Between an Arena and a Sports Bar: Online Chats of eSports Spectators - arXiv
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Between an Arena and a Sports Bar: Online Chats of
eSports Spectators
Ilya Musabirov Denis Bulygin
National Research University National Research University
Higher School of Economics Higher School of Economics, Russia,
Russia Uppsala University, Sweden
ilya@musabirov.info
arXiv:1801.02862v1 [cs.HC] 9 Jan 2018
Paul Okopny Ksenia Konstantinova
University of Bergen National Research University
Norway Higher School of Economics
Russia
ABSTRACT 1 INTRODUCTION
ESports tournaments, such as Dota 2’s The International Watching others play video games via streaming services
(TI), attract millions of spectators to watch broadcasts on is an increasingly popular practice. During eSports tourna-
online streaming platforms, to communicate, and to share ments, thousands of spectators gather in a virtual space to
their experience and emotions. Unlike traditional streams, cheer for their favourite teams and players, watch games,
tournament broadcasts lack a streamer figure to which spec- and share emotions with fellow fans.
tators can appeal directly. Using topic modelling and cross- Massive chats, which involve thousands of users, are fast
correlation analysis of more than three million messages and “loud”, with a rapid flow of text messages making it
from 86 games of TI7, we uncover main topical and temporal difficult to engage in a meaningful conversation [10] lead-
patterns of communication. First, we disentangle contextual ing researchers to treat some of these message cascades as
meanings of emotes and memes, which play a salient role in anti-social [16]. On the other hand, Ford et al. underline the
communication, and show a meta-topics semantic map of meaningful and valuable nature of this “crowd speak“ for
streaming slang. Second, our analysis shows a prevalence of establishing massive chat coherence. During key moments
the event-driven game communication during tournament of the game, spectators fill the chat with cheering, memes,
broadcasts and particular topics associated with the event and emotes resembling a “roaring” sports arena [10].
peaks. Third, we show that “copypasta” cascades and other In contrast to personal game streams, massive eSports
related practices, while occupying a significant share of mes- tournament broadcasts reduce the streamer’s figure to a mere
sages, are strongly associated with periods of lower in-game technical role and limit spectators’ opportunity to appeal
activity. Based on the analysis, we propose design ideas to directly to the players.
support different modes of spectators’ communication. This limitation dictates a one-way link between what oc-
curs between the screen and the chat suggesting the need for
CCS CONCEPTS a detailed analysis of the game-event-driven nature of chats
• Human-centered computing → Empirical studies in in tournament settings, which is less apparent in previous
collaborative and social computing; • Information sys- research on different game streaming environments [10].
tems → Chat; In this work, we perform this analysis by applying a com-
bination of topic modelling and time series cross-correlation
to chat and game event logs of the largest Dota 2 tournament,
KEYWORDS
The International 7.
Streaming, eSports, Dota 2, Twitch.tv Our contribution is twofold. First, we show thematic con-
tents of live streaming chats. We build a topic model of live
streaming chat content and show the association between
Preprint, December 2017, Preprint topics’ messages and in-game events.
2018. This is the author’s version of the work. It is posted here for your Second, we introduce the sports bar/club metaphor and
personal use. Not for redistribution. The definitive Version of Record was propose the consideration of this dual nature of massive
published in Proceedings of Preprint, December 2017, https://doi.org/10.1145/ streaming chats allowing users to participate in both “roar-
nnnnnnn.nnnnnnn.
ing” and “talking” by providing a means to manually andPreprint, December 2017, Preprint I. Musabirov et al.
automatically switch between these two modes. For example, of a stadium through the presence of the strangers, commu-
as at a sports bar, one can sit at a table and have a meaningful nity reactions to game events, and additional interactions
conversation with a group of friends while the surrounding such as betting, making acquaintances, or socialising[6]. This
crowd is roaring. At any moment, one can join the crowd in experience can be explained via an intra-audience effect [4]
cheering and switch back to the conversation with friends. where the presence of co-spectators and their vocal response
to in-game events supply informational cues to an individual.
