Most Popular Contents Requested by Users in Different Wikipedia

Editions

Antonio J. Reinoso

, Juan Ortega-Valiente

, Roc´ıo Mu˜noz-Mansilla

and Carlos Le´on

LibreSoft Research Group, URJC, C/ Camino del Molino, Fuenlabrada, Spain

Department of ICT Engineering, UAX, Avda. de la Univerisdad, 1, Vva. de la Ca˜nada, Spain

Department of Computer Science and Automation, UNED, C/ Juan del Rosal, 16, Madrid, Spain

Keywords:

Wikipedia, Use Patterns, Trafﬁc Characterization, Content Categorization.

Abstract:

This paper aims to analyze how the most requested and contributed contents in Wikipedia may signiﬁcantly

vary depending on the considered edition. The on-line Encyclopedia has become a proliﬁc research topic,

mainly in aspects related to the assessment of its contents and in its evolution forecasting. However, very

little effort has been devoted to deal with the kind of use given to Wikipedia by its visitors, either occasional

or subscribers. Thus, our work aims to explore the utilization made of Wikipedia through a classiﬁcation

of the most requested and contributed contents in some of its editions. This way, we will be in position of

determining which type of contents attracts the highest numbers of visits and contributions in these editions

and which can be a good indicator of the use given to them by their respective community of users. Apart

from the subsequent comparison purposes, such examination may reveal interesting topics such as the trans-

mission of tendencies over the different Wikipedia editions, as well as particular user patterns exhibited by the

corresponding communities of users.

1 INTRODUCTION

Wikipedia continues to be an absolute success and

stands as the most relevant wiki-based platform. As a

free and on-line encyclopedia, it offers a rich collec-

tion of contents, provided in different media formats

and related to all the areas of knowledge. Undoubt-

edly, the Wikipedia phenomenon constitutes one of

the most remarkable milestones in the evolution of

encyclopedias. In addition, its supporting paradigm,

based in the application of collaborative and coopera-

tive efforts to the production of knowledge, has been

object of a great number of studies and examinations.

Wikipedia is organized in about 285

editions,

each corresponding to a different language. All these

editions add up more than 22 million articles

, which

correspond to encyclopedic entries about particular

subjects, events or people. Wikipedia articles not only

address topics from academic disciplines, such as sci-

entiﬁc or humanistic subjects, but also from areas re-

lated to music, sports, current events and so forth. Fo-

http://meta.wikimedia.org/wiki/List of Wikipedias

(Retrieved on 6 June 2012)

http://meta.wikimedia.org/wiki/List of Wikipedias#-

Grand Total (Retrieved on 6 June 2012)

cusing on the audience, the overall set of Wikipedia

editions attracts approximately 15,000 million visits

a month

. This fact can be seen as an absolute ar-

gument for the popularity gained by Wikipedia and

contributes to reinforce its massive acceptance by the

Internet community.

As a result of this relevance, Wikipedia has turned

into a subject of increasing interest for researchers

This way, quantitative examinations about its articles,

authors, visits and contributions have been performed

in different studies such as (Ortega et al., 2007) and

(Tony and Riedl, 2009). The quality and reliability of

the offered information has propitiated a proliﬁc re-

search ﬁeld where several techniques and approaches

have been conducted: (Korﬁatis et al., 2006), (Giles,

2005) and (Chesney, 2006). In addition, Wikipedia’s

growth tendencies and general evolution have also

been largely addressed in studies such as (Capocci

et al., 2006) and (Suh et al., 2009). Several other

works have focused on particular aspects, such as mo-

tivation: (Kuznetsov, 2006) and (Nov, 2007), con-

http://stats.wikimedia.org/EN/Sitemap.htm (Retrieved

on 6 June 2012)

http://en.wikipedia.org/wiki/Wikipedia:Academic -

studies of Wikipedia (Retrieved on 6 June 2012)

415

Reinoso A., Ortega-Valiente J., Muñoz-Mansilla R. and León C..

Most Popular Contents Requested by Users in Different Wikipedia Editions.

DOI: 10.5220/0004173104150418

In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD-2012), pages 415-418

ISBN: 978-989-8565-30-3

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

sensus: (Kittur et al., 2007), (Suh et al., 2007)

and (Vi´egas et al., 2007) or vandalism: (Priedhorsky

et al., 2007). By contrast, few studies (Urdaneta et al.,

2007), (Reinoso et al., 2010) or (Reinoso, 2011) have

been devoted to analyze the manner in which users in-

teract and make use of Wikipedia.

Therefore, our main objective is the classiﬁcation

and categorization of the most solicited content of

Wikipedia, as we consider that this is directly related

to the use given by users to the Encyclopedia. Unlike

many other previousstudies, which are fundamentally

based on surveys conducted for speciﬁc populations,

such as (Konieczny, 2007) or (Willinsky, 2007), our

methodological approach is based on the analysis of

a sample of the requests sent to Wikipedia by users.

With this, we consider that the focus of our analysis

is signiﬁcantly widened: users, Wikipedia editions,

temporal period...

