Presented in the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining ASONAM 2017 Sydney, Australia, 31 July - 03 August, 2017

1. Multiplex Media Attention and Disregard
Network among 129 Countries
Haewoon Kwak Jisun An
Qatar Computing Research Institute
Hamad Bin Khalifa University
The 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining ASONAM 2017 Sydney, Australia, 31 July - 03 August, 2017

2. Outline
• Paper title 101
• Motivation
• Data collection - Unfiltered.news
• Multiplex network analyses ① ② ③ ④ ⑤
• Summary & future work

3. Multiplex Media Attention and Disregard
Network among 129 Countries

4. Multiplex Media Attention and Disregard
Network among 129 Countries
Multiplex network?
Media attention?
Media disregard?

5. Multiplex Media Attention and Disregard
Network among 129 Countries
3 Multiplex network?
1 Media attention?
2 Media disregard?

6. Media attention
• What (news) media pay attention to
• What news media report
The NYTimes reports news about Trump
The NYTimes pays attention to Trump
NYTimes Trump
1

7. Media disregard
• What (news) media do not pay attention to
• What news media do not report
• Key challenges:
• How to distinguish what media did not know (and thus
could not report) a certain incident from what media
knew it but intentionally ignored it
• We get insights from how news industry works.
2

8. How can news media report events
happening at every corner of the world?
• Open offices in every country and hire journalists there
• Infeasible due to financial reasons
• Is there anyone who gathers news reports from the world
and sells “collected” news to news media again?
• This is exactly what news agencies do.

9. Reasonable assumptions to
capture media disregard
• If many news media outlets report a certain incident, that
piece of information was probably circulated by news
agencies as well.
• In this case, if there are news media outlets that do not
report that incident, we can reasonably assume that they
disregard it.

10. Multiplex network
• A multilayer network is a network made up by multiple
layers, each of which represents a given operation mode,
social circle, or temporal instance.
• In a multiplex network, each type of interaction between
the nodes is described by a single layer network.
Text from http://cosnet.bifi.es/network-theory/multiplex-networks/
Image from https://github.com/gajduk/social-networks-analysis-wan-bms
3

11. Multiplex Media Attention and Disregard
Network among 129 Countries

12. Why is this interesting?

13. Critical role of news media
• Even in the era of social media, our understanding of the
world is still dominantly shaped by news media [2]*.
*Reference number in the talk is the same as that in the paper.
http://www.journalism.org/2016/07/07/pathways-to-news/

14. https://www.ted.com/talks/alisa_miller_shares_the_news_about_the_news

15. Bias in foreign news coverage
• What we see, read, and hear about other countries is a
result of the gatekeeping by the journalists and of the
social, economic, and political relationships across
countries [3].
➡ Ideological discourses of hierarchy and inequality are
articulated throughout the mediated representation [11].
✓Understanding media attention is essential to detect bias
in news media and make news better.

16. Previous literature on
foreign news coverage
• Theory of news values
• Why some countries are more likely to be covered than
other countries (e.g., USA, UK, China, …)
• Systematic factors of international relationships
• Why one country covers a certain country more than
other countries (e.g., Japan frequently covers S. Korea)