2 BACKGROUND: DOTA 2, TOURNAMENTS, AND These cues influence the formation of normative behaviour
STREAMING [3, 18].
Dota 2 is a free-to-play multiplayer online battle arena video ESports, in turn, are computer-mediated. Thus, there is no
game in which two teams of five players participate. In addi- physical sports arena for fans to visit, as is even the case of an
tion to being a game, Dota 2 is an eSports discipline featuring open tournament, which is organised at a physical location,
tournaments and cups, which attract millions of people to with a monitor remaining as s a barrier [9]. Fans gather
watch. online in forums, social networks, and streaming platforms
The gameplay of Dota 2 consists of two phases. First, is to participate in an event and share experiences [11].
the “picks” phase, in which players choose characters to play. Hamilton et al. in [10] analysed streaming chats and found
Second, is the game itself in which players operate their that a “waterfall of text, “ which is typical for massive chats,
characters. The most common two objectives in the game can disrupt a meaningful conversation within the audience
are to kill enemy characters and destroy buildings. or between the audience and a streamer. Seering et al. in
Dota 2 tournaments resemble real-world sports competi- their analysis [16] concluded that the practice of copying
tions. After qualifications, the best teams battle each other and pasting messages is anti-social and usually senseless as
through in-game sessions. Usually, teams compete for prizes viewers create “long, often nonsensical messages with many
provided by the event organisers and sponsors. Dota 2 tourna- emotes, “ thus flooding the chat. Emotes are often employed
ments are streamed via different platforms, including Twitch.tv. in online chats to convey emotions which otherwise could
Spectators on Twitch can communicate with each other and not be expressed briefly and accurately in the text form.
with a streamer using the built-in chat interface. Emotes require only a few keystrokes or mouse clicks to
During the tournament, the game stream is accompanied write allowing users to send messages rapidly [20].
by commentators who describe what happens in the game, Both [10] and [7] acknowledged that the speed with which
share their feelings, analyse the situations, and predict the new chat messages appear is a problem. People engaged
outcome of the game. in communication have difficulty reading and responding
A professional production team supports the event by to incoming messages, which is characteristic of large and
operating cameras in the game (so-called cameraman), pro- massive stream chats [10].
viding the venue, and maintaining the broadcast. However, These communication “breakdowns“ are also associated
unlike traditional streams, there is no streamer to respond with in-game events. During exciting moments, spectators
to the chat and communicate with the audience. The analyst can share their emotions and remind themselves they are
desk is a crew of professional eSports commentators and an- part of one group [10]. Recktenwald in [14] refers to these
alytic specialists. Unlike commentators, analysts appear on a sudden bursts of text as “pivoting“, in which “participants
broadcast during breaks between games to discuss previous create a local meaning for the game event.“
games or offer predictions about future games. Sometimes, Ford et al. in [7] explore the ways spectators build “crowd
the analyst desk includes an invited person, which was the speak,“ a “distinctive form of communication“ which sup-
case during TI7 where professional analysts and popular ports massive chats with thousands of users. Ford et al. em-
players participated. phasise that these practices make the massive chat coherent.
Thus, it is “not experienced as a breakdown, overload, or
another difficulty“ but as a meaningful medium.