The rest of the paper is organized as follows.

Next section presents the most relevant aspects of the

methodology conducted to develop our analysis. Af-

ter this, some important results are adequately pre-

sented. Finally the section dedicated to present our

conclusions and ideas for further work ﬁnishes the pa-

per.

2 METHODOLOGY

The Wikimedia Foundation has deployed a layer

of special Squid servers to deal with the incom-

ing trafﬁc directed to its several wiki-based projects.

Squids work as reverse proxy servers, and perform

web caching to avoid the operation of both web and

database servers placed behind them. When requested

contents can be found in their caches, users receive

their response directly from the Squids. Otherwise,

Squids ask web servers for the solicited resource and,

once obtained, they send it to the user. Independently

of the way in which contents sent to users are ob-

tained, Squid servers write down in a log line data

related to each particular served request.

The analysis presented here is based on a sample

of the log lines stored by Squid servers. All the Squid

servers arranged by the Wikimedia Foundation pack

and send their log lines to a central aggregator host.

In this system, lines are managed by special log pro-

cessors that can write them to a given destination or

pipe them to other processes. In both cases, a sam-

pling factor is conﬁgured to determine the number of

lines to be processed. Our sample corresponds to the

whole 2009 and a sampling factor of the 1% was used

to extract it. As a result, we managed to receive one

in every hundred requests composing the trafﬁc to the

several projects maintained by the Wikimedia Foun-

dation. To summarize, more than 14,000 million log

lines have been analyzed as a part of this work.

Receiving log lines from a centralized system is

specially relevant for our analysis as it means that

our sample is made up of lines from all the Squids

deployed by the Wikimedia Foundation as a part of

its Content Delivery Network (CDN), which is com-

posed, at a glance, by two large groups of Squid clus-

ters (located, respectively, in USA and in the Nether-

lands) to whom requests are directed using geograph-

ical DNS balancing policies. This guarantees the het-

erogeneity of our data feed and prevents our results

from localized effects or trends due to sociocultural

particularities that may arise if we only examined log

lines from particular Squids or groups o them.

Once the log lines have been received in our sys-

tems, they become ready to be analyzed by the tool

developedfor this purpose: The WikiSquilter project

The analysis consists in a characterization performed

in a three-step process: parsing, ﬁltering and storage.

Firstly, log lines are parsed to extract relevant infor-

mational elements from the users’ requests. Secondly,

these informational elements are ﬁltered to determine

if the corresponding requests ﬁts the directives of the

analysis. Finally, information ﬁelds from requests

considered of interest are normalized and stored in a

database for statistical examinations.

Important information concerning users’ requests,

like their date or if they led to a write operation in the

database, can be directly obtained from the log lines

ﬁelds. Nevertheless, most of the data needed for our

analysis is embedded in the URL constituting each

request. Therefore, URLs have to be parsed in order

to extract their relevant informational elements:

1. The Wikimedia Foundation project to which the

URL is directed.

2. The corresponding language edition of the

project.

3. When the URL requests an article, its namespace.

4. The title of every requested article.

This information allow us to isolate the requests

directed to the Wikipedia project from all the ones

that compose our sample, as we have just focused on

targeting speciﬁc editions. Speciﬁcally, we have con-

sidered only the top-ten editions regarding both their

number of articles and visits. These editions are the

German, English, Spanish, French, Italian, Japanese,

Dutch, Polish, Portuguese and Russian ones.

As the articles’ titles are available, we obtained

the ones corresponding to the 65 most visited and

http://sourceforge.net/projects/squilter (Retrieved on 6

June 2012)

KEOD2012-InternationalConferenceonKnowledgeEngineeringandOntologyDevelopment

416

contributed articles in the German, English, Spanish

and French Wikipedias during 6 random months from

2009, and assigned them to an speciﬁc set of cate-

gories based in a previous one described in (Spoerry,

2007). At the moment, this is the only step that has to

be performed manually, as there are no categorization

systems that can be fed only with the few words form-

ing a title. The list of categories is presented below:

• Entertainment (ENT) and Current Issues (CUR).

• Politics and War (POL) and Geography (GEO).

• Information and Communication Technologies

(ICT).

• Science (SCI) and Arts and Humanities (ART).

• Sexuality (SEX).

3 ANALYSIS AND RESULTS

This section presents our most remarkable ﬁndings in

respect to the categorization of the most visited and

contributed articles in the considered Wikipedia edi-

tions. Such results can be considered as representa-

tive of the type of use made of these editions by their

corresponding communities of users. Regarding vis-

its as those requests devoted just to get the informa-

tion contained in the Wikipedia’s articles and that do

not entail any kind of contribution nor any other type

of action, Table 1 presents the categories of contents

most repeatedly demanded in the analyzed editions.

