Post on 28-Jan-2018
Multiplex Media Attention and Disregard Network among 129 Countries
Haewoon Kwak Jisun An
Qatar Computing Research InstituteHamad 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
Outline• Paper title 101
• Motivation
• Data collection - Unfiltered.news
• Multiplex network analyses ① ② ③ ④ ⑤
• Summary & future work
Multiplex Media Attention and Disregard Network among 129 Countries
Multiplex Media Attention and Disregard Network among 129 Countries
Multiplex network?
Media attention?
Media disregard?
Multiplex Media Attention and Disregard Network among 129 Countries
3 Multiplex network?
1 Media attention? 2 Media disregard?
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
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
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.
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.
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
Multiplex Media Attention and Disregard Network among 129 Countries
Why is this interesting?
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/
https://www.ted.com/talks/alisa_miller_shares_the_news_about_the_news
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.
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)
Algeria
Senegal
SloveniaTunisiaLuxembourg
Bulgaria
Italy
Greece
Bahrain
Libya
Morocco
Palestine
Lebanon
Mauritania
Ukraine
Bosnia and Herzegovina
GeorgiaArmenia
Croatia
Poland
Estonia
Serbia
Latvia
Slovakia
Russia
Netherlands
Belarus
Moldova
Uzbekistan
Azerbaijan
Montenegro
FranceIran
Cyprus
Belgium
TurkeyRéunion
Paraguay
Dominican Republic
Brazil
Guyana
Portugal
Cuba
Uruguay
Jamaica
Bolivia
Barbados
Costa RicaVenezuela
El Salvador
Mexico
Trinidad and Tobago
HondurasPhilippines
Nicaragua
Peru
IndiaCanada
Spain
Guatemala
Pakistan
Czech Republic
Zimbabwe
Bangladesh
Uganda
Kazakhstan
Kyrgyzstan
Sri Lanka
Lithuania
Kenya
Thailand
Vietnam
JapanFiji
MacauHong Kong
Puerto Rico
China
New ZealandAngola
Haiti
Argentina
Hungary
United States of America
Colombia
Sweden
Liechtenstein
Nigeria
Denmark
United Kingdom
OmanQatar
Kuwait
Sudan
United Arab Emirates
Saudi Arabia
Egypt
Malta
French Polynesia
Romania
Mali
GermanyAustria
South Africa
SyriaFinland
Switzerland
Norway
Cameroon
Israel
Yemen
Iraq Jordan
AustraliaEcuador
Panama
IrelandTaiwan
MalaysiaSingapore Chile
Nepal
Limitations in previous work• Pairwise modeling is not enough to model complex nature
of media attention among multiple countries.
Our approach• We build a multiplex media attention and disregard
network (MADN) among countries and analyze its structural characteristics.
Algeria
Senegal
SloveniaTunisiaLuxembourg
Bulgaria
Italy
Greece
Bahrain
Libya
Morocco
Palestine
Lebanon
Mauritania
Ukraine
Bosnia and Herzegovina
GeorgiaArmenia
Croatia
Poland
Estonia
Serbia
Latvia
Slovakia
Russia
Netherlands
Belarus
Moldova
Uzbekistan
Azerbaijan
Montenegro
FranceIran
Cyprus
Belgium
TurkeyRéunion
Paraguay
Dominican Republic
Brazil
Guyana
Portugal
Cuba
Uruguay
Jamaica
Bolivia
Barbados
Costa RicaVenezuela
El Salvador
Mexico
Trinidad and Tobago
HondurasPhilippines
Nicaragua
Peru
IndiaCanada
Spain
Guatemala
Pakistan
Czech Republic
Zimbabwe
Bangladesh
Uganda
Kazakhstan
Kyrgyzstan
Sri Lanka
Lithuania
Kenya
Thailand
Vietnam
JapanFiji
MacauHong Kong
Puerto Rico
China
New ZealandAngola
Haiti
Argentina
Hungary
United States of America
Colombia
Sweden
Liechtenstein
Nigeria
Denmark
United Kingdom
OmanQatar
Kuwait
Sudan
United Arab Emirates
Saudi Arabia
Egypt
Malta
French Polynesia
Romania
Mali
GermanyAustria
South Africa
SyriaFinland
Switzerland
Norway
Cameroon
Israel
Yemen
Iraq Jordan
AustraliaEcuador
Panama
IrelandTaiwan
MalaysiaSingapore Chile
Nepal
Macau
Honduras
IrelandParaguay
Chile
Sudan
Uzbekistan
Israel
Zimbabwe
Iran
Belgium
Romania
Nicaragua
France
Croatia
Denmark
Argentina
Costa RicaPeru
Finland
SlovakiaRéunion
IcelandNew ZealandSlovenia
PortugalBarbadosEcuador
Moldova
Belarus
Czech RepublicLebanon
BoliviaJordan
Bosnia and Herzegovina
Switzerland
Libya
El Salvador
Senegal
Austria
Hungary
Bulgaria
Taiwan
LithuaniaGuineaCanada
Latvia
Saudi ArabiaSyria
Australia
KazakhstanYemen
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 LankaPuerto Rico
Nigeria
Philippines
Russia
TurkeyJamaica
PakistanDominican Republic
Mali
Nepal
French Polynesia
GuyanaAngola
Mauritania
Italy
SpainUnited States of America
Estonia
VenezuelaNorway
Vietnam
Fiji Sweden
Kenya
Georgia
Cameroon
Kyrgyzstan
CubaGuatemala
PolandBahrain
GermanyQatar TunisiaCyprus
Thailand Mexico
South Korea
Panama
Indonesia
Colombia
Attention Disregard
Datasets we need• Collect news from many countries in the world
• Handle English and non-English contents
• Do not filter specific types of news
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
Why Unfiltered.news?• Retrieve data from Google News
• Covers 100+ countries
• Translate contents in major languages
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.
