Focus Entertainment<EMAIL_ADDRESS>Focus Entertainment,
<EMAIL_ADDRESS>Nicolas Grelier, Stéphane Kaufmann
<ccs2012> <concept> <concept_id>10010405.10010476.10010477</concept_id>
<concept_desc>Applied computing Publishing</concept_desc>
<concept_significance>500</concept_significance> </concept> </ccs2012>
[500]Applied computing Publishing John Q. Open and Joan R. Access 2 42nd
Conference on Very Important Topics (CVIT 2016) CVIT 2016 CVIT 2016 December
24–27, 2016 Little Whinging, United Kingdom 42 23

# Data-Driven Classifications of Video Game Vocabulary

Nicolas Grelier  Stéphane Kaufmann

###### Abstract

As a novel and fast-changing field, the video game industry does not have a
fixed and well-defined vocabulary. In particular, game genres are of interest:
No two experts seem to agree on what they are and how they relate to each
other. We use the user-generated tags of the video game digital distribution
service Steam to better understand how players think about games. We
investigate what they consider to be genres, what comes first to their minds
when describing a game, and more generally what words do they use and how
those words relate to each other. Our method is data-driven as we consider for
each game on Steam how many players assigned each tag to it. We introduce a
new metric, the priority of a Steam tag, that we find interesting in itself.
This allows us to create taxonomies and meronomies of some of the Steam tags.
In particular, in addition to providing a list of game genres, we distinguish
what tags are essential or not for describing games according to players.
Furthermore, we provide a small group of tags that summarise all information
contained in the Steam tags.

###### keywords:

taxonomy, meronomy, video game genres, Steam tags, game analytics

###### category:

##  1 Introduction

In the last two decades, game analytics has gained a lot of interest [28].
According to Su, Backlund and Engström, “game analytics is the process of
identifying and communicating meaningful patterns that can be used for game
decision making” [28]. They present a classification of game analytics into
four groups: game development analytics, game publishing analytics, game
distribution channel analytics and game player analytics. In this paper, we
focus on game player analytics. Our aim is to understand the vocabulary used
by players to speak about video games. More precisely, we are interested in
how players describe games: what are the most important words, what are the
most used words, and so on.

In our study, we rely exclusively on Steam tags: groups of words that players
can freely assign to games. Some examples are _Strategy_ , _Card Battler_ ,
_Agriculture_ , _Ninja_ , _Difficult_ and _Free to Play_. In total, there
exist $427$ Steam tags. For a more comprehensive explanation of Steam tags,
please refer to Subsection 1.4.

###  1.1 From beliefs to facts

In [6], Drachen et al. observe that game analytics “is in its infancy and the
available knowledge is heavily fragmented, not the least due to Game Analytics
interdisciplinary nature and the lack of knowledge sharing between academia
and industry”. As industry researchers, the goal of our paper is to build
bridges with academia. Drachen et al. add that “the root cause of these
problems is the lack of a framework for organizing current knowledge and
prioritising interesting problems” [6]. They introduce the concept of
_stylised facts_ for mobile game analytics (although we believe that the
concept is essential to game analytics in general) in order to better connect
industry and academia.

However, they argue that the available knowledge and data are too fragmented
and incomplete to establish stylised fact. Thus, they introduce two “proto-
stylised fact concepts which describe situations where less empirical
validation is available: _beliefs_ and _hypothetical stylised facts_ ” [6].
The aim is to “provide a roadmap for structuring current knowledge and
building towards a situation where stylised facts can be generated and
validated”. Beliefs are statements supported by virtually no empirical
evidence, whereas hypothetical stylised facts are supported by some empirical
evidence. However, they are still not stylised facts, since “while there maybe
some available empirical evidence supporting these hypothetical stylised
facts, it is clearly not enough to rigorously, generally, support them”.
Establishing hypothetical stylised facts help in obtaining stylised fact,
which is essential for the research field (see [6] for a detailed
argumentation).

In this paper, we provide empirical evidence for several hypothetical stylised
facts. Some of which we introduce, while others were already beliefs in the
industry, for instance that genre is one of the most crucial pieces of
information when describing a video game. We aim at presenting our findings in
a quantitative manner, and to provide an interpretation of the metrics we
introduce.

###  1.2 Taxonomies and meronomies

In Section 3, we present a data-driven taxonomy of all Steam tags. This
consists in doing a hierarchical classification, where we divide the tags into
categories and subcategories. Those are referred to as “taxa”. Each tag is put
into one of the taxa of lower rank, which are themselves grouped into taxa of
higher rank. Our taxonomy has a tree-like structure, but others may be more
network-like, with one child having several parents.

We also provide in Section 4 two data-driven meronomies of some Steam tags. A
meronomy differs from a taxonomy as it deals with part-whole relationships,
whereas a taxonomy is a classification of discrete elements. Using
mathematical notation, a meronomy can be thought of as a partially ordered set
where the elements are sets and the relation considered is inclusion (we have
$A\leq B$ if $A$ is a subset of $B$). Here, our aim is to better understand
how some tags relate to each other. Using a data-driven approach, we establish
some relation between tags, for instance that _2D Platformer_ is a subtag of
_Platformer_ , and that _Sailing_ is a subtag of _Naval_.

###  1.3 A focus on game genres

A part of our study concerns game genres. In particular, we are interested in
finding a complete list of game genres, and to understand how much genre
matters to players. Arsenault observes that many websites allow the user to
search through video games while filtering by genre [3]. However, no two
websites seem to agree on what the list of genres is. For instance, he
mentions GameSpot’s system (at the time of his writing) that does a taxonomy
of the genres: genres are subdivided into subgenres, thus creating $157$
categories. He gives some examples:

  * •

_Action_ $\rightarrow$ _Shooter_ $\rightarrow$ _First-Person_ $\rightarrow$
_Fantasy_ ,

  * •

_Miscellaneous_ $\rightarrow$ _Puzzle_ $\rightarrow$ _Action_ ,

  * •

_Strategy_ $\rightarrow$ _Real-Time_ $\rightarrow$ _Fantasy_.

Arsenault notes that “while every category is unique and independent in
principle, some of the lower-tiered descriptors appear as sub-branches of
multiple genres” [3]. He adds that “the levels or branches themselves are not
named, which means there is no basis on which to compare them”, and “comparing
all 2nd-level classes would probably amount to comparing apples to oranges to
shoes to faith”. He concludes: “One thing that these different taxonomies
highlight is the fluidity and impreciseness of the concept of genre itself,
and how it is used in actually describing games”. The same issues are noted by
Heintz and Law [10]:

  * •

Genres are not clearly or consistently defined,

  * •

The relation between genres is unknown,

  * •

Definitions are based on completely different aspects,

  * •

Different sources use different sets of genres.

Steam provides a set of tools for developers and publishers named Steamworks.
They provide a taxonomy of Steam tags [27]. Tags are divided into several
taxa: Genres, Visual Properties, Themes & Moods, Features, Players, Other
Tags, Software, Assessments, Ratings etc, Hardware/Input and Funding etc. The
taxon Genres, which is our main focus here, is split into three taxa: Super-
Genre, Genre and Sub-Genre. This taxonomy is to be understood with the broad
definition of a taxonomy, in which a child may have several parents. For
instance, _Heist_ is a sub-genre and also a theme. _Flight_ is a sub-genre and
also a feature. _Experimental_ is a super-genre and a genre. One objective of
this paper is to challenge Steam’s taxonomy and investigate whether players
concur with it.

Classifying video games into genres is valuable for studying and comparing
meaningful categories. For instance, in the literature we can find studies on
what genres are the most successful [8, 22] (and also [24] although the study
is about board games instead of video games), how considering games from the
same genre is important for deciding on a release date [7, 9], how genre
relates to players motivations, gender and localisation [16, 23, 29, 30], or
what are their usability profiles [20]. All these studies use a list of
genres, but do not discuss much (or not at all) whether it makes sense to
discuss about genres, whether the list is comprehensive, and so on.

A more industry-oriented reason for establishing a comprehensive list of game
genres, along with definitions and relationships between the genres, comes
from trailer making. Lieu, who was involved in the creation of the trailers of
_Half-Life: Alyx_ , _Among Us_ and _The Long Dark_ among others, asserts that
a trailer of a video game must first clearly establish its genre [14].
Therefore, it is crucial to have an exact understanding of what a genre is.
Moreover, Lieu details what a trailer should show according to the game genre
[13]. Similarly, Carless, who is the founder of GameDiscoverCo, a video game
discoverability consultancy firm, believes that game genre is “what [their]
users want to see information about”. In a database meant for people working
in the video game industry, Carless “care[s] most that the main genres are in
_Genre_ and are easy to see and sort” ([Carless, personal communication]).

