Yana Volkovich Moscow 2013.pptx

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Beyond Common Knowledge on the Internet

Moscow, May 16th, 2013

Yana Volkovich

Barcelona Media Innovation Center, Barcelona, Spain


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, 16 , 2013

Barcelona Media Innovation Center, ,


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Acknowledgment

Barcelona Media Innovation Center carries out applied research focused on the

needs of the Media and Communications industry in Spain and Brazil

Social Media group

Pablo Aragn

Karolin Kappler*

Andreas Kaltenbrunner

Jessica G. Neff*

David Laniado

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Knowledge on the Internet

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Knowledge is about

objects

&

connections between these objects

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Network of objects

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Knowledge is about

objects

&

connections between these objects

Knowledge on the Internet is about

Wikipedia

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Beyond common knowledge on the Internet

Wikipedia as a global memory place

Wikipedia as the largest existing collaborative projects

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Networks of individuals: Biographical Social Networks

Networks of social entities: Sister-city networks

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Biographical Social networks

Is history made by great man?

or

is great man made by history?

undoubtedly social connections shape history

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Biographical Social networks

Wikipedia as global collective memory place

allows to extract from biographies how social links are recorded across cultures

to generate networks of links between biographical articles

Questions:1. Who are the most central characters in these networks?

2. Do culture related peculiarities exist?

3. Which cultures are more similar?

4. What is the shared knowledge about connections between persons across cultures?

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Biographical Social Networks

data extraction

Selected the 15 largest language editions of Wikipedias

Starting point: 296 511 biographies from the English Wikipedia (from Dbpedia)

Identified the corresponding articles (when existing) on the remaining 14 languages

Generated a directed biographical network for each language version:

nodes -> persons

edges -> links between the articles of the corresponding persons

Manage alternative titles of articles: track redirects

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[redirects]

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[redirects]

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[redirects statistics]

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[different language networks]language code # nodes # edges

averageclustering

coefficient

% nodes inGiant

component

average

path lengthreciprocity diameter

English en 198 190 928 339 0.03 95% 6.53 0.17 43

German de 62 402 260 889 0.05 94% 6.83 0.14 33

French fr 51 811 283 453 0.06 96% 6.11 0.15 36

Italian it 35 756 190 867 0.06 95% 6.28 0.14 42

Spanish es 34 828 169 302 0.06 97% 6.29 0.16 36

Japanese ja 26 155 109 081 0.08 96% 6.47 0.20 26

Dutch nl 24 496 76 651 0.08 94% 7.91 0.18 37

Portuguese pt 23 705 85 295 0.07 94% 6.98 0.18 45

Swedish sv 23 085 60 745 0.07 91% 8.27 0.20 46

Polish pl 22 438 50 050 0.08 85% 8.94 0.16 43

Finish fi 18594 44 941 0.07 87% 7.80 0.17 30

Norwegian no 18 423 49 303 0.09 83% 8.31 0.22 48

Russian ru 16 403 34 436 0.06 87% 9.10 0.10 35

Chinese zh 11 715 44 739 0.17 91% 7.20 0.20 32

Catalan ca 11 027 42 321 0.09 93% 7.14 0.17 32

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low clustering: with exception for Chinese c=0.17

two persons are rarely mutually connected: parasocial interactions

one-sided interpersonal relationships in which one part knows a great deal about the other,

but the other does not

a person is influenced by the works of somebody who died decades before


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Who are the most central characters in these networks?

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Centralities

degree centrality is the number of links incident upon a node

betweenness centrality counts the number of shortest paths

between other nodes passing this node

Example for centralities measures:

A has the highest degree and D has the highest betweenness centrality

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Most central persons in the English Wikipedia

The top 25 persons in the English Wikipedia ranked by in-degree. Ranks for out-degree,

betweenness and PageRank in parenthesis

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Do culture related peculiarities exist?

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Biographical Social NetworksMost central persons in different language Wikipedias

Most central persons in different language Wikipedias are known to be (or have been) highly

influential : political leaders, revolutionaries, famous musicians, writers and actors

Hitler, Bush, Obama dominate in almost all top rankings

Top ranked in many languages reflect country specifities

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Which cultures are more similar?

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Jaccard coefficient: Given the set of links A and B of two networks

J=|AB|/|AUB|

the ratio between the number of links present in both networks (their intersection)

and the number of links existing in their union

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What is the shared knowledgeabout

connections between persons across cultures?