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 100k
Num
ber o
f Cou
ntrie
s
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.
Build MADN
NA: Attention network
ND: Disregard network
+
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
Basic topological characteristics of MADN
Examining structures of MADN
• Node level
• Dyadic level
• Triadic level
• Community level
• Network levelBottom-up manner
Node level: Country centrality
Country centrality
Attention network Disregard network
Country centrality
Small countries tend to report foreign news more
Country centrality
The USA is placed in the brightest spotlight on the stage of the news world [3]
Country centrality
Some countries appear in both
Country centrality
A country has different news values for different countries
Dyadic level: Media attention bias
Media attention asymmetry
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.
Density plot of wA and wD
0
50
100
150
200
0 50 100 150 200Link weight in NA
Link
wei
ght i
n N
D
1.0000007.38905654.598150403.4287932980.957987
Most of links have high weights in only one network
0
50
100
150
200
0 50 100 150 200Link weight in NA
Link
wei
ght i
n N
D
1.0000007.38905654.598150403.4287932980.957987
News value of one country to another country is stable to some extent
0
50
100
150
200
0 50 100 150 200Link weight in NA
Link
wei
ght i
n N
D
1.0000007.38905654.598150403.4287932980.957987
Media attention is less flexible and might have some country bias.
Characterizing a link by adding wA and -wD
Country A Country B
Country A Country B
wA
wD
w = wA - wD
w > 0: pay attentionw = 0: no significant pattern w < 0: disregard
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
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%
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
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.
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
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
Considering link weights can reveal rich dynamics
Country A Country B
1. exchanging attention
Country A Country B
Country A Country B
or
or
…
Algeria
Senegal
SloveniaTunisiaLuxembourg
Bulgaria
Italy
Greece
Bahrain
Libya
Morocco
Palestine
Lebanon
Mauritania
Ukraine
Bosnia and Herzegovina
GeorgiaArmenia
Croatia
Poland
Estonia
Serbia
Latvia
Slovakia
Russia
Netherlands
Belarus
Moldova
Uzbekistan
Azerbaijan
Montenegro
FranceIran
Cyprus
Belgium
TurkeyRéunion
Paraguay
Dominican Republic
Brazil
Guyana
Portugal
Cuba
Uruguay
Jamaica
Bolivia
Barbados
Costa RicaVenezuela
El Salvador
Mexico
Trinidad and Tobago
HondurasPhilippines
Nicaragua
Peru
IndiaCanada
Spain
Guatemala
Pakistan
Czech Republic
Zimbabwe
Bangladesh
Uganda
Kazakhstan
Kyrgyzstan
Sri Lanka
Lithuania
Kenya
Thailand
Vietnam
JapanFiji
MacauHong Kong
Puerto Rico
China
New ZealandAngola
Haiti
Argentina
Hungary
United States of America
Colombia
Sweden
Liechtenstein
Nigeria
Denmark
United Kingdom
OmanQatar
Kuwait
Sudan
United Arab Emirates
Saudi Arabia
Egypt
Malta
French Polynesia
Romania
Mali
GermanyAustria
South Africa
SyriaFinland
Switzerland
Norway
Cameroon
Israel
Yemen
Iraq Jordan
AustraliaEcuador
Panama
IrelandTaiwan
MalaysiaSingapore Chile
Nepal
Backbone extraction [16]• Applying disparity filter proposed by [16] and extracting
significant links (called a “backbone”)
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?
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?
Findings by relationship characterization in NA
• 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.