17. Algeria
Senegal
SloveniaTunisia
Luxembourg
Bulgaria
Italy
Greece
Bahrain
Libya
Morocco
Palestine
Lebanon
Mauritania
Ukraine
Bosnia and Herzegovina
Georgia
Armenia
Croatia
Poland
Estonia
Serbia
Latvia
Slovakia
Russia
Netherlands
Belarus
Moldova
Uzbekistan
Azerbaijan
Montenegro
France
Iran
Cyprus
Belgium
Turkey
Réunion
Paraguay
Dominican Republic
Brazil
Guyana
Portugal
Cuba
Uruguay
Jamaica
Bolivia
Barbados
Costa Rica
Venezuela
El Salvador
Mexico
Trinidad and Tobago
HondurasPhilippines
Nicaragua
Peru
India
Canada
Spain
Guatemala
Pakistan
Czech Republic
Zimbabwe
Bangladesh
Uganda
Kazakhstan
Kyrgyzstan
Sri Lanka
Lithuania
Kenya
Thailand
Vietnam
Japan
Fiji
Macau
Hong Kong
Puerto Rico
China
New Zealand
Angola
Haiti
Argentina
Hungary
United States
of AmericaColombia
Sweden
Liechtenstein
Nigeria
Denmark
United Kingdom
Oman
Qatar
Kuwait
Sudan
United Arab Emirates
Saudi Arabia
Egypt
Malta
French Polynesia
Romania
Mali
Germany
Austria
South Africa
Syria
Finland
Switzerland
Norway
Cameroon
Israel
Yemen
Iraq Jordan
Australia
Ecuador
Panama
Ireland
Taiwan
Malaysia
Singapore Chile
Nepal
Limitations in previous work
• Pairwise modeling is not enough to model complex nature
of media attention among multiple countries.

18. Our approach
• We build a multiplex media attention and disregard
network (MADN) among countries and analyze its
structural characteristics.
Algeria
Senegal
SloveniaTunisia
Luxembourg
Bulgaria
Italy
Greece
Bahrain
Libya
Morocco
Palestine
Lebanon
Mauritania
Ukraine
Bosnia and Herzegovina
Georgia
Armenia
Croatia
Poland
Estonia
Serbia
Latvia
Slovakia
Russia
Netherlands
Belarus
Moldova
Uzbekistan
Azerbaijan
Montenegro
France
Iran
Cyprus
Belgium
Turkey
Réunion
Paraguay
Dominican Republic
Brazil
Guyana
Portugal
Cuba
Uruguay
Jamaica
Bolivia
Barbados
Costa Rica
Venezuela
El Salvador
Mexico
Trinidad and Tobago
HondurasPhilippines
Nicaragua
Peru
India
Canada
Spain
Guatemala
Pakistan
Czech Republic
Zimbabwe
Bangladesh
Uganda
Kazakhstan
Kyrgyzstan
Sri Lanka
Lithuania
Kenya
Thailand
Vietnam
Japan
Fiji
Macau
Hong Kong
Puerto Rico
China
New Zealand
Angola
Haiti
Argentina
Hungary
United States
of AmericaColombia
Sweden
Liechtenstein
Nigeria
Denmark
United Kingdom
Oman
Qatar
Kuwait
Sudan
United Arab Emirates
Saudi Arabia
Egypt
Malta
French Polynesia
Romania
Mali
Germany
Austria
South Africa
Syria
Finland
Switzerland
Norway
Cameroon
Israel
Yemen
Iraq Jordan
Australia
Ecuador
Panama
Ireland
Taiwan
Malaysia
Singapore Chile
Nepal
Macau
Honduras
Ireland
Paraguay
Chile
Sudan
Uzbekistan
Israel
Zimbabwe
Iran
Belgium
Romania
Nicaragua
France
Croatia
Denmark
Argentina
Costa Rica
Peru
Finland
Slovakia
Réunion
Iceland
New Zealand
Slovenia
Portugal
BarbadosEcuador
Moldova
Belarus
Czech Republic
Lebanon
Bolivia
Jordan
Bosnia and Herzegovina
Switzerland
Libya
El Salvador
Senegal
Austria
Hungary
Bulgaria
Taiwan
LithuaniaGuinea
Canada
Latvia
Saudi Arabia
Syria
Australia
Kazakhstan
Yemen
Egypt
United Kingdom
BangladeshSingapore
Oman
Netherlands
Algeria
Hong Kong
China
Malaysia
Brazil
India
Ukraine
Ghana
Kuwait
United Arab Emirates
Palestine
Iraq
Morocco
Malta
Liechtenstein
Uganda
Zambia
Azerbaijan
Haiti
Armenia
Malawi Luxembourg
South Africa
Montenegro
Uruguay
Trinidad and Tobago
GreeceJapan
Serbia
Sri Lanka
Puerto Rico
Nigeria
Philippines
Russia
Turkey
Jamaica
Pakistan
Dominican Republic
Mali
Nepal
French Polynesia
Guyana
Angola
Mauritania
Italy
Spain
United States of America
Estonia
Venezuela
Norway
Vietnam
Fiji Sweden
Kenya
Georgia
Cameroon
Kyrgyzstan
Cuba
Guatemala
Poland
Bahrain
GermanyQatar
Tunisia
Cyprus
Thailand Mexico
South Korea
Panama
Indonesia
Colombia
Attention Disregard

