YouPorn is one of the largest providers of adult content on the

web. Being free of charge, the video portal allows users - besides

watching - to upload, categorize, and comment on pornographic

videos. With this position paper, we point out the challenges of

analyzing the textual data offered with the videos. We report on

first experiments and problems with our

YouPorn dataset

, which we

extracted from the non-graphical content of the YP website. To gain

some insights, we performed association rule mining on the video

categories and tags, and investigated preferences of users based on

their nickname. Hoping that future research will be able to build

upon our initial experiences, we make the ready-to-use

YP dataset

publicly available

Schuhmacher, Michael

Völker, Johanna

Zirn, Cäcilia

Mannheim Electronic DOCument Server der Universitätsbibliothek Mannheim

Exploring youporn categories, tags, and nicknames for pleasant recommendations

MAnnheim DOCument Server

Exploring YouPorn Categories, Tags, and Nicknames for
Pleasant Recommendations
Michael Schuhmacher, Cäcilia Zirn, Johanna Völker
Data and Web Science Group
Universität Mannheim, Germany
{michael, caecilia, johanna}@informatik.uni-mannheim.de
ABSTRACT
YouPorn is one of the largest providers of adult content on the
web. Being free of charge, the video portal allows users - besides
watching - to upload, categorize and comment on pornographic
videos. With this position paper, we point out the challenges of
analyzing the textual data offered with the videos. We report on
first experiments and problems with our YouPorn dataset, which we
extracted from the non-graphical content of the YP website. To gain
some insights, we performed association rule mining on the video
categories and tags, and investigated preferences of users based on
their nickname. Hoping that future research will be able to build
upon our initial experiences, we make the ready-to-use YP dataset
publicly available.
1. INTRODUCTION
Sex sells, and it is not astonishing that adult content is widespread
on the internet. According to Alexa Internet Inc., XVideos, PornHub
and YouPorn are ranked among the hundred most popular websites.
Recommender systems as well as data mining techniques could help
guiding the user through the large amount of available media to the
content most relevant to her/him. In this paper, we focus on the
user-created, textual meta information of a video for this purpose.
We report on first experiences with the YouPorn (YP) data set, a
large corpus of nicknames, comments, video tags and categories
which we constructed by crawling the publicly available pages of
youporn.com. We try to outline the key challenges of working with
these user-generated contents in the pornography domain illustrating
our findings by the preliminary results of our experiments with this
data. Our experiments were motivated by the following research
questions: Would it be feasible to recommend videos to users only
knowing their nicknames? Can we recommend additional tags (or
categories) for a video based on the ones already assigned to it?
The preliminary work presented in the following is still far from giv-
ing comprehensive answers to these questions, but it helps getting
a grip on this rather unusual type of data. We hope that our experi-
ences and the availability of our data set will inspire and facilitate
future research on mining adult content.
2. THE YP DATA SET
The YP data set consists of textual content extracted from 165,402
single HTML video pages from youporn.com. We fetched the
HTML content with a custom Python screen-scrapping program,
effectively retrieving all, as of Oct 2012, available video pages. We
used regular expressions to extract the following features and in-
clude them in our YP corpus: The unique video title, the average
rating and the ratings count, all categories and tags assigned, and
all comments including comment text, nickname, and date of com-
menting. The corpus with all features listed is publicly available to
encourage further research by third parties.1 In the following, we
describe the main aspects of the corpus data.
Video Categories and Tags: Any user can assign categories of a
fixed vocabulary to a video. The categories are used for the web-
site’s main menu categorization. In addition, users can freely create
and assign tags which allows for more fine grained differentiations.2
Of the 165,402 pages, around 50% of pages have at least one cate-
gory/tag. The maximum numbers of categories/tags we found is 19,
the average is at 7.6. The most frequently used category is amateur
(19,122 videos), followed by blowjob (18,964) and the tags hard-
core (12,868) and cumshot (12,061). The categories and tags are not
mutually exclusive, e.g. European and Turk both exist. Furthermore,
the categories/tags cover a different dimension of descriptions, e.g.
3some and Wife (actor information) co-occurs with Blow and Doggy
(sexual techniques).
User Comments and Nicknames: At youPorn, users can comment
on videos and leave a self-selected nickname. 62% of the pages
have at least one comment, these having on average 8.8 comments.
The distribution of the 910,000 comments show a nearly steady
decline for the page count with respect to the number of comments
per video page. The comments themselves are rather brief, with
an average word count of 11.2 per comment. An example for a -
comparably - meaningful comment is “I kissed my girlfriend like
that - she slapped me in my face” from nickname “burning face”.
Rank Nickname # Rank Nickname #
1 lol 4,911 57 sex 776
2 me 4,898 68 Au Cindy 671
3 xxx 3,597 129 Bill 69 474
8 john 1,603 189 Cunnilinguo 341
14 Con-naisseur 1,393 234 love 303
41 Camille Crimson 966 659 Fred Flintstone 125
45 :) 923 1,288 OldschoolRobert 67
Table 1: Selected nicknames ordered by frequency, i.e. number
of comments
The nickname for each comment can be freely chosen and neither
has to be registered nor unique.3 The nicknames in the corpus are
thus only plain strings. We identified about 305,000 unique strings
of which selected frequently used nicknames are given in Table 1.
1http://blog.uni-mannheim.de/mschuhma/yp-corpus/
2Since end of Nov 2012, YP does no longer offer user-created tags.
3Since end of Nov 2012, YP offers comments with registered nicknames.