3 RELATED WORK
Sports spectatorship is an inherently social activity as shar- 4 DATA AND METHODS
ing enriches spectators’ experiences [15]. Live sports co- In this paper, we focus on the analysis of chat messages that
spectatorship in a public place, e.g. a pub or bar, is a “ritual are connected to broadcast- and game-related events. Us-
practice“ performed together with “other like-minded fans.“ ing the Chatty 1 application, we parsed the stream channel
[19] (cited in [8]). This practice can involve an interaction dota2ti, which was used during The International 7.We gath-
between viewers and the overall feeling of a crowd, and may ered more than three million messages from approximately
even result in the development of a new community. Thus,
watching sports tournaments in bars can provide an illusion 1 chatty.github.io/Between an Arena and a Sports Bar: Online Chats of eSports Spectators Preprint, December 2017, Preprint
180 000 viewers. On average for this class of tournament, After computing cross-correlation profiles, we find clus-
there can be 22 000 messages submitted by 6 000 unique ters of topics similar in their relation to game event occur-
users per game. We enriched the message-related informa- rences. We clusterized vectors of cross-correlation coeffi-
tion with in-game event data, e.g., a destruction of a building cients for different topics using Spearman’s correlation be-
or death of a character, which we acquired by parsing the tween them as a measure of similarity, i.e. two topics with a
Dotabuff.com website and employing the Open Dota 2 API. high correlation of cross-correlation profiles are close in our
We utilised the probabilistic topic modelling algorithm, clustering. To select the number of clusters, we used both
Latent Dirichlet Allocation (LDA)[1], to analyse the contents visual-guided inspection of the dendrogram and silhouette
of the chat. LDA takes a collection of text documents as input metrics for cluster analysis, resulting in five clusters. We next
and derives a pre-set number of topics – sets of words, which analysed topic content and interpreted each topic according
often co-occur in the same documents. LDA associates each to the most common comprising words. We labelled clusters
text document to all the topics with different probabilities. based on topic interpretation.
We classified a topic as “most popular“ if it had the highest
probability (out of all topics) in the given text document. 5 ANALYSIS AND RESULTS
Thus, a chat is characterised by a numeric sequence in which Previous research suggested that communication in mas-
each element represents the dominating topic ID in the given sive Twitch.tv chats is event-driven in nature. To explore
time window. the connection between game events and the topic structure
During preliminary analysis, we found that chat messages of messages, we applied cross-correlation analysis to time
in our corpora are often one-word, which is not suitable for a series of game events and message counts per topic, result-
proper LDA analysis. We split chat messages into slots, each ing in clusters based on different temporal patterns of game
one covering a seven-second time window, and joined mes- event-chat reaction connections. The resulting clusters were
sages within these windows into larger text documents. The labelled as: emotional response, stream content, profes-
window size was preferred as it allowed us to compile text sional scene, boredom and copypasta, and analyst desk.
documents long enough for analysis (mean = 7.99 messages Based on the clustering results, we calculated the average
per second, SD = 7, max = 115) and short enough to produce percentage of messages belonging to each cluster for each
distinctive topics. game.
We fitted models with a number of topics ranging between Association between these clusters and the topic model
30 and 100. We chose the 100-topic model because the pro- captures the differences in a context for similar topics and
duced topics were quite heterogeneous in terms of thematic particular tokens (words, emojis), which is an especially for-
content and interpretable enough to be labelled. tunate feature for an emote-based contextually rich com-
We applied cross-correlation analysis to establish the rela- munication of streaming chats. For example, topics in the
tion between in-game events and prevailing topics in the chat Analyst Desk cluster contain a discussion of themes and in-
[2]. For each topic, we compared its appearance as the most formation brought to viewers during breaks between games
popular topic and the number of in-game events (typically, it by analyst desk anchors and guests. The stream Content clus-
was 0 or 1) in the given time frame. We tested resulting time- ter contains topics devoted to the technical details and the
series with the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) game setup of the ongoing stream. The Professional Scene
test to ensure they are stationary [12], and as a result, for cluster contains topics connected to the professional play-
each of 100 topics, we obtained a vector of correlation coeffi- ers, teams, and brands within and outside the context of the
cients between two series for each time lag (Figure 1-5). current game.