###  1.4 The Steam tags

In this paper, we rely solely on Steam data. More precisely, we study the
user-generated tags on Steam. There exists a total of $427$ tags that players
have assigned to the $50757$ games in Steam. Those tags, consisting of groups
of words or acronyms like _Action RPG_ , _MOBA_ or _Open World_ , are freely
assigned to games by players, who can also invent new ones.

There exist many papers relying on Steam data, for instance Steam players
reviews [15, 19, 21, 26, 33] or Steam user accounts [4, 5, 17, 25]. Some of
these studies use Steam tags, but there are only used as tools for filtering
games, and are not the topic of study. To the best of our knowledge, there are
only three papers that truly study Steam tags per se. First, Windleharth et
al. do in [32] a conceptual analysis of Steam tags. They discuss and classify
Steam tags into categories, some containing many elements like Gameplay genre,
and some containing only one elements like Relationships which contains only
the tag _Remake_. Windleharth et al. rely on their own expertise to determine
what tags are genre tags. In this paper, we use a data-driven approach with
the same goal in mind. A second paper that studies Steam tags is [12], in
which Li and Zhang examine how genre tags are related to each other. Finally,
in [11], Li uses correlation between Steam tags related to gameplay to obtain
a list of $29$ “factors”. Those factors are not necessarily Steam tags but
rather groups of highly correlated Steam tags. For instance, the factor
_Rogue_ contains the Steam tags _Dungeon Crawler_ , _Rogue-like_ and _Rogue-
lite_. We compare our method to theirs in Section 2, and argue why ours is
more reliable. We also compare our results with theirs in Sections 3 and 4.

All players are free to assign to games some of the already-existing Steam
tags, or even to add new ones. For a game, Steam shows “tags that were applied
to the product by the most users”. Steam shows at most $20$ tags for any game,
sorted by decreasing order of number of players who assigned the tag. The
exact number of players who assigned a tag is not directly shown on Steam, but
can be found in the source code of the page. To obtain the data of how many
players assigned a tag to a game, we used SteamSpy’s API. There are $13848$
games with $20$ tags shown on Steam, and therefore $50757-13848=36909$ games
with fewer than $20$ tags. One can make the experience of adding a new tag to
a game with fewer than $20$ tags, and have the surprise of not having their
newly added tag shown in Steam. The reason for that is that Steam only shows
tags that were assigned by at least $5$ players. Steam does not explicitly
mention this, but we could check it by going over all games. Since anyone can
add any set of nonsensical words as a tag, this is probably for the best.

It is important to note that Steam does not offer a definition of the tags,
one sufficient reason being that tags are freely added and invented by players
(as can been seen in the inconsistent capitalisation and hyphenation of the
tags). Therefore, players interpret by themselves the meanings of the tags.
For our study, this comes with pros and cons. Since “genres are not clearly or
consistently defined” [10], we may, thanks to Steam tags, find consensuses
about what a given genre is. We believe that there exists some common
understanding of what, say, an adventure game is. By an equivalent of the law
of large numbers, Steam tags can help decipher what the definition is.
However, there are also cases when players are clearly referring to distinct
things while using the same tag, which perturbs our analysis. One easily
spotted example is the tag _Football_ , which sometimes refers to soccer, and
sometimes to American football. An another example is the tag _Fighting_. For
some games, it seems that the tag is used to state the genre of the games,
whereas in other games it may be used to merely inform that there is some
fighting in the game. This motivates why some tags have several parents in
Steamworks’ taxonomy [27].

Several of our results rely on computing the Pearson correlation coefficient
between tags. We point out another difficulty we face, that stems from the
nature of the database. One would expect some pairs of tags to have an
extremely low correlation (around $-1$), for instance: _2D_ and _3D_ , _First-
Person_ and _Third Person_ 111We follow Steam spelling with a hyphen in
_First-Person_ but none in _Third Person_., _Singleplayer_ and _Multiplayer_ ,
and so on. Indeed, a priori, a game is either in 2D or 3D, either has a first-
person or third person viewpoint, and is either a singleplayer or a
multiplayer game. To compute the correlation, we create a matrix with one
entry per game, and one column per tag. There is a value of $1$ if the tag is
assigned to the corresponding game, and $0$ otherwise. We compute a
correlation of $-0.17$ for _2D_ and _3D_ , $0.04$ for _First-Person_ and
_Third Person_ , and $0.10$ for _Singleplayer_ and _Multiplayer_. We
investigated what was the cause of these surprisingly high correlation
coefficients. There are games that use 2.5D or isometric representations.
Those are mixes of 2D and 3D features. We found that generally, whenever the
tag _2.5D_ or _Isometric_ is assigned to a game, the tags _2D_ and _3D_ are
also (wrongly) assigned to the game by players. This is why the correlation is
closer to $0$ than to $-1$. Concerning the viewpoint, there are many shooter
games using the third person view that change for a first-person view when the
player is aiming. In those games, we found that both tags _First-Person_ and
_Third Person_ are assigned. Similarly, if a game contains a singleplayer
campaign and a multiplayer mode, then both tags _Singleplayer_ and
_Multiplayer_ are usually assigned. In conclusion, one has to be very careful
when exploiting pairs of tags with low correlation, since one might miss a few
relevant pairs.

Steam does not provide the information of how many players assigned tags to a
given game. However, the number of players who assigned the most given tag of
a game $G$ gives us a lower bound on the number of players who assigned tags
to $G$. Using this method, we know that there are at least $15843$ games for
which at least $100$ players assigned some tags.

We said that Steam shows at most $20$ tags for a game. This is not exactly
true in the source code. Indeed, there are a few games, $235$ to be exact,
with $21$ tags. They all share a common property: The $21$st tag is _VR Only_
, and is assigned by precisely one player. This is probably a feature added by
Steam. We make the choice of removing this tag from the database, since it was
not added by real players.

###  1.5 Our contributions

In Section 2 we present a new concept: the priority of a tag for a game. We
show that this is a useful notion, for we establish as a hypothetical stylised
fact that tags with the highest priority are what players think about first
when describing the game (see Section 2 for a formal definition of priority
and a formal statement of the hypothetical stylised fact). On the other hand,
tags with low priority are what players deem interesting about the game, but
which only come later to their minds. Using this concept of priority, we give
empirical evidence in Section 3 for the following hypothetical stylised fact:

######  Hypothetical stylised fact .

Game genre is what players think about first when describing a game.

Consequently, we believe that this is also what they want to hear about first
when discovering a new game, confirming the soundness of Lieu’s practices in
trailer making [14]. On a related note, using a data-driven approach we
establish a list, as comprehensive as possible, of game genres. Our aim here
is to encompass all words that players may consider as game genres. The list
can be found in Appendix A.

We provide in Section 4 a meronomy of the tags (not necessarily about genre)
with broadest meanings. In particular, we find a set of seven tags with
broadest meanings, that we name the “capital tags”. There are four properties
that we want the capital tags to satisfy. First, they should form a rather
short list. As we have $7$ capital tags out of the $427$ Steam tags, this is
clearly satisfied. Secondly, for any game, at least one of these tags should
be assignable to the game. Thirdly, those tags should not be redundant.
Finally, any other tag should be a more refined version of some of the capital
tags. We show in Section 4 that the capital tags satisfy this three last
properties. This implies that the capital tags summarise all information
contained in Steam tags.

We provide a second meronomy in Section 4, that aims at reducing the number of
Steam tags without loosing information, by merging synonymous tags together.
We propose a method for doing so as well as finding a representing tag for
each group of merged tags.

##  2 The priority of Steam tags

In [12], Li and Zhang defines a graph that aims to accurately represent the
Steam tags. There is one vertex per tag, and two tags are connected by an edge
if they are both assigned to a same game. The edges are weighted according to
how many games have both tags assigned to them. The authors discard some edges
by using the “Narrow by tag” feature in Steam. We find the authors’ results
difficult to interpret due to their chosen methodology. First, it is not clear
what the Steam “Narrow by tag” feature does. Additionally, the authors connect
two tags $T$ and $T^{\prime}$ with an edge even if for the game $G$ to which
they are both assigned, the tag $T$ is the most assigned tag to $G$ and
$T^{\prime}$ is the least assigned tag to $G$, without discussing the
rationale behind this decision. It seems wrong to us to connect the two tags
with an edge since $T$ is apparently essential for describing $G$ whereas
$T^{\prime}$ is not so much relevant.

