Structure of media attention
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Transcript of Structure of media attention
The structure of media attention
V.A. Traag, R. Reinanda, J. Hicks, G. Van Klinken
KITLV, Leiden, the Netherlandse-Humanities, KNAW, Amsterdam, the Netherlands
September 30, 2014
eRoyal Netherlands Academy of Arts and SciencesHumanities
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.
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
B Y
1
1
1
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
B Y
1
1
1
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
B Y
1
1
1
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
B Y
1
1
1
Strength
100 101 102 103
100
101
Degree
Average
weigh
t
Joyo
100 101 102 103 104
Degree
NYT
Data
Hubs co-occur more frequently.
Strength
100 101 102 103
100
101
Degree
Average
weigh
t
Joyo
100 101 102 103 104
Degree
NYT
Data Bipartite
Hubs co-occur more frequently.
Clustering
100 101 102 10310−3
10−2
10−1
100
Degree
Clustering
Joyo
100 101 102 103 104
Degree
NYT
Data
Hubs tend to cluster less.
Clustering
100 101 102 10310−3
10−2
10−1
100
Degree
Clustering
Joyo
100 101 102 103 104
Degree
NYT
Data Bipartite
Hubs tend to cluster less.
Clustering
100 101 102 103
10−1
100
Degree
Weigh
tedClustering
Joyo
100 101 102 103 104
Degree
NYT
Data
Hubs tend to cluster less (also weighted).
Clustering
100 101 102 103
10−1
100
Degree
Weigh
tedClustering
Joyo
100 101 102 103 104
Degree
NYT
Data Bipartite
Hubs tend to cluster less (also weighted).
Neighbour degree
100 101 102 103101
102
103
Degree
Neigh
bou
rDegree
Joyo
100 101 102 103 104
Degree
NYT
Data
Hubs tend to connect to low degree nodes.
Neighbour degree
100 101 102 103101
102
103
Degree
Neigh
bou
rDegree
Joyo
100 101 102 103 104
Degree
NYT
Data Bipartite
Hubs tend to connect to low degree nodes.
Weighted Neighbour degree
100 101 102 103
102
103
Degree
Weigh
tedNeigh
bou
rDegree
Joyo
100 101 102 103 104
Degree
NYT
Data
But hubs connect much stronger to other hubs.
Weighted Neighbour degree
100 101 102 103
102
103
Degree
Weigh
tedNeigh
bou
rDegree
Joyo
100 101 102 103 104
Degree
NYT
Data Bipartite
But hubs connect much stronger to other hubs.
Predict weight
100 101 102 103 104
100
101
102
103
104
Weight
PredictedWeigh
t
Joyo
100 101 102 103 104
Weight
NYT
Data
wij ∼ Jγij exp(α(si sj)β)
Predict weight
100 101 102 103 104
100
101
102
103
104
Weight
PredictedWeigh
t
Joyo
100 101 102 103 104
Weight
NYT
Data Bipartite
wij ∼ Jγij exp(α(si sj)β)
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 presentin bipartite randomisation. Largest deviations, empirically:
• Degree is lower, average weight is higher.
• Weighted neighbour degree increases.
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)−β .
3 Repeat
Degree & Weight
Empirical Bipartite Model
JoyoAvg. Degree 12.4 22.1 12.2Avg. Weight 2.9 1.2 2.8
NYTAvg. Degree 22.3 45.2 22.6Avg. Weight 2.01 1.11 1.31
Strength
100 101 102 103
100
101
Degree
Average
weigh
t
Joyo
100 101 102 103 104
Degree
NYT
Data Model
Weight increases more in the model.
Weighted neigbhour degree
100 101 102 103
102
103
Degree
Weigh
tedNeigh
bou
rDegree
Joyo
100 101 102 103 104
Degree
NYT
Data Bipartite
Weighted neighbour degree increases in the model.
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.
Thank you! Questions?
Presentation: SlideSharePaper: arXiv:1409.1744
Dynamics of network: arXiv:1409.2973
http://www.traag.net • @vtraag