Cross-correlation tests are applied if two variables cor-
relate with a lag in some range. Thus, for each topic, we Cluster Number of topics Average % in the game (SD)
obtained a time pattern of its relation to the in-game events. Emotional 11 17% (9%)
This time pattern represents the overall presence of the given response
topic in the chat before (negative lag), during (zero lag), and Stream 18 18% (13%)
content
after (positive lag) the event. Considering in our case that Professional 14 16% (16%)
chat message content is unlikely to influence the game, we scene
treat the possible connection as a one-way link, i.e. if an in- Boredom and 30 30% (16%)
game event is followed or preceded by a burst of particular copypasta
topic messages. Analyst desk 27 17% (16%)
Table 1: Meta-topics and distribution per gamePreprint, December 2017, Preprint I. Musabirov et al.
Emotional response terrain BlessRNG,“ “TERRAIN WutFace,“ etc. Eventually, in
The Emotional Response cluster consists of topics containing response to these messages, the terrain was changed to the
responses to in-game and related events. For example, specta- original.
tors might comment on the game balance, a character death, Messages associated with topics in this cluster also con-
building destruction, or cameraman missing an interesting tain references to Dota 2 memes and terms. For instance,
in-game event. Messages on these topics are short and often the message “1k 2k 3k 4k 5k 6k 7k 8k 9k 10k only the cho-
contain only one word, emote, or abbreviation, e.g. “gg”, “ez”, sen one can hold his true MMR“3 uncovers the mechanism
“pogchamp”, or “kreygasm”. These messages are not always of ranking in Dota 2, which is based on gaining or losing
cheering as they sometimes depict mockery toward a player MMR-score depending on a game outcome. Another notable
or team due to poor performance, .e.g., “322 LUL.“ theme in this cluster is the first human-vs-AI game in which
Most time patterns for topics in this cluster show these one of the most famous professional Dota 2 players, Dendi,
“reactions” are tightly connected with in-game events as lost to the AI-player: “dendi lost against bot hahahahaha“,
topics emerge in the chat soon before the event and dis- “OPENAI MMR BOOST“, “MrDestructoid TIME MrDestructoid
appear shortly after (see Figure 1). The only topic which TO MrDestructoid WATCH MrDestructoid MY MrDestructoid
does not fit this pattern is related to complaints of camera- BOY MrDestructoid OPENAI MrDestructoid ENSLAVE MrDe-
men failing to move the in-game camera toward an in-game structoid THE MrDestructoid HUMAN MrDestructoid RACE
event: “TTours DAMN TTours IT TTours FEELS TTours GOOD MrDestructoid.“
TTours TO TTours MISS TTours ALL TTours THE TTours AC- Regarding time patterns, topics in this cluster present in
TION TTours.“ This type of topic is present before, during, the chat before and after, but not during in-game events
and after an in-game event. We suggest that spectators were (see Fig. ??). Thus, we can state that, in general, spectators
aware of the in-game situation and knew when something tend to comment stream content during the whole stream
interesting happened, even if it did not appear on the screen. except for small time windows when in-game events grab
Thus, they began to complain to cameramen before the actual their attention causing other topics to prevail.
event and continued afterwards.
Figure 2: Stream content time pattern example
Figure 1: Emotional response time pattern example
Professional Scene
Stream content
The Professional cluster contains topics related to Dota 2’s
Topics in the Stream cluster are related to the current stream- professional scene. Spectators discussed topics such as fa-
ing content. Spectators reacted to what or who they saw on mous professional teams and players (“liquid gh miracle con-
screen, including players (e.g. “Time to watch my boy Ar- trol“), a confrontation between Chinese and Western teams
tour win some Dotes FeelsGoodMan“) and teams (e.g., “Hey during the tournament (“budstar liquid go lets china lfy“), or
Virtus Pro, Vlad Putin here. Your president. Doing great! the all-starts match where two teams are comprised of play-
Just a reminder: There are 5 spots open in a Siberian Prison ers from different organizations (“worst ti allstars“). Also,
waiting for you if you don’t win. No pressure! See you guys it includes topics about past incidents with specific players,
soon!¡‘). Spectators also commented on new map terrains, “rip monitor biblethump secret sobayed,“ referring to a player
which they did not appreciate2 : “BlessRNG pls use default from the team, Secret, who broke his monitor in anger4 .
