of DutyDark SoulsDotaGears of WarCounter-StrikeHaloLeague of LegendsMarvel vs. CapcomMineCraftRageStarcraft IIStar WarsStreet FighterMario’sThe Elder ScrollsWarcraft IIIWorld of WarcraftZeldaHeroes of Newerth
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A first characterization of the E-Sport community
Major E-Sport events (What?)
20000
30000
40000
50000
60000
70000
Oct. 11 Nov. 11 Dec. 11 Jan. 12
IEM N-YMLG Orlando
IGN Pro LeagueDreamHack Winter
Blizzard Cup
Home Story CupNASL S2 Finals
NE League S2 Grand Finals12 hours for charity
#views
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A first characterization of the E-Sport community
Stream and Streamer characteristics
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(b) StreamerDuration of streams and aggregate duration of streamers
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(c) Stream
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(d) StreamerStream and streamer audience
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1 A first characterization of the E-Sport community
2 Predicting stream popularity
3 Ranking streamers
4 Conclusion and perspectives
Predicting stream popularity
MotivationCurrent Twitch recommendation strategy
New and interesting streams may take too long (or even never)to become visible
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Predicting stream popularity
Motivation
Streaming sessions have a highly skewed popularity distribution,short duration, and slow popularity evolution.
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hours since the beginning of a session
average session for the top-100 streamers
(g)
Stream popularity, duration and popularity evolution
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Predicting stream popularity
Idea
Predicting popularity using initial popularity records
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po
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larity
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popularity after ti minutes
(h) ti = 5 min.
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popularity after ti minutes
(i) ti = 30 min.
Correlation between stream popularity after ti minutes and 1 hour
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Predicting stream popularity
Correlation Varying ti
Correlation between popularity after ti minutes and 1 hour
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corr
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mean s
quare
d e
rror
ti (min)
corr.ε
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Predicting stream popularity
Prediction Model
Model
log(pop(tf )) = β0 + β1 log(pop(ti )) + ε
Predicted vs. actual (based on popularity after ti minutes)
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predicted popularity after 1 hour
(j) ti = 5 min.
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predicted popularity after 1 hour
(k) ti = 30 min.
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Predicting stream popularity
MSE Varying ti
MSE for different values of ti (minutes)
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quare
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corr.ε
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1 A first characterization of the E-Sport community
2 Predicting stream popularity
3 Ranking streamers
4 Conclusion and perspectives
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?
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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. 10 19:00, WhiteRa is preferred to EG.IdrA. They arenot comparable with Mill.Stephano.
crawl time Oct. 29 16:30 Oct. 29 16:35 Nov. 10 19:00EG.IdrA 1950 6350 1020
Mill.Stephano 4450 3680 -WhiteRa 935 2301 4535
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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 votersand apply a Condorcet method that is known to be good forranking: Maximum Majority Voting.
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Ranking streamers
Ranking the pairs of streamers
Three criteria with the following precedence:
c1 How often the first streamer is preferred to thesecond;
c2 How often they have the exact same popularity;
c3 How often they broadcast at the same time.
c1 c2 c3
(EG.IdrA,WhiteRa) 0.9615 0 156(EG.IdrA,Mill.Stephano) 0.9 0 20(WhiteRa,Mill.Stephano) 0.7829 0 175
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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 cycleinvolving it;
3 Remove those involved in a cycle;
4 Go to 1.
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Ranking streamers
Resulting graph
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Ranking streamers
Results with Top-100 streamers
Focusing on eight StarCraft II players
Web poll (# votes) Simple ranking (pos.) CondorcetWhiteRa (11,112) EG.IdrA (20) EG.IdrA
Mill.Stephano (9,192) WhiteRa (21) Mill.StephanoEG.IdrA (6,746) Liquid’Ret (31) EG.HuKEG.HuK (5,050) EG.HuK (32) WhiteRa
Liquid‘HerO (2,160) Mill.Stephano (33) Liquid‘HerOLiquid’Sheth (846) Liquid‘HerO (53) QxG.SaSe
QxG.SaSe (833) Liquid’Sheth (72) Liquid’ShethLiquid’Ret (684) QxG.SaSe (91) Liquid’Ret
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1 A first characterization of the E-Sport community
2 Predicting stream popularity
3 Ranking streamers
4 Conclusion and perspectives
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, socialsciences, etc.)
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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
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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 toanother into popularity?
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Conclusion and perspectives
ExamplesPopularity: 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 unknownstream for a spectator?
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Conclusion and perspectives
ExamplesFacebook, tweets, IRC events, etc.
NLP, sentiment analysis (each game has a specific vocabulary)
Graph-mining, network analysis
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Conclusion and perspectives
Examples
Artificial Intelligence
Abstracting (very!) noisy series of events, without knowing thegame state that remains to be approximated
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Conclusion and perspectives
Examples
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Conclusion and perspectives
Thank you!All datasets used for this article are available
http://homepages.dcc.ufmg.br/~kaytoue/
Other datasets
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