19. Datasets we need
• Collect news from many countries in the world
• Handle English and non-English contents
• Do not filter specific types of news

20. Candidate: GDELT?
• The GDELT datasets http://www.gdeltproject.org/
• Supported by Google Jigsaw
• Monitors news media around the world in over 100
languages
• Actively studied in recent years [9]
• Not appropriate for our work
• Filters news according to predefined 300+ categories
Two Tales of the World: Comparison of Widely Used World News Datasets GDELT and EventRegistry,
Haewoon Kwak and Jisun An, ICWSM (4pg), 2016

21. Unfiltered.news
• http://unfiltered.news run by Jigsaw

22. Why Unfiltered.news?
• Retrieve data from Google News
• Covers 100+ countries
• Translate contents in major languages

23. Data collection
• Over 212 days, for each country, we collect
• Daily k topics mentioned more than other topics
• Daily k topics mentioned less than news media in other
countries mentioned (former definition is in the paper)
✴ 100 is the maximum number of topics that
Unfiltered.news offers for a given day.

24. How to choose k
• Considering human capacity for processing information*,
we choose k=10.
0
50
100
150
200
0 10 20 30 40 50 60 70 80 90 100
k
NumberofCountries
No countries (k=100)
without missing data
129 countries without missing data (k=10)
Miller, George A. "The magical number seven, plus or minus two: some limits on our capacity for processing information."
Psychological review 63.2 (1956): 81.

25. Build MADN
NA: Attention network
ND: Disregard network
+

26. Modeling weighted directed
network
Country A Country B
Topic 1
Topic 2
Topic 3
Country B
Topic 5
…
Topic 10
7 March 2016
Country B
Topic 2
Topic 3
Topic 4
Topic 5
…
Topic 10
10 March 2016
Topic 1
Topic 2
Topic 3
Topic 4
Topic 5
…
Country B
12 April 2016
weight=3
The top 10 topics mentioned more in country A

27. Basic topological
characteristics of MADN

28. Examining structures of
MADN
• Node level
• Dyadic level
• Triadic level
• Community level
• Network level
Bottom-up manner

29. Node level:
Country centrality

30. Country centrality
Attention network Disregard network

31. Country centrality
Small countries tend to
report foreign news more

32. Country centrality
The USA is placed in the brightest spotlight on
the stage of the news world [3]

33. Country centrality
Some countries appear in both

34. Country centrality
A country has different news values
for different countries

35. Dyadic level:
Media attention bias
Media attention asymmetry

36. Does one country pay attention to the
other country at one time and disregard
the same country at another time?
Country A Country B
Country A Country BND
NA
wA
wD
This shows the stability of news value of one country to another.
Let’s look into the relationship between wA and wD.

37. Density plot of wA
and wD
0
50
100
150
200
0 50 100 150 200
Link weight in NA
LinkweightinND
1.000000
7.389056
54.598150
403.428793
2980.957987

38. Most of links have high
weights in only one network
0
50
100
150
200
0 50 100 150 200
Link weight in NA
LinkweightinND
1.000000
7.389056
54.598150
403.428793
2980.957987

39. News value of one country to another
country is stable to some extent
0
50
100
150
200
0 50 100 150 200
Link weight in NA
LinkweightinND
1.000000
7.389056
54.598150
403.428793
2980.957987
Media attention is less flexible
and might have some country bias.