3. EXPERIMENTS
For gaining a better understanding of the YP data set, we conducted
some experiments in an exploratory manner, as reported below.
Recommendation of Videos by nickname: We assume that the
choice of nickname is - besides situational influences in that very
moment - largely based on the user’s personality. While one person
might call her/himself Dragon Slayer or Jabba the Hutt, another
one would rather prefer using her/his real given name or point out
the size of her/his body parts. This arises the question whether in
return it is possible to draw conclusions which videos a user prefers
depending on the choice of her/his nickname.
In [1] and [2], the authors analyze nick names in Internet Relay Chat
(IRC) and a web forum on eating disorders, manually classifying the
nicknames they found. The name category scheme defined by [1] is
rather focused on the meaning of the names, using categories like
“famous names”, “objects”, “sex-related nick names”. In contrast,
the scheme used in [2] is linguistically oriented (e.g. “commonly
known names”, “nouns and phrases“ or ”adjectives“).
Inspired by the above mentioned category schemes, we categorized
the nicknames found in the YP corpus. Aiming at a completely au-
tomatic labeling process, we simply matched the nickname strings
with predefined lists. Out of the 973,963 comments, we categorized
69,819 comments as showing with a male given name4 in the nick-
name, 22,791 with a female given name4, and 13,409 containing
explicit vocabulary (this includes vulgar or pornographic language)5.
Each name was classified into exactly one category. In case it fit
into several categories, explicit content was prioritized; in case of
ties with male and female given name, we did not give any label.
We have to point out that the category female given name does not
necessarily mean the comment was entered by a woman, it could
for instance refer to a porn actress.
Male given name
Female given name
Explicit Vocabulary
69
les
bia
n
ba
ck
ro
om
cu
nn
ilin
gu
s
ch
ub
by
ba
be
s
kis
sin
g
lic
kin
g
na
ug
ht
y
big
−a
ss
ro
m
an
tic
−0.4
−0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
Figure 1: Share of selected categories/tags per nickname type
As a recommender system would rely on measurable differences
between these three groups, we analyzed them by comparing their
video category/tag distributions. While the majority of the video
labels are used equally often amongst the three groups, like for
blowjob, we also found some differences in terms of preferred
labels. Fig. 1 shows the relative difference between the share of
a specific category/tag within one nickname group compared to
the overall share of this category/tag for all comments. The most
salient observation is the increased amount of female user names
for the tags 69, lesbian, cunnilingus, kissing, licking and romantic,
while they are fewer represented compared to the other groups for
backroom, chubby and big-ass. Explicit vocabulary user names,
4http://www.andythenamebender.com
5http://www.variety.com/graphics/photos/
_storypics/TV-BAD-WORDS.pdf
however, can be found with augmented occurrences for the tags
naughty and big-ass, and appear rarer in romantic.
Recommendation of Tags: Besides the video recommendation for
users, improving the completeness and quality of the category/tag
assignments as well as query expansion with related categories/tags
might also be of interest. We therefore analyzed the relationship
between the categories/tags by mining for association rules. We used
a minimum support value of 5 and a lower confidence bound of 50%.
While not accessing the large number of rules with measures of rule
relevance, we could identify some interesting rules as reported in
Table 2, highlighting the potential to suggest additional tags and
thus completing the categorization of the videos. Though including
the nickname categories of Section 3, we could not identify any
interesting rules containing them.
Conclusion Premise Supp in # Conf in %
female-friendly kissing, romantic 2,170 91.7
drunk reality, russian-students 1,113 100.0
british stockings, senior 315 100.0
nerd glasses, ponytail 7 100.0
Table 2: Selected association rules between video categories/
tags with absolute support and confidence
4. CONCLUSION
Processing textual data associated with pornographic media requires
methods which can effectively deal with a number of problems. The
user-generated data obtained from these sites tends to use highly
colloquial language or slang that is not covered by any common
lexical resource (e.g. WordNet). Privacy issues and the fact that
very little of the actual data is publicly accessible makes it hard to
get information about individual users.
Nevertheless, we believe that mining such data is likely to yield
interesting conclusions about both a site’s contents and its users. In
this paper we reported on preliminary experiments using a data set
which we created by crawling the publicly accessible contents of
youporn.com. Though we could not distinguish between disjunct
preference profiles for the three nickname categories, we revealed
existing differences in their commenting behavior. In order to an-
alyze these differences in more depth, we plan on extending our
nickname labeling process with a broad covering, yet fine-grained
category scheme, capturing for example nicknames like Jabba the
Hutt as a movie character or Camille Crimson as a porn actress. For
result interpretation, we head towards a collaboration with sociolo-
gists, which we believe is recommendable also for other descriptive
analysis of our data set. Furthermore, we will investigate the benefit
of organizing categories/tags in a hierarchical scheme for content
retrieval.
Acknowledgements. Johanna Völker is financed by a Margarete-
von-Wrangell scholarship of the European Social Fund (ESF)
and the Ministry of Science, Research and the Arts Baden-
Württemberg.
5. REFERENCES
[1] H. Bechar-Israeli. From "Bonehead" to "cLoNehEAd":
Nicknames, Play and Identity on Internet Relay Chat. J.
Computer-Mediated Communication, 1(2), 1995.
[2] W. Stommel. Mein Nick bin ich! Nicknames in a German
Forum on Eating Disorders. J. Computer-Mediated
Communication, 13(1):141–162, 2007.


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