Presentation at the ASCoR Spring Colloquium ‘Big Data at the University of Amsterdam’ February 18, 2014.

1. Connecting political data to media data
Laura Hollink
VU University Amsterdam
Web & Media group
ASCoR Spring Colloquium ‘Big Data at the University of Amsterdam’
February 18, 2014

2. Laura Hollink Damir Juric
Geert-Jan Houben
Martijn Kleppe
Max Kemman
Henri Beunders
Johan Oomen
Jaap Blom
Funded by Clarin-NL

5. Questions we want to answer
• Which events have attracted
a lot of media attention?
• What are the differences
between different media?
E.g. in different newspapers,
or newspapers vs. radio
bulletins?
• Has the coverage changed
over time?
• How are the events visualized
(photos, layout of newspaper,
etc.).

7. Transcriptions of all 9,294
meetings of the Dutch
parliament between
1945-1995, consisting of
1,208,903 speeches.

8. Transcriptions of all 9,294
meetings of the Dutch
parliament between
1945-1995, consisting of
1,208,903 speeches.
Archives of hundreds of
newspaper with tons of
newspaper issues or 10’s
of Millions of articles
between 1618-1995.
(We only use 1945-1995)

9. Transcriptions of all 9,294
meetings of the Dutch
parliament between
1945-1995, consisting of
1,208,903 speeches.
Roughly 1.8 Million news
bulletins between
1937-1984
(We only use 1945-1995)
Archives of hundreds of
newspaper with tons of
newspaper issues or 10’s
of Millions of articles
between 1618-1995.
(We only use 1945-1995)

10. PoliMedia methods

11. Step 1: Translate the Dutch parliamentary debates
to the standard structured web format RDF
nl.proc.sgd.d.
194519460000002
nl.proc.sgd.d.
194519460000002.1
PartOfDebateDebate
http://resolver.politicalmashup.nl/nl.proc.sgd.d.194519460000002
http://statengeneraaldigitaal.nl/
http://resolver.kb.nl/resolve?urn=sgd:mpeg21:19451946:0000002:pdf
nl.proc.sgd.d.19720000002
Handelingen Verenigde
Vergadering...
Dutch
1945-11-20
rdf:type
dc:id
dc:source
dc:source
dc:publisher
dc:language
dc:date
hasPart
rdf:type
nl.proc.sgd.d.
194519460000002.1.1
hasPart
DebateContext
rdf:type
nl.proc.sgd.d.
194519460000002.1.2
Speech
rdf:type
hasPart
nl.proc.sgd.d.
194519460000002.1.3
hasSubsequentSpeech
"Mijnheer de
Voorzitter, de
Commissie
van …"
hasSpokenText
sem:hasActor
Speaker_0006
4
Party_kvp
hasParty
hasSpeaker
member_of
_parliament
"De voorzitter
opent de
vergadering…"
hasText
http://resolver.kb.nl/resolve?urn=ddd:011198136:mpeg21:a0525:ocr
coveredIn
Party
KVP
Katholieke Volkspartij
rdf:type
hasAcronym
hasFullName
Joannes Antonius James
Bargefoaf:firstName
foaf:lastName
Barge
rdfs:label
http://resolver.politicalmashup.nl/nl.m.00064
dc:source
Politician
rdf:type
hasRole
nl.proc.sgd.d.
194519460000002.2
hasSubsequentPartOfDebate
XML by
War in
Parliament
Project

12. Modeling the debates as events
• An event has a date, a
location, actors, and
possibly sub-events.
• We build on the Simple
Event Model (SEM).
•links to the original sources
•reusing existing
vocabularies
nl.proc.sgd.d.
194519460000002
Debate
http://resolver.politicalmashup.nl/nl.proc.sgd.d.194519460000002
http://statengeneraaldigitaal.nl/
http://resolver.kb.nl/resolve?urn=sgd:mpeg21:19451946:0000002:pdf
nl.proc.sgd.d.19720000002
Handelingen Verenigde
Vergadering...
Dutch
1945-11-20
rdf:type
dc:id
dc:source
dc:source
dc:publisher
dc:language
dc:date
dc:title

13. •the part-of structure and
chronological order of the
debates.
nl.proc.sgd.d.
194519460000002
nl.proc.sgd.d.
194519460000002.1
PartOfDebate
hasPart
rdf:type
nl.proc.sgd.d.
194519460000002.1.1
hasPart
DebateContext
rdf:type
nl.proc.sgd.d.
194519460000002.1.2
Speech
rdf:type
hasPart
nl.proc.sgd.d.
194519460000002.1.3
hasSubsequentSpeech
"Mijnheer de
Voorzitter, de
Commissie
van …"
hasSpokenText
"De voorzitter
opent de
vergadering…"
hasText
nl.proc.sgd.d.
194519460000002.2
hasSubsequentPartOfDebate
Handelingen Verenigde
Vergadering...
dc:title