In [11], Li computes correlation between tags. The analysis is more precise
than in [12] since when a tag $T$ is assigned to a game, a weight is
associated to $T$. If for a given game $G$, the tag $T$ is the tag most
assigned to $G$, then $T$ obtains a weight of $20$. If $G$ has $20$ tags and
$T$ is the least assigned, it obtains a weight of $1$. More generally, the
$n$-th most assigned tag obtains a weight of $20-n+1$. We point out two
issues. First, Li does not discuss whether it makes sense to use these
weights, and how to interpret them. Secondly, the distributions of the number
of players who assigned tag varies greatly over all games. For instance, the
two most assigned tags to the game _HITMAN 2_ are _Stealth_ ($10373$ players)
and _Assassin_ ($819$ players). The ratios of the number of players for these
tags is therefore $819/10373\approx 0.08$. If we do the same with the same
with the game _Golf It!_ , we find that the most assigned tag is _Multiplayer_
($249$ players) and the second most assigned is _Mini Golf_ ($243$ players).
This time, the ratio is $243/249\approx 0.98$. With Li’s method, those ratios
would be $19/20=0.95$ for both games [11].

In this section, we define, interpret and study a new metric, that will be
used in all later sections for the taxonomies and meronomies. It is a more
refined version of what was proposed by Li [11]. For each Steam tag, we define
what we call its _priority_ for a given game.

tag | number of players | priority
---|---|---
_Adventure_ | 1000 | 1
_Puzzle_ | 750 | 0.75
_2D_ | 500 | 0.5
_Atmospheric_ | 100 | 0.1
_3D_ | 0 | 0
Table 1: A fictional example of tags assigned to a game with their priorities

######  Definition 2.1.

The priority of a tag $T$ for a given game $G$ is a score that ranges from $0$
to $1$. Let $t_{T}$ denote the number of players who assigned to the game $G$
the tag $T$. Similarly, let $t_{\max}$ denote the maximum over all tags
assigned to $G$ of the number of players who assigned the tag. The priority of
$T$ for $G$ is equal to $t_{T}/t_{\max}$. A fictional example is shown in
Table 1.

In particular, any game with at least one tag has a tag with priority $1$: the
tag that was assigned to it by the largest number of players. If a tag was not
among the $20$ most assigned tags to a given game, then its priority is $0$
(recall that Steam only shows the $20$ most assigned tags). We choose this
metric as we are interested in identifying how players think about games. Let
us consider all positive priorities over all games and tags. We compute a mean
of $0.60$, a median of $0.61$ and a standard deviation of $0.28$. This shows
that the priority contains some information, as the standard deviation is
quite high. There are two compatible reasons as to why not all tags have the
same priority for a given game $G$. First, it may be that some players assign
much fewer tags than other players. This would imply that the tags with
highest priority correspond to what players think about first when describing
$G$. The second reason would be that players do not perfectly agree on what
tags are best suited to describe $G$. This second reason would imply that the
tags with highest priority are what players agree upon for describing $G$, and
that their opinions diverge on the tags with low (but non-zero) priority. We
argue that the first reason is the most important, and present the following
hypothetical stylised fact:

######  Hypothetical stylised fact 2.2.

For a given game $G$, the tags with highest priority correspond to what
players think about first when describing $G$.

We provide empirical evidence supporting Hypothetical Stylised Fact 2.2. The
fact is hypothetical as defined in [6] because our methods for supporting it
cannot establish it for good. This could be done if we had access to a
database of the tags assignment per player, but this is not publicly
available.

(a) Histogram of the ratios of the priority of _FPS_ and _Shooter_ for the
$1867$ games whose _FPS_ priority is higher than the one of _Shooter_.

(b) Histogram of the ratios of the priority of _Online Co-Op_ and _Co-op_ for
the $1171$ games whose _Online Co-Op_ priority is higher than the one of _Co-
op_.

Figure 1: Some empirical evidence for Hypothetical Stylised Fact 2.2.

We consider two tags: _Shooter_ and _FPS_. “FPS” stands for “first-person
shooter”, and is a subgenre of shooter game [1, 31]. Therefore, anyone who
thinks that a given game $G$ is an FPS would also agree that it is a shooter
game. We consider all of the $1867$ games to which both tags _Shooter_ and
_FPS_ are assigned, with _FPS_ having the highest number of players. If
Hypothetical Stylised Fact 2.2 were wrong, for each game we should have very
similar priorities for both tags. Indeed all players who assigned the tag
_FPS_ would also assign the tag _Shooter_. In contrast, we believe that some
players assign only a few tags, corresponding to what comes first to their
mind. Under this hypothesis, we would expect some players to only assign the
tag _FPS_ , but not the tag _Shooter_ as it is less important to them, or
because they consider it to be already implied by the tag _FPS_. The histogram
in Figure 1(a) illustrates the ratios between the priority of _FPS_ and
_Shooter_. We observe a long tail phenomenon; also the mean is equal to $0.76$
and the median to $0.83$. This implies that for half of the considered games,
less than $83\%$ of those players who assigned the tag _FPS_ also assigned the
tag _Shooter_.

We apply the same method for the tags _Online Co-Op_ and _Co-op_ 222Steam tags
are case sensitive. We follow the Steam capitalisation which may differ from
tag to tag.. Obviously, a game with an online co-op mode thereby contains a
co-op mode. However, when we consider the $1171$ games to which both tags
_Online Co-Op_ and _Co-op_ are assigned, with _Online Co-Op_ having the
highest number of players, we observe again a long tail phenomenon in Figure
1(b). The mean is equal to $0.73$ and the median $0.80$. The natural
explanation to this long tail phenomenon and to these low means and medians is
Hypothetical Stylised Fact 2.2: Many players only assign a few tags to a game,
the ones that come first to their mind. We have shown here the results for
only two pairs of tags, for which the numbers of corresponding games were
important ($1867$ and $1171$, respectively). We tested other pairs, and for
all of them we obtained similar results, but due to lack of space we do not
show them here. Also, the number of games was significantly lower. Some
examples of those pairs are _Top-Down Shooter_ and _Top-Down_ ($655$ games),
_Football_ and _Sports_ ($165$ games), _Traditional Roguelike_ and _Rogue-
like_ 333Once again we follow the spelling of Steam, where rogue-like is not
always written with a hyphen. ($102$ games).

Figure 2: Histogram of the ratios of the priority of _RPG_ and _Multiplayer_
for the $1124$ games whose _RPG_ priority is higher than the one of
_Multiplayer_.

In Figure 1, we observed histograms with a peak around a priority of $0.9$ and
a long tail. Recall that we were considering pairs of tags where the first tag
implies the second. We argued that causality was not perfectly reflected in
the tag distributions, because of the long tail phenomenon. However, the
position of the peak still indicates that there is a high correlation. As a
comparison, let us do the same for two tags which are a priori not correlated.
In Figure 2, we applied the same method as before on the tags _RPG_ and
_Multiplayer_. Those are intuitively not correlated since there are
multiplayer RPGs (for instance MMORPGs), but also many singleplayer RPGs. We
observe that the histogram is rather constant; there is no strong peak around
$1$. This is a first hint that useful information might be contained in the
correlation between tag priorities. We use this idea of considering the
correlation for our meronomies in Section 4.

We point out an issue that we discovered concerning the priority. Although we
still believe the notion to be of interest, and that it brings useful
information, we think that the notion could be refined and improved in future
work. It seems that for some reason unknown to us, the priority of some tags
behave erratically. Let us consider for instance the tag _Free to Play_. We
consider three free to play games: _Dota 2_ , _Team Fortress 2_ and _Counter-
Strike: Global Offensive_. For the first two games, _Free to Play_ has a
priority of $1$. However, the second tag with highest priority for _Dota 2_ is
_MOBA_ with a priority of $0.33$, whereas for _Team Fortress 2_ it is _Hero
Shooter_ with a priority of $0.99$. It is quite intriguing to us how the
priorities of the second tags with highest priorities can be so different.
This phenomenon occur regularly with the tag _Free to Play_. We do not
understand why the priority of the second most assigned tag can differ so
much, when both games are equally free to play. We fear that for _Dota 2_ ,
the tag _Free to Play_ pushes all other priorities to very low values, thereby
introducing noise in the data. Concerning _Counter-Strike: Global Offensive_ ,
the priority of _Free to Play_ is $0$. The reason for this can be easily
understood: The game was released in $2012$ and made free to play only in
$2018$. We believe that the tags were mostly assigned soon after the release
of the game, and that is why _Free to Play_ is not amongst the $20$ most
assigned tags. This motivates even further the fact that the method should be
refined, with a particular emphasis on the tag _Free to Play_.