40. Characterizing a link by
adding w
A
and -w
D
Country A Country B
Country A Country B
wA
wD
w = wA - wD
w > 0: pay attention
w = 0: no significant pattern
w < 0: disregard

41. Relationship between two
countries
Country A Country B
Country A Country B
Country A Country B
Country A Country B
Country A Country B
1. exchanging attention 2. one-way attention
3. exchanging disregard 4. one-way disregard
5. exchanging attention & disregard

42. Relationship between two
countries
Country A Country B
Country A Country B
Country A Country B
Country A Country B
Country A Country B
1. exchanging attention 2. one-way attention
3. exchanging disregard 4. one-way disregard
5. exchanging attention & disregard
11.2% 22.9%
16.6% 40.1%
7.5%

43. Relationship between two
countries
Country A Country B
Country A Country B
Country A Country B
Country A Country B
Country A Country B
1. exchanging attention 2. one-way attention
3. exchanging disregard 4. one-way disregard
5. exchanging attention & disregard
11.2% 22.9%
16.6% 40.1%
7.5%
More than a half of country relationships
are unidirectional

44. Relationship between two
countries
Country A Country B
Country A Country B
Country A Country B
Country A Country B
Country A Country B
1. exchanging attention 2. one-way attention
3. exchanging disregard 4. one-way disregard
5. exchanging attention & disregard
11.2% 22.9%
16.6% 40.1%
7.5%
Geographical neighbors
are interested in each other.

45. Relationship between two
countries
Country A Country B
Country A Country B
Country A Country B
Country A Country B
Country A Country B
1. exchanging attention 2. one-way attention
3. exchanging disregard 4. one-way disregard
5. exchanging attention & disregard
11.2% 22.9%
16.6% 40.1%
7.5%
Mainly led by coverage of
China, Yemen, Saudi Arabia, or Ukraine

46. Relationship between two
countries
Country A Country B
Country A Country B
Country A Country B
Country A Country B
Country A Country B
1. exchanging attention 2. one-way attention
3. exchanging disregard 4. one-way disregard
5. exchanging attention & disregard
11.2% 22.9%
16.6% 40.1%
7.5%
Relationships between some local hubs
and global hub

47. Considering link weights
can reveal rich dynamics
Country A Country B
1. exchanging attention
Country A Country B
Country A Country B
or
or
…

48. Algeria
Senegal
SloveniaTunisia
Luxembourg
Bulgaria
Italy
Greece
Bahrain
Libya
Morocco
Palestine
Lebanon
Mauritania
Ukraine
Bosnia and Herzegovina
Georgia
Armenia
Croatia
Poland
Estonia
Serbia
Latvia
Slovakia
Russia
Netherlands
Belarus
Moldova
Uzbekistan
Azerbaijan
Montenegro
France
Iran
Cyprus
Belgium
Turkey
Réunion
Paraguay
Dominican Republic
Brazil
Guyana
Portugal
Cuba
Uruguay
Jamaica
Bolivia
Barbados
Costa Rica
Venezuela
El Salvador
Mexico
Trinidad and Tobago
HondurasPhilippines
Nicaragua
Peru
India
Canada
Spain
Guatemala
Pakistan
Czech Republic
Zimbabwe
Bangladesh
Uganda
Kazakhstan
Kyrgyzstan
Sri Lanka
Lithuania
Kenya
Thailand
Vietnam
Japan
Fiji
Macau
Hong Kong
Puerto Rico
China
New Zealand
Angola
Haiti
Argentina
Hungary
United States
of AmericaColombia
Sweden
Liechtenstein
Nigeria
Denmark
United Kingdom
Oman
Qatar
Kuwait
Sudan
United Arab Emirates
Saudi Arabia
Egypt
Malta
French Polynesia
Romania
Mali
Germany
Austria
South Africa
Syria
Finland
Switzerland
Norway
Cameroon
Israel
Yemen
Iraq Jordan
Australia
Ecuador
Panama
Ireland
Taiwan
Malaysia
Singapore Chile
Nepal
Backbone extraction [16]
• Applying disparity filter proposed by [16] and extracting
significant links (called a “backbone”)

49. Link characterization by:
1. Is the link from ci to cj significant to ci compared to other
links from ci?
2. Is the link from ci to cj significant to cj compared to other
links to cj?
ci cj ci cj
1. significant to ci? 2. significant to cj?

