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Quantifying national
information interests using
the activity of Wikipedia
editors
F. Karimi, L. Bohlin, A. Sammoilenko, M. Rosvall , A. Lacincinetti
IceLab – Umeå University, Umeå, Sweden
GESIS – Leibnitz institute for social science, Cologne, Germany
Background
•  Historically spreading of information was
limited by the word of mouth, social
gatherings, written documents, roads that
connect cities etc.
•  Today advances in the electronic
communication have revolutionized how we
access and perceive information.
•  Many people around the planet have more
equal access to the same types of information.
Does the globalization of
technology enforce globalization
of information interests?
•  Information is being added and
edited by the citizens of the
world
•  No centralized authority
•  Widely used across different
countries
•  Available in many languages
•  Contains all sorts of information
Why Wikipedia?
•  > 1 million random Wikipedia articles in
all language editions including English
•  Retrieve the location of unregistered
editors.
•  > 23 million edits from 234 Countries and
248 languages
Method
Linking locations if they edit the
same WP topics.
?
Filtering model
•  Expected chance that two countries edit
the same article with size n, follows a
multinomial distribution.
< AB >= n(n−1)pA pB
var(AB)= n(n−1)pA pB((6−4n)pA pB +(n−2)(pA + pB )+1)
Extracting significant links
z-score =Σ ((empirical weight – expected weight) /
standard deviation)
•  Threshold: 5% pvalue with Bonferroni correction
Significant weight = z-score - threshold
Network of information interests
Clusters of countries with similar
information interests
Clusters of countries with similar
information interests
Austria
Germany
Belgium
Netherlands
SwitzerlandFrance
Luxembourg
Monaco
Liechtenstein
18 Italy, San Marino, Holy See (Vatican City)
aCaribbean Islands in the list are: Jamaica, Trinidad and Tobago, Saint Lucia, Barbad
and the Grenadines, Belize, US Virgin Islands, Dominica, Cayman Islands, British Virg
Supplementary Table 2 Top 10 Wikipedia articles
co-edit based on the filtering analysis
Rank DE-AT SE-NO
1 Christina Stürmer Tipuloid
2 Steffen Hofmann Dansban
3 Piefke Erik Ha
4 Klagenfurt Sweden
5 Kottan ermittelt Petter Jö
6 Der Bulle von Tölz Causerie
7 Puls 4 List of t
8 Austrian legislative election, 2006 Allmänn
9 Wolfgang Ambros Fredrik
10 Single cable distribution Daniel Ö
Middle East
North America
Russia &
Eastern
Europe
South America
Scandinavia
Interests highways
Conclusion
Despite the globalization of technology we
still care about local information.
Thank you!
@fariba_k
fariba.karimi@gesis.org
arXiv: 1503.05522

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Quantifying national information interests using the activity of Wikipedia editors

  • 1. Quantifying national information interests using the activity of Wikipedia editors F. Karimi, L. Bohlin, A. Sammoilenko, M. Rosvall , A. Lacincinetti IceLab – Umeå University, Umeå, Sweden GESIS – Leibnitz institute for social science, Cologne, Germany
  • 2. Background •  Historically spreading of information was limited by the word of mouth, social gatherings, written documents, roads that connect cities etc. •  Today advances in the electronic communication have revolutionized how we access and perceive information. •  Many people around the planet have more equal access to the same types of information.
  • 3. Does the globalization of technology enforce globalization of information interests?
  • 4. •  Information is being added and edited by the citizens of the world •  No centralized authority •  Widely used across different countries •  Available in many languages •  Contains all sorts of information Why Wikipedia?
  • 5.
  • 6. •  > 1 million random Wikipedia articles in all language editions including English •  Retrieve the location of unregistered editors. •  > 23 million edits from 234 Countries and 248 languages Method
  • 7. Linking locations if they edit the same WP topics.
  • 8. ?
  • 9. Filtering model •  Expected chance that two countries edit the same article with size n, follows a multinomial distribution. < AB >= n(n−1)pA pB var(AB)= n(n−1)pA pB((6−4n)pA pB +(n−2)(pA + pB )+1)
  • 10. Extracting significant links z-score =Σ ((empirical weight – expected weight) / standard deviation) •  Threshold: 5% pvalue with Bonferroni correction Significant weight = z-score - threshold
  • 12. Clusters of countries with similar information interests
  • 13. Clusters of countries with similar information interests Austria Germany Belgium Netherlands SwitzerlandFrance Luxembourg Monaco Liechtenstein 18 Italy, San Marino, Holy See (Vatican City) aCaribbean Islands in the list are: Jamaica, Trinidad and Tobago, Saint Lucia, Barbad and the Grenadines, Belize, US Virgin Islands, Dominica, Cayman Islands, British Virg Supplementary Table 2 Top 10 Wikipedia articles co-edit based on the filtering analysis Rank DE-AT SE-NO 1 Christina Stürmer Tipuloid 2 Steffen Hofmann Dansban 3 Piefke Erik Ha 4 Klagenfurt Sweden 5 Kottan ermittelt Petter Jö 6 Der Bulle von Tölz Causerie 7 Puls 4 List of t 8 Austrian legislative election, 2006 Allmänn 9 Wolfgang Ambros Fredrik 10 Single cable distribution Daniel Ö
  • 14. Middle East North America Russia & Eastern Europe South America Scandinavia Interests highways
  • 15. Conclusion Despite the globalization of technology we still care about local information.