##  3 A taxonomy of Steam tags

In this section, we categorise all of the Steam tags through a taxonomy. In
Section 2, we defined the notion of priority of a tag for a game, and
established that it contained some information. In particular, we gave
empirical evidence for Hypothetical Stylised Fact 2.2, stating that for a
given game $G$, tags with high priorities correspond to what players think
about first when describing $G$. In this section, we consider tags one by one,
and for each tag we look at its priority distribution over all games for which
the priority is non-zero. This allows us to define the taxa of higher rank
into which we classify the Steam tags.

###  3.1 The taxa of higher rank

At the highest level, we have three taxa: High priority tags, Medium priority
tags and Low priority tags. Let us start with the taxon Low priority tags.

####  3.1.1 Low priority tags

According to Hypothetical Stylised Fact 2.2, those tags correspond to what few
players deem interesting for describing a game, and that is not what they
think about first. We define this taxon as the set of tags whose priority
median is at most $0.45$. There are $80$ tags in this taxon. We chose this
threshold using our own expertise of the video game industry. Tags with higher
priority medians seem to us to come significantly faster in players’ minds
when describing a game.

tag | priority median | number of games
---|---|---
_Masterpiece_ | 0.18 | 6
_Epic_ | 0.20 | 106
_TrackIR_ | 0.20 | 31
_Vikings_ | 0.22 | 45
_Reboot_ | 0.23 | 11
_Mod_ | 0.24 | 62
_Addictive_ | 0.24 | 409
_Cult Classic_ | 0.25 | 305
_Kickstarter_ | 0.26 | 181

Table 2: Tags with the lowest priority medians.

tag | priority median | number of games
---|---|---
_Singleplayer_ | 0.36 | 23376
_Multiplayer_ | 0.42 | 6371
_Retro_ | 0.43 | 4748
_VR_ | 0.44 | 4696
_Difficult_ | 0.39 | 4404
_Great Soundtrack_ | 0.31 | 4245
_Co-op_ | 0.39 | 3153
_Controller_ | 0.40 | 2469
_Combat_ | 0.40 | 2135

Table 3: Low priority tags that are most assigned to games.

We present in Table 2 the tags with the lowest priority median, along with the
number of games to which they are assigned. One could argue that we do not
have enough data to deal with tags that are assigned to very few games.
However, for the taxon of Low priority tags, we think that if a tag was
seldomly assigned, it supports even more the claim that this tag is not what
players think about first when describing a game. Nonetheless, among the Low
priority tags, there are some that are assigned to thousands of games. We show
them in Table 3. We find those tags especially interesting. Players do not use
those tags as their primary descriptors for a game, yet they are still
assigned to many games. This gives us an interesting learning: Having a low
priority does not mean that the tag is not important. Indeed, it is important
for players to know whether a game has singleplayer or multiplayer modes. It
is just not what is the most essential to describe a game. In Subsection 4.2,
we delve deeper into the tags _Singleplayer_ and _Multiplayer_.

####  3.1.2 High priority tags

Figure 3: Histograms of the priorities of _RPG_ , _Racing_ and _Roguelike
Deckbuilder_.

Before defining the taxon of High priority tags, let us first consider
priority histograms of tags that clearly should be in that taxon. Three are
depicted in Figure 3. We observe that these histograms roughly look like Dirac
functions centred at $1$. We want to define our High priority tags taxon as
the set of tags whose histograms look similarly to those three. To allow for
some wiggle room, we decided to define the taxon as the set of tags whose
priority median is sufficiently high, at least $0.574803$, and whose maximum
value in the histogram is obtained for a priority of at least $0.765644$. Both
thresholds were decided using our own expertise of video games. The median
threshold was chosen so that the tag _Twin Stick Shooter_ would be in that
taxon, as its priority median is exactly that value, but not _Underground_ and
_Conversation_ that were following. The maximum value threshold was chosen
such that _Tabletop_ would be a High priority tag, but not _2.5D_ and _Lore-
Rich_ that were following. With this definition, we have $155$ High priority
tags. By looking at these tags, it seems that nearly all of them are about
genre. We discuss this in further detail in Subsection 3.2.

####  3.1.3 Medium priority tags

Figure 4: Histograms of the priorities of _2D_ , _Open World_ and _PvE_.

Figure 5: Histograms of the priorities of _1990’s_ , _Military_ and _Robots_.

We have one last taxon of higher rank for all of the remaining $192$ tags.
When looking at the histograms, we distinguish three types of shapes. Figure 4
depicts them. We have curves that look like Gaussian curves, as with _2D_ ,
curves that look rather constant, like the one of _Open World_ , and curves
with two peaks, like the one of _PvE_. However, it seems that these types of
shapes do not procure any insightful meaning. Indeed, one can see in Figure 5
three histograms of other Medium priority tags. Observe that they are all
similar to the one of _2D_. However, we do not see any similarity between _2D_
, _1990’s_ , _Military_ and _Robots_ beyond the fact that they are Medium
priority tags. It seems to us that in this case, the shape of the histogram
does not bring us any further insight.

###  3.2 The taxa of lower rank

In this subsection, we divide taxa of higher rank into several taxa. Our most
interesting comments concerns High priority tags, thus we focus on their
study.

####  3.2.1 The taxon of High priority tags

Interestingly, all High priority tags related to gameplay are genre names:
_Adventure_ , _RPG_ , _Farming Sim_ and so on. Furthermore, nearly all tags
commonly understood as genre tags are in the High priority tags taxon. We
consider this finding to be one of the main contributions of this paper. We
can now define “genre tags” as “gameplay related tags with high priority”.
Also, it provides empirical evidence for Hypothetical Stylised Fact 1.5: Genre
is indeed what players think about first when describing a game.

Some tags are assigned to fewer than $100$ games. We believe that this is too
few games for qualifying those tags as “genres”. We create a taxon for those
tags that have high priority median, but that are assigned to too few games.
Those are: _BMX_ , _Cycling_ , _Motocross_ , _Skateboarding_ , _Tennis_ ,
_Baseball_ , _Lemmings_ , _Wrestling_ , _Mini Golf_ , _Social Deduction_ ,
_Warhammer 40K_ , _Roguevania_ , _Medical Sim_ , _LEGO_ , _Outbreak Sim_ ,
_Pinball_ , _Spelling_ , _Golf_ , _Jet_ and _Action RTS_.

Steam products are not limited to video games, strictly defined. Other
products are also available, and their corresponding tags are all in the High
priority tags taxon. This is unsurprising, as if these products were deemed to
be games, then the following High priority tags would undoubtedly qualify as
game genres: _Utilities_ , _Audio Production_ , _Video Production_ ,
_Animation & Modeling_, _Design & Illustration_, _Photo Editing_ , _Software
Training_ and _Web Publishing_. On a related note, we also have the tags _Game
Development_ and _Programming_ which are more about making than playing video
games. Those tags we put on a separate taxon. In Subsection 4.3, we provide
further evidence to support our decision to treat those tags as an independent
taxon.

In total, we divide the taxon of High priority games into four taxa: game
genres (which are gameplay related tags), tags assigned to too few games, tags
related to products that are not video games, and the miscellaneous tags. In
this last taxon we find: _Free to Play_ , _Indie_ , _Early Access_ ,
_Massively Multiplayer_ , _e-sports_ , _Sexual Content_ , _Nudity_ , _LGBTQ+_
, _Dinosaurs_ , _Mechs_ , _Cats_ , _Experimental_ , _Noir_ , _Lovecraftian_
and _Western_.