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Intersection of networks in different languages

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http://localhost/var/www/apps/conversion/tmp/scratch_4//localhost/Users/liefje/Dropbox/Documents/2012/networks_wikisym2012/cameraReady/figures/intersection13_boundedV2-eps-converted-to.pdf


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[to-gos]

Global social network measures are largely similar for all

networks

Most central persons unveil interesting peculiarities about thelanguage communities

Networks are more similar for geographically or linguistically

closer communities

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Network of Social Entities

Analysis of institutional (sister city) relations

via elsief1 @Flickr

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Network of sister cities

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[Motivation]

Institutional partnership between two cities or towns with the

aim of cultural and economical exchange

These relations had never been analyzed before

to understand

social,

geographical,

and economic

mechanisms of city pairings

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Interesting facts:

The earliest form of town twinning in Europe was between the German city

of Paderborn and the French city of Le Mans in 1836

Coventry twinned with Stalingrad (Volgograd) and with Dresden: all threecities having been heavily bombed during the war

Many German cities still are twinned with other German cities:

Hanover and Leipzig or Hamburg and Dresden

Tashkent was twinned with Seattle, Washington in 1973 and became the first

Soviet city to be twinned with one in the US

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Example for Wikipedia article used for data extraction

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Data extraction process

automated parser and a manual cleaning process.

Google Maps API to geo-locate cities.

Disclaimer No central register

User generated data (only 30% of reciprocal connections)

No guarantee that the dataset is complete

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network

number of

nodes

number of

edges

clustering

coefficient

% nodes in

giantcomponent

average path length

city network 11 618 15 225 0.11 61.35 6.74

country

network207 2 933 0.43 100 2.12


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[Top 20 cities ranked by degree]

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city degree betw. centrality

Saint Petersburg 78 1

Shanghai 75 4

Istanbul 69 12

Kiev 63 5

Caracas 59 23

Buenos Aires 58 36

Beijing 57 124

So Paulo 55 24

Suzhou 54 6

Taipei 53 20

Izmir 52 3

Bethlehem 50 2

Moscow 49 16

Odessa 46 8

Malchow 46 17

Guadalajara 44 9

Vilnius 44 14

Rio de Janeiro 44 29

Madrid 40 203

Barcelona 39 60


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[Top 20 cities countries ranked by degree]

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country degree betw. centrality

USA 4520 1

France 3313 3

Germany 2778 6

UK 2318 2

Russia 1487 9

Poland 1144 33

Japan 1131 20

Italy 1126 7

China 1076 4

Ukraine 946 27

Sweden 684 14

Norway 608 22

Spain 587 11Finland 584 35

Brazil 523 13

Mexico 492 21

Canada 476 28

Romania 472 32

Belgium 464 23

the Netherlands 461 16


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[Assortativity]Measure ofdiversityin network: do nodes having many connections

preferentially interact with one another or with poorly connected nodes?

Degree assortativity by city: Cities with many connections tend to be

connected with cities with many connection and vice-versa

Relations are assortative by country: Gross Domestic Product per capita;

Human Development Index;

Political Stability Index

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Comparison of distances between two pairs of

connected sister-cities

random (not necessarily connected) cities

An evidence of the Death of Distance (F. Cairncross The Death of Distance: How the

Communications Revolution Is Changing our Lives)

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[to-gos]

Assortative mixing with respect to degree, economic and

political country indexes.

Sister-city relationships reflect country preferences

Geographic distance between sister cities does not influence

city pairing

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Wikipedia as the largest existing

collaborative projects

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Wikipedia visible side

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Wikipedia article talk pages

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The hidden side of Wikipedia

since 2007 growth of Wikipedia has notably slowed down (B. Suh et al.;

The singularity is not near: slowing growth of Wikipedia; 2009)

The hidden side of Wikipedia is gaining importance

article talk pages explicit coordination and discussion

user talk pages personal communications (sort ofpublic inbox)

Article Barack Obama: discussion split into 72 pages

22 000 comments in the article talk pages (17 500 edits done to the article)

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The hidden side of Wikipedia

[motivation]

Unlike in other online discussion spaces

in Wikipedia

the users discuss to reach consensus and to coordinate their activity with each other

Detect patterns of interaction in the communications between the

Wikipedians

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Discussion tree for article Presidency of

