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Structure of media attention
1. The structure of media attention
V.A. Traag, R. Reinanda, J. Hicks, G. Van Klinken
KITLV, Leiden, the Netherlands
e-Humanities, KNAW, Amsterdam, the Netherlands
September 30, 2014
e
Humanities
Royal Netherlands Academy of Arts and Sciences
2. Background
Research focus
Study elite (network) behaviour.
Relation with political developments.
Data: newspaper articles. How can we use them?
Data
Current corpus: Joyo/Indonesian News Service, 2004{2012.
Contains about 140 263 articles.
3. Network
Building the network
1 Detect names automatically .
I Budhisantoso would ask Kalla to team up with Yudhoyono .
2 Disambiguate names.
I Susilo Bambang Yudhoyono or Dr. Yudhoyono , etc. . .
3 Co-occurrence in sentence (record frequency).
I Budhisantoso would ask Kalla to team up with Yudhoyono .
K
1
B Y
1
1
4. Network
Building the network
1 Detect names automatically .
I Budhisantoso would ask Kalla to team up with Yudhoyono .
2 Disambiguate names.
I Susilo Bambang Yudhoyono or Dr. Yudhoyono , etc. . .
3 Co-occurrence in sentence (record frequency).
I Budhisantoso would ask Kalla to team up with Yudhoyono .
K
1
B Y
1
1
5. Network
Building the network
1 Detect names automatically .
I Budhisantoso would ask Kalla to team up with Yudhoyono .
2 Disambiguate names.
I Susilo Bambang Yudhoyono or Dr. Yudhoyono , etc. . .
3 Co-occurrence in sentence (record frequency).
I Budhisantoso would ask Kalla to team up with Yudhoyono .
K
1
B Y
1
1
6. Network
Building the network
1 Detect names automatically .
I Budhisantoso would ask Kalla to team up with Yudhoyono .
2 Disambiguate names.
I Susilo Bambang Yudhoyono or Dr. Yudhoyono , etc. . .
3 Co-occurrence in sentence (record frequency).
I Budhisantoso would ask Kalla to team up with Yudhoyono .
K
1
B Y
1
1
7. Strength
101
100
100 101 102 103
Degree
Average weight
Joyo
NYT
100 101 102 103 104
Degree
Data
Hubs co-occur more frequently.
8. Strength
101
100
100 101 102 103
Degree
Average weight
Joyo
NYT
100 101 102 103 104
Degree
Data Bipartite
Hubs co-occur more frequently.
9. Clustering
100
10−1
10−2
100 101 102 103 10−3
Degree
Clustering
Joyo
NYT
100 101 102 103 104
Degree
Data
Hubs tend to cluster less.
10. Clustering
100
10−1
10−2
100 101 102 103 10−3
Degree
Clustering
Joyo
NYT
100 101 102 103 104
Degree
Data Bipartite
Hubs tend to cluster less.
11. Clustering
100
10−1
100 101 102 103
Degree
Weighted Clustering
Joyo
NYT
100 101 102 103 104
Degree
Data
Hubs tend to cluster less (also weighted).
12. Clustering
100
10−1
100 101 102 103
Degree
Weighted Clustering
Joyo
NYT
100 101 102 103 104
Degree
Data Bipartite
Hubs tend to cluster less (also weighted).
13. Neighbour degree
103
102
100 101 102 103 101
Degree
Neighbour Degree
Joyo
NYT
100 101 102 103 104
Degree
Data
Hubs tend to connect to low degree nodes.
14. Neighbour degree
103
102
100 101 102 103 101
Degree
Neighbour Degree
Joyo
NYT
100 101 102 103 104
Degree
Data Bipartite
Hubs tend to connect to low degree nodes.
15. Weighted Neighbour degree
103
102
100 101 102 103
Degree
Weighted Neighbour Degree
Joyo
NYT
100 101 102 103 104
Degree
Data
But hubs connect much stronger to other hubs.
16. Weighted Neighbour degree
103
102
100 101 102 103
Degree
Weighted Neighbour Degree
Joyo
NYT
100 101 102 103 104
Degree
Data Bipartite
But hubs connect much stronger to other hubs.
21. Core-periphery
Summary Results
Hubs attract much more weight.
Most of the weight between hubs.
Low degree node connect to hubs.
Low degree nodes cluster locally.
Consistent with core-periphery structure. But, seems also present
in bipartite randomisation. Largest deviations, empirically:
Degree is lower, average weight is higher.
Weighted neighbour degree increases.
22. Model
Simple model to overcome deviations:
1 Create empty sentence
2 Add certain number of nodes
1 Either random node (with PA)
2 Or random neighbour (with PA)
Probability (ki + 1)
25. Strength
101
100
100 101 102 103
Degree
Average weight
Joyo
NYT
100 101 102 103 104
Degree
Data Model
Weight increases more in the model.
26. Weighted neigbhour degree
103
102
100 101 102 103
Degree
Weighted Neighbour Degree
Joyo
NYT
100 101 102 103 104
Degree
Data Bipartite
Weighted neighbour degree increases in the model.
27. Conclusions
Results:
Network looks like core-periphery.
Probably due to bipartite structure.
But also to repetitive interaction.
Further research:
Basis for comparing elite networks.
Compare networks across time and space.
Dynamical, temporal aspects.