We list the learnings we obtain from these tags being High priority tags.
Unsurprisingly, players care about whether the game is free. We indeed expect
players to mind how much they have to pay for a game, which is why _Free to
Play_ is a High priority tag. Secondly, the tags _Indie_ , _Early Access_ ,
_Massively Multiplayer_ and _e-sports_ provide a lot of information about the
type of game, even though those are not genres. Players know better how much
money was invested into the game, whether the game is entirely finished,
whether it involves a lot of interactions with other players, and whether it
is made for e-sports. This tag in itself is full of information. It implies
that the game is hard to master, that it has a multiplayer mode, and that it
is competitive. The presence of the tags _Sexual Content_ and _Nudity_ is also
not surprising, as they can be important filters for players to know whether
they want to play a game. Next, we have the tags _LGBTQ+_ , _Dinosaurs_ ,
_Mechs_ and _Cats_ 444Interestingly, players are more interested in cats than
in dogs. Indeed _Cats_ has a median priority of $0.60$ with a maximum value in
the histogram at $0.96$, whereas for the tag _Dog_ those values are $0.56$ and
$0.45$, respectively. which are themes that players think to be so important
that they are worth mentioning right away when describing a game. Similarly,
_Experimental_ , _Noir_ , _Lovecraftian_ and _Western_ are ambiances of high
importance for players when describing a game. We believe that game developers
can benefit from knowing that these themes and ambiances matter particularly
to players.

####  3.2.2 The taxon of Medium priority tags

As we said, our most interesting comments are for High priority tags. In
particular, we showed in Figure 5 that the priority histogram was not
sufficient for classifying the Medium priority tags into meaningful taxa.
Thus, for the rest of the section, we only discuss why a few tags widely
regarded as genres were put into the Medium priority tags taxon. Those tags
are: _FPS_ , _Shooter_ , _Fighting_ , _Stealth_ , _Hack and Slash_ ,
_Survival_ , _Survival Horror_ , _Horror_ , _MOBA_ , _4X_ , _RTS_ , _Grand
Strategy_ , _Trading Card Game_ , _Match 3_ , _Hidden Object_ and _MMORPG_. We
still believe that those tags should be considered as genre, and explain why
they were misclassified. Concerning _FPS_ , _Shooter_ , _Fighting_ , _Stealth_
, _Hack and Slash_ , _Survival_ , _Survival Horror_ and _Horror_ , they have
relatively low priority medians because they are often used by players to say
that some elements from these genres appear in a game. For instance, if it is
possible to fight in a game, then the tag _Fighting_ will surely appear with
medium priority. _Fighting_ is not the genre of the game, but it corresponds
here to a gameplay element. Let us take another example with _FPS_ and
_Shooter_. The most assigned tags to the game _Fallout 4_ are _Open World_ ,
_Post-apocalyptic_ , _Exploration_ and _RPG_. In our opinion, those tags
indeed describe well this game. With a lower priority, we find also the tags
_Shooter_ and _FPS_. It is true that there are FPS elements in the game, which
occur for instance when the player is aiming, therefore it is understandable
that the tag appears with a medium priority. The takeaway is that tags may
have different purposes and connotations. There are tags that might be used as
genres, but also sometimes used to merely describe some gameplay elements. If
it were a common practice to distinguish the two things and have a tag _FPS -
genre_ and a tag _FPS - gameplay_ , we believe that the former would be put
into the High priority tags taxon while the latter would be rightly classified
as a Medium priority tag. It was most surprising to us to discover that _MOBA_
is in that taxon. We expected it to only appear as a High priority tag.
However, its priority median is quite low, with a value of $0.48$, making it
even closer to Low priority tags than High priority tags. After investigation,
it appears that the tag is often assigned with a medium priority to team-based
multiplayer games, even when they do not belong to the MOBA genre. It seems
that players and the industry have a different understanding of the tag.

Concerning the remaining tags, _4X_ , _RTS_ , _Grand Strategy_ , _Trading Card
Game_ , _Match 3_ , _Hidden Object_ and _MMORPG_ , they seem to be
overshadowed by other tags preferred by players. For instance _4X_ , _RTS_ and
_Grand Strategy_ are overshadowed by _Strategy_ , _Turn-Based_ and _Real-
Time_. _Trading Card Game_ often appears after _Card Game_ and _Card Battler_.
_Match 3_ and _Hidden Object_ are overshadowed by _Puzzle_. Likewise, _MMORPG_
is regularly chosen only after _RPG_ , _Multiplayer_ and _Massively
Multiplayer_. This is why, although they have a high priority median, their
occurrence peak in the histogram is not close to a priority value of $1$.

###  3.3 List of genres comparison

Using our data-driven approach, we have now obtained a list of genre tags. It
can be found in Appendix A. Those are mainly in the High priority taxon, and
we presented in Paragraph 3.2.2 a list of tags that should still be considered
as genre tags, although they were classed as Medium priority tags. We compare
our list to Li’s [11] and to the one in Steamworks [27] as they are both made
from Steam tags. It would be interesting to compare ours to all genre lists.
However, for the sake of brevity, we concentrate solely on those two lists.

Li’s list, although also being made from a data-driven approach, serves a
different role than ours [11]. His aim was to extract from Steam tags a
reasonably small list of genre tags that would allow one to characterise all
games, whereas our goal is to find an extensive list of genres. Consequently,
Li’s list contains $29$ genres, and ours contains $127$ tags. Still, the
comparison is interesting as there are a few tags which are in Li’s list but
not in ours. Those are: _Soccer_ , _Resource Management_ , _Music_ and
_Classic_. For each genre, Li associates a group of correlated gameplay tags.
Concerning _Soccer_ , the group contains _Soccer_ , _Football_ and _Sports_.
This choice seems strange to us, for then according to Li’s list all sports
game would be considered as soccer games. In our genre list, we have the tag
_Sports_ instead of _Soccer_ , which seems more reasonable. The same happens
with _Resource Management_ that we would replace by _Management_ and _Music_
that we would replace by _Rhythm_. Concerning _Classic_ in Li’s list, its
presence follows from the fact that Li considers _Classic_ and _Cult Classic_
to be related to gameplay. We disagree with that statement, although we cannot
substantiate our belief with data.

Similar to ours, Steamworks’ list of genre tags strives for comprehensiveness
[27]. Our list contains $127$ tags whereas theirs contains $140$. The two
lists share $112$ tags in common, which indicates that they mostly agree with
each other. Let us start with the $15$ tags that are in our list but not in
Steamworks’. It is worth noting that despite Steamworks appearing to have all
of the Steam tags (whether genre-related or not), some tags are actually
absent. The genre tags in our list that do not even appear on Steamworks are:
_Cooking_ , _Creature Collector_ , _Party Game_ , _Puzzle-Platformer_ and
_Roguelike Deckbuilder_. More interestingly, there are a few tags that are
High priority tags related to gameplay (which we thus consider as genre tags),
which are not considered as genre tags by Steamworks. We provide the list
here: _Archery_ , _Automation_ , _Boxing_ , _Deckbuilding_ , _Fishing_ ,
_Hunting_ , _Naval Combat_ , _Vehicular Combat_ , _Otome_ and _Parkour_. All
those tags were classed as Features in Steamworks instead of genres, except
for _Otome_ and _Parkour_ which are classed as Themes & Moods. We think that
the fact that those are High priority tags supports the idea of considering
them as genre tags.

We present the $28$ tags that are in Steamworks’ list of genre tags but not in
ours. Some are High priority tags about gameplay that we removed in Paragraph
3.2.1 because they were assigned to fewer than $100$ games. Indeed, it seems
to us that tags assigned to too few games cannot really define a genre. Those
tags are: _BMX_ , _Baseball_ , _Cycling_ , _Golf_ , _Medical Sim_ , _Mini
Golf_ , _Motocross_ , _Outbreak Sim_ , _Roguevania_ , _Pinball_ ,
_Skateboarding_ , _Spelling_ , _Tennis_ and _Wrestling_. There are also a few
High priority tags that we did not put into our list of genre tags because we
deem that they are not clearly enough related to gameplay. Those are
_Programming_ , _e-sports_ and _Experimental_. The remaining tags are all
Medium priority tags, and are therefore not part of our genre lists. Our
findings indicate that those are not words that first come to players mind
when describing a game, and supports the idea of not considering them as
genres. The tags are: _Basketball_ , _Bowling_ , _Football_ , _Hacking_ ,
_Hockey_ , _Open World_ , _Skating_ , _Skiing_ , _Snowboarding_ , _Soccer_ and
_Investigation_.

##  4 Two meronomies of Steam tags

In this section, we propose two meronomies of some Steam tags. In Section 3,
we classified the tags into taxa, where two tags in the same taxa share some
properties (related to their priority distributions). In Section 4, we use
totally different methodologies, for different goals. In the resulting
meronomies, we say that a tag $A$ is a part of a broader tag $B$, or
equivalently that $A$ is a meronym of $B$.