14. •the different roles and parties
that a speaker can have in his/
her career.
nl.proc.sgd.d.
194519460000002.1.2
Speech
rdf:type
"Mijnheer de
Voorzitter, de
Commissie
van …"
hasSpokenText
sem:hasActor
Speaker_0006
4
Party_kvp
hasParty
hasSpeaker
member_of
_parliament
http://resolver.kb.nl/resolve?urn=ddd:011198136:mpeg21:a0525:ocr
coveredIn
Party
KVP
Katholieke Volkspartij
rdf:type
hasAcronym
hasFullName
Joannes Antonius James
Bargefoaf:firstName
foaf:lastName
Barge
rdfs:label
Politician
rdf:type
hasRole

15. Step 2: Linking speeches in the debate to the
newspaper articles that cover them
We created a linking method to deal with our two challenges:
1.How to link documents that are so different in nature?
2. Can we use the structure of the debates: people, chronologic
order of speeches, introductions to each new topic, etc?
Detect
topics in
speeches
Create
queries
Search
newspaper
archive
Topics
Named
Entities
Name of
speaker
Detect
Named
Entities in
speeches
Candidate
articles
Queries
Rank
candidate
articles
Links
between
speeches
and articles
Debates
Date of
debate

16. Step 2: Linking speeches in the debate to the
newspaper articles that cover them
Detect
topics in
speeches
Create
queries
Search
newspaper
archive
Topics
Named
Entities
Name of
speaker
Detect
Named
Entities in
speeches
Candidate
articles
Queries
Rank
candidate
articles
Links
between
speeches
and articles
Debates
Date of
debate
Intuition 1: The name of the speaker should
appear in the article and the article should
be published within a week of the debate

17. Step 2: Linking speeches in the debate to the
newspaper articles that cover them
Detect
topics in
speeches
Create
queries
Search
newspaper
archive
Topics
Named
Entities
Name of
speaker
Detect
Named
Entities in
speeches
Candidate
articles
Queries
Rank
candidate
articles
Links
between
speeches
and articles
Debates
Date of
debate
Intuition 1: The name of the speaker should
appear in the article and the article should
be published within a week of the debate
Intuition 2: the more the article and the
speech overlap in terms of topics and
named entities, the more they are related.

18. Evaluation: what do we use to rank the candidate
articles?
• Experiment on 150 <newspaper article, speech in debate> pairs, 2 raters, K
= 0.5
• Compare text of candidate articles to:
• Setting 1: Named Entities in speech
• Setting 2: Named Entities + Topics in speech
• Setting 3: Named Entities + Topics in speech and larger part-of-debate
Score Setting 1 Setting 2 Setting 3
I don’t know 0.14 0.15 0.08
0 - unrelated 0.38 0.23 0.12
1- related 0.29 0.36 0.36
2- explicit mention of the debate 0.19 0.26 0.44
1+2 0.48 0.62 0.80

19. Results
•An open data set of Dutch parliamentary debates,
•with almost 3 Million links between 450.000 speeches and URL’s of 1.5
Million news paper articles and radio bulletins at the National Library.
•accessible though a Web demonstrator and through a SPARQL endpoint.

20. Demo

27. SPARQL endpoint
• A service to query a knowledge
base using the SPARQL query
language.
“All speeches with more
than 60 associated news
items.”
SELECT ?speech ?no_newsitems {{
SELECT ?speech (COUNT(?news) AS ?no_news_items)
WHERE{
?speech <http://purl.org/linkedpolitics/nl/polivoc#coveredAt> ?news .
}
GROUP BY ?speech }
FILTER (?no_news_items > 60) }

32. Reflection: to what extend can we answer these
questions?
• Which events have attracted
a lot of media attention?
• What are the differences
between different media?
E.g. in different newspapers,
or newspapers vs. radio
bulletins?
• Has the coverage changed
over time?
• How are the events visualized
(photos, layout of newspaper,
etc.).

33. Future work
• More types of links
• From just “coveredIn” to “quotedIn”, “coveredIn”, “backgroundOf”
“talksAbout”
• More types of media
• More types of (political) events.

34. Project ‘Talk of Europe / Traveling Clarin Campus’
2014-2015
Funded by CLARIN-ERIC
From left to right: Max Kemman, Marnix van Berchum, Laura Hollink, Astrid van Aggelen, Steven Krauwer,
Henri Beunders. (Unfortunately, Martijn Kleppe and Johan Oomen were not present to join the group pic.)

35. Plans of ‘ToE/TTC’
1.Publish proceedings of the EU parliamentary debates in RDF
• hosted by DANS
2.Organize 3 workshops/hackathons/‘Traveling Clarin Campuses’ in which we
invite international partners to work with the data.
3.In collaboration with international partners:
• enrich with annotations, e.g. topics, structured data about people, parties,
etc.
• link to national datasets, e.g. media or national parliaments