Triadic level: Motif analysis
Network motif
• (Usually 3- or 4-) sized subgraphs that repeat in a given network
Proportions of each motif are different according to the types of networks
Superfamilies of Evolved and Designed NetworksRon Milo, Shalev Itzkovitz, Nadav Kashtan, Reuven Levitt, Shai Shen-Orr, Inbal Ayzenshtat,Michal Sheffer, Uri Alon Science, 303(5663), 2004
Well-known names of each motif
Feed forward loop (FFL)Fan-out Cascade
Fan-in
Fully connected triad
Double feedback loop
Motif profiles of NA and ND
Motif profiles of NA and ND
Two networks are structurally very different
Motif profiles of NA
Transitive hierarchy is found. (If A->B and B->C, then A->C)
Feed forward loop (FFL)
Motif profiles of ND
Led by star-shaped subnetworks
Fan-out Fan-in
Motif profiles of ND
Transitivity does not hold in ND
Cascade
Significant colored motifs in MADN
AttentionDisregard
Complex & asymmetric nature of MADN
Attention only
Disregard only
Community level: Global village and unique
blocks
Communities in NA found by InfoMAP [21]
Global village trend is found in media attention
83 countries are in one big community
Some small communities
MENA
Russia & Neighbors
Southern Asia
Southern EuropeNorth Africa
Southern Asia
Interestingly, Qatar is in a global village not in a MENA cluster
Qatar
Yemen
UAESaudi ArabiaEgypt Bahrain
Kuwait
Sudan
Huntington’s civilizational divides [22]
Western Orthodox
Islamic
African
Latin American Hindu
BuddhistSinic Japanese
Some alignment between two divisions but a global village [animated gif]
Communities in ND 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.
Network level: Node2Vec [24]
t-SNE visualization of vector representations of nodes in NA
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
HungaryIcelandInd
Indonesia
IranIraq
Ireland
Israel
Italy
Jamaica
JapanJordan
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 LankaSudan
Sweden
Switzerland
Syria
TaiwanThailand
Trinidad and Tobago
Tunisia
Turkey
Uganda
Uk aine
United Arab Emirates
United Ki domited States f merica
Uruguay
Uzbekistan
Venezuela
Vietn mYemen
Zambia
Zimbabwe
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
HungaryIcelandInd
Indonesia
IranIraq
Ireland
Israel
Italy
Jamaica
JapanJordan
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 LankaSudan
Sweden
Switzerland
Syria
TaiwanThailand
Trinidad and Tobago
Tunisia
Turkey
Uganda
Uk aine
United Arab Emirates
United Ki domited States f merica
Uruguay
Uzbekistan
Venezuela
Vietn mYemen
Zambia
Zimbabwe
Mix of different regions
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
HungaryIcelandInd
Indonesia
IranIraq
Ireland
Israel
Italy
Jamaica
JapanJordan
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 LankaSudan
Sweden
Switzerland
Syria
TaiwanThailand
Trinidad and Tobago
Tunisia
Turkey
Uganda
Uk aine
United Arab Emirates
United Ki domited States f merica
Uruguay
Uzbekistan
Venezuela
Vietn mYemen
Zambia
Zimbabwe
Group of countries in the same region
t-SNE visualization of vector representations of nodes in ND
AlgeriaArgentina
Armenia
Australia Austria
Azerbaijan
Bahrain
Bangladesh
BelarusBe giumBolivia
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
FranceGeorgia
Germany
Ghana
Greece
Guatemala
Honduras
Hong Kong
Hungary
India
Indonesia
Iran
Iraq
Ireland
Isra
Italy
Jamaica
pan
Jordan
Kazakhstan
KenyaKuwait
Kyrgyzstan
Latvia
Lebanon
Libya
Liechtenstein
Lithuania
LuxembourMacau
Malaysia
Malta
Mexico
Moldova
Montenegro
MoroccoNepal
Netherlands
New Zealand
Nicaragua
Nigeria
Norw y
Oman
Pakistan
Palestine
Panama
Paraguay
Peru
Philippines
PolandPort a
Puerto Rico
Qatar
Romania
Russ a
Réunion
Sau ArabiaSenegal
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
VenezuelaVie nam
Yemen
Zimbabwe
No correlation between media disregard and geographical proximity
AlgeriaArgentina
Armenia
Australia Austria
Azerbaijan
Bahrain
Bangladesh
BelarusBe giumBolivia
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
FranceGeorgia
Germany
Ghana
Greece
Guatemala
Honduras
Hong Kong
Hungary
India
Indonesia
Iran
Iraq
Ireland
Isra
Italy
Jamaica
pan
Jordan
Kazakhstan
KenyaKuwait
Kyrgyzstan
Latvia
Lebanon
Libya
Liechtenstein
Lithuania
LuxembourMacau
Malaysia
Malta
Mexico
Moldova
Montenegro
MoroccoNepal
Netherlands
New Zealand
Nicaragua
Nigeria
Norw y
Oman
Pakistan
Palestine
Panama
Paraguay
Peru
Philippines
PolandPort a
Puerto Rico
Qatar
Romania
Russ a
Réunion
Sau ArabiaSenegal
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
VenezuelaVie nam
Yemen
Zimbabwe
Mix of different regions
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.
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).
@haewoon http://haewoon.io
@JisunAn http://jisun.me
https://arxiv.org/abs/1707.04941