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 on the Internet
Knowledge is about
objects
&
connections between these objects
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Network of objects
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Knowledge on the Internet
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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Wikipedia as a global memory place
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Wikipedia as a global memory place
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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Biographical Social Networks
[redirects]
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Biographical Social Networks
[redirects]
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Biographical Social Networks
[redirects statistics]
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Biographical Social Networks
[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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Biographical Social Networks
Who are the most central characters in these networks?
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Biographical Social Networks
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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Biographical Social Networks
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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Biographical Social Networks
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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Biographical Social Networks
Which cultures are more similar?
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Biographical Social Networks
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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Biographical Social Networks
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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Biographical Social Networks
[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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Network of sister cities
[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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Network of sister cities
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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Network of sister cities
Example for Wikipedia article used for data extraction
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Network of sister cities
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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Network of sister cities
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Network of sister cities
[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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Network of sister cities
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Network of sister cities
[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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Network of sister cities
[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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Network of sister cities
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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Network of sister cities
[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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Wikipedia brings political opponents
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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Wikipedia brings political opponents
togetherWhat are the identity and representation practices of users who claim
their affiliation to a party within the Wikipedia community?
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Wikipedia brings political opponents
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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Wikipedia brings political opponents
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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Wikipedia brings political opponents
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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Wikipedia brings political opponents
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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