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Presentation at ICCSA14, Le Havre, June 24, 2014
Citation preview
Dynamics of Media Attention
V.A. Traag, R. Reinanda, J. Hicks, G. Van Klinken
KITLV, Leiden, the Netherlandse-Humanities, KNAW, Amsterdam, the Netherlands
June 24, 2014
eRoyal Netherlands Academy of Arts and SciencesHumanities
Background
Research focus
• Study elite (network) behaviour.
• Particular focus on regime changes.I Case I: Indonesia, fall of Suharto 1998.I Case II: Indonesian independence after WWII.
• 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 date).I “ Budhisantoso would ask Kalla to team up with Yudhoyono .”
K
B Y
19-04-2004
19-04-2004
19-04-2004
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 date).I “ Budhisantoso would ask Kalla to team up with Yudhoyono .”
K
B Y
19-04-2004
19-04-2004
19-04-2004
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 date).I “ Budhisantoso would ask Kalla to team up with Yudhoyono .”
K
B Y
19-04-2004
19-04-2004
19-04-2004
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 date).I “ Budhisantoso would ask Kalla to team up with Yudhoyono .”
K
B Y
19-04-2004
19-04-2004
19-04-2004
Media dynamics
Dynamical characteristics
• How does attention grow, decay?
• How long do people stay in the news?
• How often do new people enter the news?
• Are there bursty patterns of attention?
Comparison
• Look at particular events.
• Are dynamics any different?
• Does attention grow or decay more quickly?
• Do people drop from the news sooner than expected?
Media dynamics
Dynamical characteristics
• How does attention grow, decay?
• How long do people stay in the news?
• How often do new people enter the news?
• Are there bursty patterns of attention?
Comparison
• Look at particular events.
• Are dynamics any different?
• Does attention grow or decay more quickly?
• Do people drop from the news sooner than expected?
Weekly cycles
2004 2005 2006 2007 2008 2009 2010 2011 2012Date
050
100150200250300
Frequency
0 5 10 15 20 25 30 35 40Lag (days)
0.0
0.2
0.4
0.6
0.8
1.0
Auto
corr
ela
tion
0.0 0.1 0.2 0.3 0.4 0.5Frequency (days−1 )
0
10000
20000
30000
40000
50000
Magnit
ude
Neighbours’ timeseries correlate: underlying commonality.
Growth/decay attention
-104 -103 -102 -101 0 101 102 103 104
Time relative to peak (days)
10-5
10-4
10-3
10-2
10-1A
ttenti
on
α−βlog|t|
α|t|−β
αe−β|t|
αe−β|t| |t|−γ
Best fit e−βtt−γ .
Inter-event time
0 500 1000 1500 2000 2500 3000Inter-event time (Days)
10-7
10-6
10-5
10-4
10-3
10-2
10-1
100Pro
babili
tyEdgesNodes
Powerlaw with exponential cut-off x−βe−γx .
Lifetime
0 500 1000 1500 2000 2500 3000Lifetime (days)
10-3
10-2
10-1
100Pro
babili
tyEdgesNodes
Very long lifetime for nodes.
New nodes
1500 1000 500 0 500 1000 1500 2000 2500 3000First Time Delay (days)
0.00
0.01
0.02
0.03
0.04
0.05
0.06Pro
babili
tyEdgesNodes
New nodes (and edges) continuously appear in the media.
Conclusion
Timescales
Measure Short time Long time
Peak growth/decay power law PoissonInter-event power law PoissonLifetime fast (very) slowNew nodes/edges appear continuously
Theory
• Issues arise at a constant rate (Poisson process)
• Within issues, faster dynamics, shorter lifetimes
Conclusion
Questions?