50. Relationship characterization by:
ci cj ci cj
ci cjci cj
1. Lij is significant to ci? 2. Lij is significant to cj?
3. Lji is significant to cj? 4. Lji is significant to ci?

51. Findings by relationship
characterization in N
A
• Most of the country relationships (91.2%) are non-significant
for both-countries.
• Neighboring countries tend to show a strong dependency of
media attention.
• Hub countries get the significant media attention but do not
return back well.
• Former colonial ties show dependent relationships.
• While the US (1st) and Syria (2nd) have similar PageRank, they
receive significant media attention from 53 and 11 countries,
respectively.

52. Triadic level:
Motif analysis

53. Network motif
• (Usually 3- or 4-) sized subgraphs that repeat in a given
network

54. Proportions of each motif are different
according to the types of networks
Superfamilies of Evolved and Designed Networks
Ron Milo, Shalev Itzkovitz, Nadav Kashtan, Reuven Levitt, Shai Shen-Orr, Inbal Ayzenshtat,Michal Sheffer, Uri Alon
Science, 303(5663), 2004

55. Well-known names of each
motif
Feed forward loop (FFL)Fan-out Cascade
Fan-in
Fully connected triad
Double feedback loop

56. Motif profiles of NA
and ND

57. Motif profiles of NA
and ND
Two networks are structurally very different

58. Motif profiles of NA
Transitive hierarchy is found.
(If A->B and B->C, then A->C)
Feed forward loop (FFL)

59. Motif profiles of ND
Led by star-shaped subnetworks
Fan-out Fan-in

60. Motif profiles of ND
Transitivity does not hold in ND
Cascade

61. Significant colored motifs in
MADN
Attention
Disregard
Complex & asymmetric nature of MADN
Attention only
Disregard only

62. Community level:
Global village and unique
blocks

63. Communities in N
A
found by
InfoMAP [21]

64. Global village trend is found
in media attention
83 countries are in one big community

65. Some small communities
MENA
Russia & Neighbors
Southern Asia
Southern Europe
North Africa
Southern Asia

66. Interestingly, Qatar is in a global
village not in a MENA cluster
Qatar
Yemen
UAESaudi Arabia
Egypt Bahrain
Kuwait
Sudan

67. Huntington’s civilizational
divides [22]
Western Orthodox
Islamic
African
Latin American
Hindu
Buddhist
Sinic Japanese

68. Some alignment between two divisions
but a global village [animated gif]

69. Communities in N
D
found by
InfoMAP [21]
• We found only one community that contains all the
countries, meaning that there is no group of countries
that disregard/are disregarded each other.

70. Network level:
Node2Vec [24]

71. t-SNE visualization of vector
representations of nodes in N
A
Algeria
Angola
Argentina
Armenia
Australia
Austria
Azerbaijan Bahrain
Bangladesh
Barbados
BelarusBelgium
Bolivia
Bosnia and Herzegovina
Brazil
Bulgaria
Cameroon
Canada
Chile
China
Colombia
Costa Rica
Cr atia
Cuba
Cyprus
Czech Republic
Denmark
Dominican Republic
Ecuador
Egypt
El Salvador
Estonia
Fiji
Finland
France
French Polynesia
Georgia
Germany
Ghana
Greece
Guatemala
Guinea
Guyana
Haiti
Honduras
Hong Kong
HungaryIceland
Ind
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Jamaica
Japan
Jordan
Kazakhstan
Kenya
Kuwa
Kyrgyzstan
Latvia
Lebanon
Libya
Liechtenstein
Li uania
Luxembourg
Macau
Malawi
Malaysia
Mali
Malta
Mauritania
Mexico
Moldova
Montenegro
MoroccoNepal
Netherlands
New Zealand
Nicaragua
Nigeria
Norway
Oman
Pakistan
Palestine
Panama
Paraguay
Peru
Philippines
Poland
Portugal
Puerto Rico
Qatar
Romania
Russia
Réun n
udi A abia
Senegal
Serbia
Singapore
Slov kia
Slovenia
South Africa
South Korea
Spain
Sri Lanka
Sudan
Sweden
Switzerland
Syria
Taiwan
Thailand
Trinidad and Tobago
Tunisia
Turkey
Uganda
Uk aine
United Arab Emirates
United Ki dom
ited States f merica
Uruguay
Uzbekistan
Venezuela
Vietn m
Yemen
Zambia
Zimbabwe