The first meronomy we present focuses on the tags with the broadest meanings.
We aim at finding the most general tags, and to see how they relate to each
other. In particular, we focus on the tags at the top of the meronomy, the
ones that are parts of no others, but all others tags are parts of them. We
name them the “capital tags”. We claim that the capital tags are interesting
because they form the smallest set of words that one needs to describe all
video games (without going into detail).

The second meronomy aims at finding synonymous tags. We believe that one can
reduce the number of Steam tags without loosing too much information, as many
tags share similar meanings. Our aim is to find those groups of tags, and for
each one to find one representative.

###  4.1 The method

The method starts the same for both meronomies. The idea is to look at pairs
of tags $A$ and $B$ that are correlated enough, and then check whether $A$ or
$B$ appears more often. If $B$ appears more often, we declare that $A$ is a
meronym of $B$. Intuitively, $A$ should indeed be a part of $B$, as they are
correlated and $B$ appears more often, ergo has a broader meaning.

For each game $G$, we have an entry with $427$ values (the number of Steam
tags), where the value is the priority of the corresponding tag for the game
$G$. As there are at most $20$ tags with positive priority for any given game,
it implies that at least $407$ values are equal to $0$. We compute the Pearson
correlation coefficient for all pairs of tags. A natural idea would be to take
all pairs with correlation above some threshold and be done with it. The issue
is that many tags are assigned to very few games. Thus, their priority is $0$
for virtually all games. This induces a bias, and two of such tags will have a
strong correlation even though they are hardly related. To tackle this issue,
we consider all pairs of tags $A$ and $B$ with correlation above $0.1$, and
then compute what we call their _local Pearson correlation coefficient_. It is
equal to the Pearson correlation coefficient, but it is computed only on games
to which at least one of $A$ or $B$ is assigned.

For the first meronomy, we keep only the pairs with local Pearson correlation
coefficient above $-0.7$, over all pairs with (global) Pearson correlation
above $0.1$. For the second meronomy, we take all pairs with local Pearson
coefficient above $-0.6$, while keeping a global Pearson correlation threshold
of $0.1$. Let us motivate the choice of these thresholds. For the global
threshold, the lower it is, the better for the soundness of the results.
However, getting it lower increases a lot the number of pairs of tags for
which we have to compute the local Pearson correlation. As we have $428$ tags,
there are $\binom{428}{2}=91378$ pairs to consider. This is computationally
quite expensive, which is why we only consider pairs with correlation above
$0.1$. There are $2197$ of those pairs. As mentioned earlier, we have concerns
with the global correlation approach in that it may produce some false
positives (pairs with high correlation that are not closely related). However,
we do not anticipate the occurrence of a false negative, that is a pair of
tags with similar meanings but low correlation.

For the local threshold of the first meronomy, the value $-0.7$ may seem
extremely low. However, as we do the computation only on the games to which at
least one of the tags is assigned, this diminishes drastically the correlation
value. Indeed, there are proportionally much more games to which only one tag
is assigned, since we removed all games to which none are assigned. Thus, with
a local threshold of $-0.7$, we are actually considering only $652$ pairs of
tags out of the $2196$ that have global correlation above $0.1$. Getting the
threshold even lower would make us consider pairs that are actually not so
much related, according to our own video game expertise. We present the result
in Subsection 4.2.

We study what happens when we set the local threshold to $-0.6$ in Subsection
4.3. There are only $361$ pairs with local correlation above this threshold.
The choice of the value was again made using our own video game expertise. We
chose the lowest value such that the pairs were indeed synonymous in our
opinion.

###  4.2 The capital tags

Figure 6: The oriented subgraph induced by the tags with at least 9 incoming
edges. For each tag, we write its name and the number of incoming edges in the
original graph.

Now that we have a list of $652$ pairs of tags with high correlation, we
compute for each pair $A$, $B$ whether $A$ or $B$ appears more. We construct
an oriented graph with the tags as vertices and an edge from tag $A$ to tag
$B$ if $B$ occurs more often than $A$. This graph is unreadable as there are
too many vertices and edges. However, we are mainly interested in the vertices
that have the most incoming edges, as they correspond to tags with the
broadest meanings. Figure 6 shows the subgraph induced by the vertices $V$
with at least $9$ incoming edges (that is the subgraph where the vertex set is
$V$, and we show only edges between vertices in $V$). We observe that there
are seven vertices with no outgoing edges, that we name the _capital tags_.
One might observe that the graph shown in Figure 6 does not perfectly
correspond to a meronomy. We tackle this issue at the end of the subsection,
as we want first to focus on the capital tags.

######  Definition 4.1.

The seven capital tags are _Multiplayer_ , _Singleplayer_ , _Action_ ,
_Casual_ , _Adventure_ , _Strategy_ and _Anime_. They correspond to tags that
are correlated with a lot of other tags, and that occur more than them.

The name is a reference to the seven capital sins from Christian teachings.
Thomas Aquinas uses the word “capital” for the following reason: Accordingly a
capital vice is so called, in the first place, from “head” taken in the proper
sense, and thus the name “capital” is given to a sin for which capital
punishment is inflicted. It is not in this sense that we are now speaking of
capital sins, but in another sense, in which the term “capital” is derived
from head, taken metaphorically for a principle or director of others. In this
way a capital vice is one from which other vices arise, chiefly by being their
final cause [2].

We posit that a similar phenomenon exists with tags, where the capital tags
serve as the directors of the other Steam tags, much like how the capital
vices act as the head of other vices. The seven capital tags encompass all
others, as stated in the following hypothetical stylised fact:

######  Hypothetical stylised fact 4.2.

All tags are parts of the capital tags.

By construction, our seven capital tags are tags that are correlated with a
lot of other tags, and that occur more often than them. This is already some
justification for Hypothetical Stylised Fact 4.2. We add some more evidence at
the end of the subsection when we define a meronomy of the tags in Figure 6.
Before that, we state and establish another hypothetical stylised fact. First,
we need to define the following concept:

######  Definition 4.3.

We say that a game $G$ is covered by a set of tags $\mathcal{T}$ if at least
one of the tags in $\mathcal{T}$ is assigned to $G$.

######  Hypothetical stylised fact 4.4.

The capital tags form a very small group of tags such that nearly all games
are covered by this set.

Hypothetical Stylised Fact 4.4, although being quantitative in essence, is not
perfectly mathematically defined. One reason for this is that the Steam tag
are assigned by players. So all rules and observations should suffer from a
few counterexamples, as the data is significantly noisy. On another note, it
is computationally too hard to check Hypothetical Stylised Fact 4.4. If one
wanted to check all sets of seven tags for instance, that would be
$\binom{428}{7}\approx 4.97\cdot 10^{14}$ sets of tags to test.

| percentage of covered games | percentage of covered games by this tag and the ones above
---|---|---
tag | over all games | over games with exactly $20$ tags | over all games | over games with exactly $20$ tags
_Singleplayer_ | 46 | 67 | 46 | 67
_Action_ | 45 | 54 | 70 | 85
_Casual_ | 43 | 42 | 84 | 92
_Adventure_ | 42 | 51 | 90 | 95
_Strategy_ | 21 | 26 | 93 | 98
_Multiplayer_ | 13 | 22 | 93 | 98
_Anime_ | 9 | 14 | 94 | 98

Table 4: How the capital tags cover nearly all games

One can see in Table 4 that our choice of capital tags does cover nearly all
games. We show the percentage of games covered by each of the capital tag. We
also show the percentage for the set consisting of the tag of the line and all
the ones above. The tags are sorted by decreasing percentage of games covered.
We also do the same when considering only games with $20$ tags. Games with
fewer tags can be misleading, as it may simply be that not enough players
assigned tags, and if more players did, then surely some of them would have
assigned a capital tag. We observe that Table 4 strongly supports Hypothetical
Stylised Fact 4.4.

As argued, it is not computationally possible to test all sets of seven games.
Still, we present another set that could have been a fitting candidate: _2D_ ,
_3D_ , _2.5D_ , _Isometric_ , _Third Person_ , _First-Person_ and _Top-Down_.
All games are either in 2D, in 3D, or in some mix of the two: 2.5D or
isometric. We even add _Third Person_ and _First-Person_ which imply that the
game is in 3D, but give more information about the representation. The same
holds with _Top-Down_ and _2D_. Therefore, one might expect to cover all games
with these seven tags. This is not the case, as only $47$% of the games are
covered, and only $85$% of the games with $20$ tags are covered. This again
supports Hypothetical Stylised Fact 4.4, as it substantiates the claim that
the capital tags are very few while covering essentially all games.