According to this table, we can see how in all the

studied Wikipedias, except the Spanish one, the cate-

gory related to Entertainment topics attracts most of

the visits. However, in the Spanish edition, Scientiﬁc

articles are the ones that attract most of the users’ at-

tention. We estimate that this fact could be related

with the high percentage that ICT-related Information

and Communication Technologies contents grab in

the Spanish Wikipedia which is considerably higher

than in the other analyzed editions. As the Entertain-

ment category corresponds to those topics related to

movies, celebrities, video games, music bands, etc.,

results from Table 1 could suggest that Wikipedia is

not considered as a primary source for academic or

scientiﬁc information by users in the editions where

this category is the most popular one. Considering

the common range of ages to which these types of

contents are directed to, these results could also al-

low to infer that a large number of Wikipedia vis-

itors in these editions are young people. This fact

could be reinforced by the great percentage achieved

by articles related to sexual topics in some of the

Wikipedia editions with the highest percentages in

Table 1: Categorization of the 65 most visited pages in the

German, English, Spanish and French Wikipedias.

Category DE EN ES FR

MAIN 1,54% 1,54% 1,54% 1,54%

CUR 9,23% 17,85% 5,23% 11,08%

GEO 24,62% 7,69% 13,23% 21,85%

ICT 7,08% 5,23% 12,31% 6,15%

ENT 31,08% 44,92% 16,00% 27,69%

POL 9,85% 8,92% 5,23% 6,77%

SCI 5,54% 3,38% 24,00% 4,31%

ART 4,31% 0,92% 20,92% 13,85%

SEX 6,77% 8,92% 0,31% 0,00%

UND. 0,00% 0,62% 1,23% 1,54%

Entertainment-related contents. Unfortunately, it is

not possible to go beyond these mere speculations

with the data currently available to us, as Wikimedia

Foundation’s strong policies about individual’s pri-

vacy and conﬁdentiality rights do not allow us to get

any demographical information about Wikipedia vis-

itors. On the other hand, Table 2 presents the cat-

egorization resulting from classifying the most con-

tributed articles in the same Wikipedias. As it can be

shown, articles related to the Entertainment category

gather most of contributions in the English and Span-

ish Wikipedias. In the English edition, articles related

to current events rank in the second position, whereas

in the Spanish edition the same position is occupied

by articles corresponding to geographical topics. In

the German edition, articles devoted to current events

receive the highest number of contributions whereas

Humanistic articles are in the second position. Fi-

nally, in the French Wikipedia articles related to Hu-

manities are the most contributed while those corre-

sponding to Entertainment are in the second position.

It is common to assume that Entertainment and

Current topics should be the ones most easy to con-

tribute to as they do not require users to deal with aca-

demic subjects nor a previous education, training or

context on a given matter. However, it is really in-

teresting to note how, for example, though scientiﬁc

articles are the most popular in the Spanish edition of

Wikipedia, is in the German one when they achieve

the highest percentage of contributions. This means

that users of the Spanish Wikipedia do consume a lot

of scientiﬁc information but the contributions, in the

opposite, do not target at all the same subjects.

As we can see, there are signiﬁcant differences not

only in the topics that receivesmost of users’ attention

and contributions in the analyzed Wikipedia editions,

but also in the topics most visited and edited for each

edition individually considered. This gives us an idea

of the different types of use given to the Encyclopedia

in the different communities of users and the different

patterns of use depending of the considered kind of

request even inside a particular edition.

MostPopularContentsRequestedbyUsersinDifferentWikipediaEditions

417

Table 2: Categorization of the 65 most contributed pages in

the German, English, Spanish and French Wikipedias.

Category DE EN ES FR

MAIN 0,00% 0,00% 0,00% 0,00%

CUR 19,69% 25,23% 5,23% 9,23%

GEO 15,38% 9,85% 17,54% 23,69%

ICT 7,69% 2,15% 1,85% 0,92%

ENT 14,77% 36,31% 46,46% 25,23%

POL 12,62% 9,54% 6,46% 7,38%

SCI 7,38% 1,54% 7,08% 4,62%

ART 17,23% 14,46% 13,85% 27,69%

SEX 0,31% 0,00% 0,00% 0,31%

UND. 4,92% 0,92% 1,54% 0,92%

4 CONCLUSIONS AND FUTURE

WORK

As our analysis revealed, there are considerable dif-

ferences among the types of contents most repeat-

edly visited and contributed in the different editions

of Wikipedia. If we focus on particular editions, we

can, even, found signiﬁcant differences amongst the

topics that grab users’ attention and those that receive

the most of the contributions. This can be regarded as

different patterns of use characterizing the utilization

made of the Encyclopedia by the different communi-

ties of users. Such an analysis of behavioral features

would beneﬁt from the inclusion of other aspects such

as sociological or sociocultural considerations.

In addition, we could extend our analysis with

the development of an automatic categorization sys-

tem capable of performing a wide-scope classiﬁca-

tion. Nowadays, some of our best efforts are directed

towards this target. In addition, topics involved in

other types of requests, such as history reviews or

searches, could be also categorized to obtain a more

complex proﬁle of requested contents. For sure, some

form of users related information, though hashed or

anonymized, will contribute to deﬁne more accurate

patterns of use as well as visitors/contributors proﬁles.

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