72. Global village trend is
reconfirmed
Algeria
Angola
Argentina
Armenia
Australia
Austria
Azerbaijan Bahrain
Bangladesh
Barbados
BelarusBelgium
Bolivia
Bosnia and Herzegovina
Brazil
Bulgaria
Cameroon
Canada
Chile
China
Colombia
Costa Rica
Cr atia
Cuba
Cyprus
Czech Republic
Denmark
Dominican Republic
Ecuador
Egypt
El Salvador
Estonia
Fiji
Finland
France
French Polynesia
Georgia
Germany
Ghana
Greece
Guatemala
Guinea
Guyana
Haiti
Honduras
Hong Kong
HungaryIceland
Ind
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Jamaica
Japan
Jordan
Kazakhstan
Kenya
Kuwa
Kyrgyzstan
Latvia
Lebanon
Libya
Liechtenstein
Li uania
Luxembourg
Macau
Malawi
Malaysia
Mali
Malta
Mauritania
Mexico
Moldova
Montenegro
MoroccoNepal
Netherlands
New Zealand
Nicaragua
Nigeria
Norway
Oman
Pakistan
Palestine
Panama
Paraguay
Peru
Philippines
Poland
Portugal
Puerto Rico
Qatar
Romania
Russia
Réun n
udi A abia
Senegal
Serbia
Singapore
Slov kia
Slovenia
South Africa
South Korea
Spain
Sri Lanka
Sudan
Sweden
Switzerland
Syria
Taiwan
Thailand
Trinidad and Tobago
Tunisia
Turkey
Uganda
Uk aine
United Arab Emirates
United Ki dom
ited States f merica
Uruguay
Uzbekistan
Venezuela
Vietn m
Yemen
Zambia
Zimbabwe
Mix of different regions

73. However, geographical proximity
also matters in media attention
Algeria
Angola
Argentina
Armenia
Australia
Austria
Azerbaijan Bahrain
Bangladesh
Barbados
BelarusBelgium
Bolivia
Bosnia and Herzegovina
Brazil
Bulgaria
Cameroon
Canada
Chile
China
Colombia
Costa Rica
Cr atia
Cuba
Cyprus
Czech Republic
Denmark
Dominican Republic
Ecuador
Egypt
El Salvador
Estonia
Fiji
Finland
France
French Polynesia
Georgia
Germany
Ghana
Greece
Guatemala
Guinea
Guyana
Haiti
Honduras
Hong Kong
HungaryIceland
Ind
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Jamaica
Japan
Jordan
Kazakhstan
Kenya
Kuwa
Kyrgyzstan
Latvia
Lebanon
Libya
Liechtenstein
Li uania
Luxembourg
Macau
Malawi
Malaysia
Mali
Malta
Mauritania
Mexico
Moldova
Montenegro
MoroccoNepal
Netherlands
New Zealand
Nicaragua
Nigeria
Norway
Oman
Pakistan
Palestine
Panama
Paraguay
Peru
Philippines
Poland
Portugal
Puerto Rico
Qatar
Romania
Russia
Réun n
udi A abia
Senegal
Serbia
Singapore
Slov kia
Slovenia
South Africa
South Korea
Spain
Sri Lanka
Sudan
Sweden
Switzerland
Syria
Taiwan
Thailand
Trinidad and Tobago
Tunisia
Turkey
Uganda
Uk aine
United Arab Emirates
United Ki dom
ited States f merica
Uruguay
Uzbekistan
Venezuela
Vietn m
Yemen
Zambia
Zimbabwe
Group of countries in the same region