Barack Obamared root (the article)

blue structural nodes

green anonymous comments

grey registered comments

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Discussion trees

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SlashdotWikipedia


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Discussion trees

Number of users involved

Number ofchains of length >= 3, or consecutive replies between two users

example chain of length 3: A B A good indicator ofconflictive discussions

85% of articles have 10 comments

15 000 articles have 100 comments

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Most discussed Wikipedia articles

[Top 20 ordered by number of chains]

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Article discussions categorisation

[structural differences among discussions

from different macro-categories ]

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Wikipedians networks

[to-gos]

the number of chains of direct replies between pairs of users

seems to be a good indicator of contentious discussion topics

significant differences in discussions from different semantic

areas

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Wikipedia brings political opponents

together

2004 U.S. presidential campaign: political blogs served as a prominent information

source regarding the campaign and candidates

conservative and liberal political blogs primarily link to other blogs with their same

political orientation (Adamic L, Glance N; The political blogosphere and the 2004U.S. election: Divided they blog, 2005)

people tend to read blogs that reinforce, rather than challenge, their political

beliefs (Lawrence E, Sides J, Farrell H; Self-segregation or deliberation? Blog

readership, participation, and polarization in American politics, 2010)

strong evidence of political polarization on Twitter (e.g. Aragn P, Kappler K,

Kaltenbrunner A, Laniado D, Volkovich Y; Communication Dynamics in Twitter

during Political Campaigns: the Case of the 2011 Spanish National Election, 2013)

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togetherWhat are the identity and representation practices of users who claim

their affiliation to a party within the Wikipedia community?

Do we see a division in patterns of participation along party lines?

Do users exhibit a preference for interacting with members of their same

political party?

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togetherWhat are the identity and representation practices of users who claim

their affiliation to a party within the Wikipedia community?

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together1,390 members of the Wikipedia community who explicitly proclaimed their

political affiliation as either a Republican or Democrat

conservative ideology: This user is pro-life, This user supports LEGAL

immigration, and this user thinks the global warming issue has been immensely

exaggerated

liberal ideology: This user supports the legalization of same-sex marriage, This

user is pro-choice, and This user supports immigration and the right to travel

freely upon the planet we share

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together

Do we see a division in patterns of participation along party lines?

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Most edited articles

Political (relating to a political issue or a politician, e.g. United States

Presidential Election, 2008; George Bush)

Conservative (related to a conservative politician, commentator, or

issue, e.g. Rush Limbaugh)

Liberal (related to a liberal politician, commentator, or issue, e.g. Al

Gore)

Neutral (political in nature, but not partisan, e.g. European Union,

September 11 attacks)

Not Political (e.g. Britney Spears, 2008 Summer Olympics)

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Most edited articles

100 most edited articles, Democrats and Republicans had 44 articles in common.

Democrats: 38 articles with political topics (15 liberal, 15 conservative, and 8

neutral)

Republicans: 35 articles with political topics (7 liberal, 17 conservative, and 11

neutral)

All users: 22 articles with political topics (5 liberal, 3 conservative, and 14 neutral)

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togetherDo users exhibit a preference for interacting with members of their same

political party?

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Cross-party interactions

Editors appear to be equally likely to engage conversations with users from

the other party as with users from the same party

Levels of conflict are high both within and across parties when the

discussion threads dealt with political or other potentially controversial

topics

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together [to-gos]the lack of preference to interact with same-party members in the context

of article discussions does not indicate the same polarization that has been

observed in other contexts

Wikipedian identity seems to predominate over party identity

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References

J.G. Neff, D. Laniado, K. Kappler, Y. Volkovich, P. Aragon, and A. Kaltenbrunner; Jointly they

edit: examining the impact of community identification on political interaction in

Wikipedia.; in PLOS ONE, 2013

A. Kaltenbrunner, P.Aragon, D. Laniado, and Y. Volkovich;Not all paths lead to Rome:

Analysing the network of sister cities.; in IWSOS2013

P Aragon, A Kaltenbrunner, D Laniado, and Y Volkovich, Biographical Social Networks on

Wikipedia: A cross-cultural study of links that made history.; in 8th International

Symposium on Wikis and Open Collaboration (WikiSym2012)

D. Laniado, R. Tasso, Y. Volkovich, and A. Kaltenbrunner, When the Wikipedians Talk:

Network and Tree Structure of Wikipedia Discussion Pages.; in Proceedings of the 5th

International AAAI Conference on Weblogs and Social Media (ICWSM2011); 2011

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Questions?

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