For the sake of curiosity, we extended the capital tags with as few tags as
possible in order to cover even more games. This was done by hand by looking
at which games were not covered yet, and finding what tags were assigned to
most of them. By adding the tags _Arcade_ , _RPG_ , _Simulation_ , _Survival_
, _Puzzle_ and _Horror_ to the capital tags, we cover $96.73$% percent of
games, and $99.78$% percent of games with $20$ tags. Unsurprisingly, several
of those new tags can be found in Figure 6: _Puzzle_ , _RPG_ and _Simulation_.

We make a few side-remarks that we deem interesting. First, we observe that
for six of the capital tags, the percentage of games covered is less than the
one of games with $20$ tags covered. The only one for which it decreases is
_Casual_ , which goes from $43$% to $42$%. Moreover, the increase for the six
others is quite different from tag to tag. The biggest increase in terms of
scale factor happens with _Multiplayer_ (scale factor of $1.69$). Intuitively,
the games with $20$ tags are the most mainstream. This supports the idea that
mainstream games on Steam more often offer multiplayer modes, and that they
tend to be less casual. We also note that the second biggest increase is with
_Singleplayer_ (scale factor of $1.46$). This tells us that even though
_Multiplayer_ and _Singleplayer_ are classified in the Low Priority taxon (see
Subsection 3.1), they are actually assigned to many games, and would perhaps
be assigned to all games if all games had $20$ tags assigned to them.
Therefore, low priority tags should not be thought of as of less importance.
They are not what players think about first when describing a game, but may
still be a feature that should be mentioned at a later step.

Recall that a meronomy deals with part-whole relationships. Mathematically, a
meronomy can be seen as a partially ordered set (poset). A partial order,
denoted by $\leq$, must satisfy three properties:

  1. 1.

Transitivity: if $A\leq B$ and $B\leq C$ then $A\leq C$,

  2. 2.

Reflexivity: we have $A\leq A$,

  3. 3.

Antisymmetry: if $A\leq B$ and $B\leq A$, then $A=B$.

We can see in Figure 6 that the relation depicted is not perfectly a partial
order. Indeed, let us say that $A$ is a part of $B$, denoted by $A\leq B$, if
there is an arrow from $A$ to $B$. The graph in Figure 6 looks a lot like a
Hasse diagram. A Hasse diagram of a poset $\leq$ is a representation where
there is an edge from $A$ to $B$ if $A\leq B$ and there is no $C$ such that
$A\leq C\leq B$. This is a useful and concise way of representing posets. For
the graph in Figure 6 to be a Hasse diagram, one simply has to remove the
edges from _Puzzle_ , _Atmospheric_ , _Story Rich_ and _Fantasy_ to
_Singleplayer_. Thus, we are implying that we consider _Sports_ to be a part
of _Simulation_ , which itself is a part of _Strategy_. Likewise, we say that
_Fantasy_ is a part of _RPG_ , which itself is a part of _Adventure_.

Let us mention some other relationships, not depicted in Figure 6, but that
are present in the larger graph (which we did not represent as there are too
many edges to be readable). We have arrows from _Co-op_ to _Multiplayer_ and
_Action_. There is an arrow from _Cute_ to _Colorful_ , itself being connected
to _2D_ and _Casual_. _Management_ is connected to _Singleplayer_ and
_Simulation_. Finally, we want to mention _PvP_ being connected to
_Multiplayer_. As all these relations are quite sensible, this supports our
idea of looking at that graph as the Hasse diagram of some poset, or
equivalently to establish a meronomy of those tags.

We now have evidence for Hypothetical Stylised Fact 4.2: All tags are parts of
the capital tags. By only adding or removing a few edges, we have obtained a
meronomy for the tags of Figure 6 that could be extended further to the other
tags. In this meronomy, we have seen that the capital tags are the ones at the
top: All other tags are meronyms of the capital tags.

Finally, we make a concluding remark about the capital tags. Four of them are
game genres: _Action_ , _Adventure_ , _Strategy_ and _Casual_ (although it is
debatable whether it is a genre, this is how it is considered in Steamworks’
and our genre list [27]). Two other tags give important information about the
game, even though they are not genres: _Singleplayer_ and _Multiplayer_. We
suspect that the remaining tag, _Anime_ , is actually an error. First, it is
the tag among the capital tags that covers the smallest number of games.
Secondly, the $11$ tags that have an edge directed to it in the graph are:
_JRPG_ , _Visual Novel_ , _Otome_ , _Dating Sim_ , _Romance_ , _Female
Protagonist_ , _Sexual Content_ , _NSFW_ , _Hentai_ , _Nudity_ and _Mature_.
We observe that the five latter tags refer to sexual content, and share a
similar meaning. This might induce a bias, giving to much weight to tags
related to sexual content, therefore wrongly putting _Anime_ as a tag with
many incoming edges in the graph.

###  4.3 The synonymous tags

As detailed in Subsection 4.1, we now use a local correlation threshold of
$-0.6$. We deem the pairs of tags that have a local correlation that high to
be synonymous. In Subsection 4, all edges were oriented from a tag to another:
there was an arrow from $A$ to $B$ if $B$ appears much more often than $A$. In
this subsection, we are also interested in knowing when tags appear mostly
together. Indeed, this would be the best examples of tags being synonymous.
When tags appear mostly together, we draw a _mutual_ edge: an arrow that
points towards both ends. To decide what kind of edge to draw, we do the
following: For the tags $A$ and $B$, we compute $X_{A}$, $X_{B}$, $X_{A\cup
B}$ and $X_{A\cap B}$ which denote how many times $A$ was assigned to a game
but not $B$, how many times $B$ was assigned to a game but not $A$, how many
times at least one of $A$ or $B$ was assigned, and how many times both were
assigned, respectively. We define $r_{A}:=X_{A}/X_{A\cup B}$,
$r_{B}:=X_{B}/X_{A\cup B}$ and $r_{A\cap B}:=X_{A\cap B}/X_{A\cup B}$. If the
maximum of these three ratios is $r_{A}$, then we draw an arrow from $B$ to
$A$. If it is $r_{B}$, then we draw an arrow from $A$ to $B$. If it is
$r_{A\cap B}$, then we draw a mutual arrow between $A$ and $B$.

Before running the program on the tags, we remove a few of them. The reason
for this is that there are tags which are correlated with a lot of other tags,
and that are assigned to many games. Those are the ones we mentioned in
Subsection 4.2. We remove the capital tags in addition to _2D_ , _Shooter_ ,
_Puzzle_ , _Atmospheric_ , _Simulation_ , _Story Rich_ and _Fantasy_ (which
are amongst the other tags with the highest number of edges oriented towards
them in Figure 6). Keeping those tags would distort the result and show only
correlation between those tags and few others.

Also, we use the information we obtained in Subsection 3.1. Recall that we
classified the tags into three taxa of higher rank: High priority tags, Medium
priority tags and Low priority tags. In the oriented graph of this subsection,
we draw dotted red arrows for edges where the two involved tags are not from
the same taxon. Indeed, not being in the same taxon may be a hint that these
tags are not synonymous after all, since they are not used in the same way by
players, although being highly correlated.

Figure 7: Some synonymous tags. Arrows are dotted red if the tags belong to
different taxa of higher rank.

Some synonymous tags are shown in Figure 7. Due to lack of space, we only
depict the examples we find most meaningful. As a first simple example, we
have an edge from _Crowdfunded_ to _Kickstarter_. As wanted, the meanings of
those two tags are very close. Let us consider the subgraph induced by _Naval
Combat_ , _Naval_ and _Sailing_. The arrow from _Sailing_ to _Naval_ is
natural, as the meanings are close and _Sailing_ implies _Naval_. However,
_Naval Combat_ and _Naval_ are not so much synonymous. Indeed, one is
understood as a game genre, whereas the other merely indicates that a game
involves ships. This is why the information from Subsection 3.1, depicted by
the dotted red arrow, does matter. Although it is inevitable that _Naval
Combat_ and _Naval_ be correlated, it seems wrong to treat them as synonyms.
Likewise, the arrow from _Turn-Based_ to _Turn-Based Strategy_ is treacherous,
as one is a gameplay element, and the other is a game genre. More generally,
we believe that no red arrow should be taken into account. We make one
exception for the edge from _Trading Card Game_ to _Card Game_. Indeed, we
argued in Subsection 3.2 that _Trading Card Game_ was one of the few tags that
are misclassified by our method, and that it should actually be treated as a
genre. On another note, we have a group of tags related to non-games, that we
mentioned in Subsection 3.2. All those tags can be considered as one group of
tags related to products offered by Steam that are not games.