74. t-SNE visualization of vector
representations of nodes in N
D
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bahrain
Bangladesh
Belarus
Be gium
Bolivia
Bosnia and Herzegovina
Brazil
Bulgaria
Cameroon
Canada
Chile
China
Colombia
Costa RicaCroatia
Cuba
Cyprus
Czech Republic
Denmark
Dominican Republic
Ecuador
Egypt
El Salvador
Estonia
Finland
France
Georgia
Germany
Ghana
Greece
Guatemala
Honduras
Hong Kong
Hungary
India
Indonesia
Iran
Iraq
Ireland
Isra
Italy
Jamaica
pan
Jordan
Kazakhstan
Kenya
Kuwait
Kyrgyzstan
Latvia
Lebanon
Libya
Liechtenstein
Lithuania
Luxembour
Macau
Malaysia
Malta
Mexico
Moldova
Montenegro
Morocco
Nepal
Netherlands
New Zealand
Nicaragua
Nigeria
Norw y
Oman
Pakistan
Palestine
Panama
Paraguay
Peru
Philippines
PolandPort a
Puerto Rico
Qatar
Romania
Russ a
Réunion
Sau Arabia
Senegal
Serbia
Singapo
Slovakia
Slovenia
South Africa
South Korea
Sp n
Sudan
SwedenSwitzerland
Syria
Taiwan
Thailand
Tunisia
Turkey
Ukraine
United Arab Emirate Un ed Ki dom
United States of America
Uruguay
Venezuela
Vie nam
Yemen
Zimbabwe

75. No correlation between media
disregard and geographical proximity
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bahrain
Bangladesh
Belarus
Be gium
Bolivia
Bosnia and Herzegovina
Brazil
Bulgaria
Cameroon
Canada
Chile
China
Colombia
Costa RicaCroatia
Cuba
Cyprus
Czech Republic
Denmark
Dominican Republic
Ecuador
Egypt
El Salvador
Estonia
Finland
France
Georgia
Germany
Ghana
Greece
Guatemala
Honduras
Hong Kong
Hungary
India
Indonesia
Iran
Iraq
Ireland
Isra
Italy
Jamaica
pan
Jordan
Kazakhstan
Kenya
Kuwait
Kyrgyzstan
Latvia
Lebanon
Libya
Liechtenstein
Lithuania
Luxembour
Macau
Malaysia
Malta
Mexico
Moldova
Montenegro
Morocco
Nepal
Netherlands
New Zealand
Nicaragua
Nigeria
Norw y
Oman
Pakistan
Palestine
Panama
Paraguay
Peru
Philippines
PolandPort a
Puerto Rico
Qatar
Romania
Russ a
Réunion
Sau Arabia
Senegal
Serbia
Singapo
Slovakia
Slovenia
South Africa
South Korea
Sp n
Sudan
SwedenSwitzerland
Syria
Taiwan
Thailand
Tunisia
Turkey
Ukraine
United Arab Emirate Un ed Ki dom
United States of America
Uruguay
Venezuela
Vie nam
Yemen
Zimbabwe
Mix of different regions

76. Summary
• We investigate the structural characteristics of the
multiplex media attention and disregard network
(MADN) among 129 countries.
• Through multi-level analysis from the node level to the
network level, we found the skewed, hierarchical, and
asymmetric structure of the MADN.
• Media attention and disregard have different structures.
• We observe the “global village” trend in media attention,
but, at the same time, unique attention blocks remain.

77. Future work
• “Media attention” is somewhat led by media, editors, and
journalists (professional organization).
• We would like to compare this with the worldview of
typical users collected from social media.
• Also, we plan to compare media attention network to
country networks based on other data sources (migration,
flight, trade).

78. @haewoon
http://haewoon.io
@JisunAn
http://jisun.me
https://arxiv.org/abs/1707.04941