2 https://compete.kotaku.com/the-international-7-reverts-to-default-dota- 3 Matchmaking Rating, Dota 2’s players skill level estimate.
2-map-due-t-1797504357 4 https://www.youtube.com/watch?v=lLjCvWxaONkBetween an Arena and a Sports Bar: Online Chats of eSports Spectators Preprint, December 2017, Preprint
This cluster also included notions of some particular teams.
A notable example is Team Liquid, which gained the status
of the “last stand” between English-speaking fans. In the
last two days of the tournament, Team Liquid fought against
three Chinese teams and won the tournament. This situation
evoked a burst of team mentions and gave rise to topics on
the China-West confrontation.
Before in-game events, topics within this cluster rarely
appeared. However, after the event, they appeared more
often (see Figure 3). We suggest that after interesting in-game
events, spectators started cheering for teams and favourite
players or just posted team- and player-related memes in the Figure 4: Boredom and copypasta time pattern example
chat.
Analyst desk
Topics in the analyst cluster include names of people partic-
ipating in the desk (“ccnc blitz machine“) or players elimi-
nated from the tournament (“kkona keepo ba mason[player]
[b]ulba[player] dc[team]“). It is not surprising that the time
pattern for this cluster does not feature changes associated
with in-game events, and is negatively correlated (see Figure
5). However, this cluster holds a significant share of messages
in the chat suggesting that spectators still discuss related
topics during the game, e.g., during the “picks” stage when
players choose the characters for the game.
Figure 3: Professional scene time pattern example
Boredom and Copypasta
The cluster for boredom- and copypasta-related topics con-
tained responses from spectators who conveyed their bore-
dom in various ways. For instance, messages such as “90
min game ResidentSleeper“ or “SLEEP TEST IF YOU TOUCH
THE BED , GO TO SLEEP ResidentSleeper“ could be submit-
ted for expressing ones’ tiredness of the long game without
interesting recent events.
Spectators sometimes tried to launch a flashmob on a chat
Figure 5: Analyst desk time pattern example
for the sake of self-entertainment. For example, they posted a
message “ Lets build a ladder “ to provoke others
to copy and paste the message creating a visual “ladder” on 6 DISCUSSION
the screen, which is considered classic Twitch copypasta 5 .
Keywords for the topics in this cluster are not connected to We demonstrate the overall thematic structure of massive
Dota 2 or personalities (e.g., players) in the stream. eSport events live streaming chats and show how differ-
The time pattern for topics in this cluster follows a rise ent topics are evoked and discouraged by in-game events.
before an interesting in-game event, and start declining half While hinted by previous works and often considered as
a minute before anything occurs (see Figure 4). We assume common knowledge, to our understanding, this phenome-
that spectators anticipate an interesting and exciting in-game non has never been supported by statistical evidence until
event, so they stop posting boredom-related copypasta. Dur- now.
ing and after the event, the level of these topics in the chat The event-driven and sentiment sharing nature of massive
remains low. tournaments’ chats suggests a rethinking of its design goals.
As Hamilton et al. noted in [10], sudden bursts of emotes
5 https://www.twitchquotes.com/copypastas/1452
and copypasta disrupt meaningful conversations betweenPreprint, December 2017, Preprint I. Musabirov et al.
spectators, and we demonstrated how these bursts are con- https://www.google.com/books?hl=ru&lr=&id=DJ_lBwAAQBAJ&
nected to in-game events. Hamilton et al. suggest dividing oi=fnd&pg=PR7&dq=brockwell+2013+time&ots=AcqDfG0Jh-&sig=
jYfYsuKVU599_M-i4qpW8MayeB0
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