When looking at Figure 7, a natural idea is to give a name for all these
groups of synonymous tags. This name would be a representative of the group,
the one that is most general. For instance, the group (_Crowfunded_ ,
_Kickstarter_) would be represented by _Kickstarter_. Similarly, the group of
platformer tags would be represented by _Platformer_. But how would one deal
with the group that contains card game tags and rogue-like tags? Intuitively,
this group should be split in two. Likewise, what about the group of tags not
related to games? It would seem natural to have most of those tags represented
by _Utilities_ , but it is no clear what should happen to _Education_ , as the
only edge incident to it is incoming, and not outgoing. We detail in the next
paragraph a formal explanation on how to deal with these situations, which
results in a grouping that is in accordance with our intuition.

Let $G$ be a directed graph with labeled vertices. First merge all mutual
edges of $G$. For a mutual edge $e=\\{u,v\\}$, keep arbitrarily one of the two
labels $u$ or $v$. Let $H$ denote the same graph as $G$ with the orientations
of the edges removed, resulting in an unoriented graph. Let us denote by
$W_{1},\dots,W_{k}$ the vertex sets of the $k$ connected components of $H$. We
denote by $V_{1},\dots,V_{k}$ the same vertex sets considered in $G$. Let us
consider the subgraph $G_{i}$ of $G$ induced by $V_{i}$, for some $1\leq i\leq
k$. We say that $G_{i}$ is _well-oriented_ if there exists a unique vertex
$v\in V_{i}$ such that for each $u\neq v$ in $V_{i}$, there exists a directed
path from $u$ to $v$ in $G_{i}$. Making an abuse of notation, we say that $G$
is _well-oriented_ if each $G_{i}$ is well-oriented.

Going back to the example in Figure 7, we see that most connected components
are well-oriented. Moreover, for the group of platformer tags, _Platformer_ is
the unique tag that can be reached via directed paths from all other tags in
the group. However, the group with card game tags and rogue-like tags is not
well-oriented. Now, what we suggest for splitting into well-oriented groups is
the following: Given the graph $G$, remove as few edges as possible to make it
well-oriented. To the best of our knowledge, this problem has not been
previously studied. However, we believe it is interesting from both a
theoretical perspective and for practical applications: an efficient algorithm
would be needed for naming the synonym groups. We do not know of any fast
algorithm for solving the problem, and wonder whether it is NP-hard.
Nonetheless, on the small example we are considering now, we solve the problem
by hand. In Figure 7, it suffices to remove the edges from _Roguelike
Deckbuilder_ to _Rogue-lite_ and _Rogue-like_ to obtain a well-oriented graph.
We therefore obtain the group (_Card Battler_ , _Deckbuilding_ , _Trading Card
Game_ , _Roguelike Deckbuilder_ , _Card Game_) represented by _Card Game_ ,
and the group (_Roguevania_ , _Action Roguelike_ , _Traditional Roguelike_ ,
_Rogue-lite_ , _Rogue-like_) represented by either _Rogue-lite_ or _Rogue-
like_. Similarly, the group of tags not related to video games is not well-
oriented. To make it well oriented, one has to remove the edge from _Software
Training_ to either _Education_ or _Utilities_. Although in this case we
solved the problem by hand, it would be very useful to have efficient
algorithms for solving this problem for general directed graphs. This would
allow us to deal with the synonymous tags on a larger scale.

Finally, we observe that the graph is very similar to the graph of a poset, as
was the case in Figure 6. One could remove the dotted red edges, merge the
vertices connected by a mutual edge, and remove the edges we mentioned to make
the graph well-oriented. Now it suffices to remove a few edges that give only
redundant information, like the one from _Photo Editing_ to _Utilities_ or the
one from _Roguevania_ to _Rogue-lite_ /_Rogue-like_. Our approach yields a
meronomy of synonymous tags, with the topmost tags serving as representatives
of their respective groups.

##  5 Conclusion

We proposed several hypothetical facts, such as the notion that players
primarily associate games with their genre. To support this claim, we
introduced the concept of priority of Steam tags and demonstrated its
relationship to players’ perceptions of games. Moreover, our approach enabled
us to create a data-driven taxonomy that provides a comprehensive list of
genres. We found a set of seven tags, the capital tags, which roughly
summarise all information contained in the Steam tags. Those are _Multiplayer_
, _Singleplayer_ , _Action_ , _Casual_ , _Adventure_ , _Strategy_ and _Anime_
, although we argued why the presence of this last tag in the list is dubious
at best. We found the tags _Multiplayer_ and _Singleplayer_ extremely
interesting as they are capital tags, assigned to thousands of games, but are
Low priority tags. We showed how some tags can be merged without loosing too
much information. We proposed a criterion for finding a representative of a
merged group, and ask whether there exists an efficient algorithm for applying
this method.

We list some further improvements that can be made to our study. We stated
that the notion of priority might need to be refined in order to be more
consistent, by taking the example of the tag _Free to Play_. Although
considering priority histograms allowed us to define an interesting taxonomy,
this method has some limit. In particular for Medium priority tags, we could
not find a meaningful way of subdividing this taxon into taxa (see Figure 5).
Maybe some other ideas could allow one to establish a meaningful data-driven
classification of Medium Priority tags. Secondly, our method is biased towards
games available on Steam, and we acknowledge that this may limit the
generalisability of our findings. Also, we note that our computations assume
an equal impact for all games. However, according to Orland [18], in 2014,
over a quarter of registered games on Steam had never been played. Thus, we
believe it would be beneficial to consider player engagement metrics, such as
the number of players or total hours played, and to weight games accordingly
in our computations. We noticed that some Low priority tags are assigned to
thousands of games, like _Multiplayer_ and _Singleplayer_. We believe that
they should be investigated further, to better understand how players think
about them. Using the knowledge of capital tags, we developed a method for
identifying synonymous tags. It would be intriguing to apply this method in a
database where synonymous tags are intelligently merged. Our expectation is
that, in such a scenario, _Anime_ would no longer be considered a capital tag.

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##  Appendix A List of video game genres

  * •

2D Fighter, 2D Platformer, 3D Fighter, 3D Platformer, 4X

  * •

Action, Action RPG, Action Roguelike, Action-Adventure, Adventure, Arcade,
Archery, Arena Shooter, Auto Battler, Automation, Automobile Sim

  * •

Base-Building, Battle Royale, Beat ’em up, Board Game, Boxing, Building,
Bullet Hell

  * •

CRPG, Card Battler, Card Game, Casual, Character Action Game, Chess, Choose
Your Own Adventure, City Builder, Clicker, Collectathon, Colony Sim, Combat
Racing, Cooking, Creature Collector

  * •

Dating Sim, Deckbuilding, Diplomacy, Dungeon Crawler

  * •

Education, Exploration

  * •

Farming Sim, Fighting, Fishing, Flight, FPS

  * •

God Game, Grand Strategy

  * •

Hack and Slash, Heist, Hero Shooter, Hidden Object, Horror, Hunting

  * •

Idler, Immersive Sim, Interactive Fiction

  * •

JRPG

  * •

Life Sim, Looter Shooter

  * •

Management, Match 3, Metroidvania, Mining, MMORPG, MOBA, Mystery Dungeon

  * •

Naval Combat

  * •

On-Rails Shooter, Open World Survival Craft, Otome

  * •

Parkour, Party Game, Party-Based RPG, Platformer, Point & Click, Political
Sim, Precision Platformer, Programming, Puzzle, Puzzle-Platformer

  * •

RPG, Racing, Real Time Tactics, Rhythm, Rogue-like, Rogue-lite, Roguelike
Deckbuilder, RTS, Runner

  * •

Sandbox, Shoot ’Em Up, Shooter, Side Scroller, Simulation, Sokoban, Solitaire,
Souls-like, Space Sim, Spectacle fighter, Sports, Stealth, Strategy, Strategy
RPG, Survival, Survival Horror

  * •

Tabletop, Tactical RPG, Third-Person Shooter, Time Management, Top-Down
Shooter, Tower Defense, Trading, Trading Card Game, Traditional Roguelike,
Trivia, Turn-Based Strategy, Turn-Based Tactics, Twin Stick Shooter, Typing

  * •

Vehicular Combat, Visual Novel

  * •

Walking Simulator, Wargame, Word Game