This project analyses the relations on Twitter between politicians and journalists in the triangle of political communication in a hybrid media system (Chadwick, 2013).

1. 1
Automated Analysis of Journalists’ and
Politicians’ Online Behavior on Social Media
• Principal Investigator: Prof. dr. Marcel Broersma,
Director of the Centre for Media Studies and
Journalism, University of Groningen.
• Co-applicant: dr. Marc Esteve Del Valle, Assistant
Professor, Centre for Media Studies and Journalism,
University of Groningen.
• Data Scientists:
• Herbert Kruitbosch, Center for Information and
Technologies, University of Groningen.
• Erik Tjong-Kim-Sang, e-Science Center.

2. 2
1. Theoretical Background
The triangle of political communication in a hybrid media
system (Chadwick, 2013).
Politicians Citizens
Journalists
• Blue: Journalist
• Green: Politician

3. 3
1. Theoretical Background
Politicians’ use of Twitter mimic that of journalists (Broersma
and Graham, 2016):
Monitor social media
Network with reporters
Harvest stories of citizens
Publish information
Brand themselves

4. 4
1. Theoretical Background
The triangle of political communication in a hybrid
media system (example)
‘Woodstein’ @realDonaldTrump

5. 5
1. Theoretical Background
Limitations of the current research:
● Who are communicating with who: network analysis.
● And about what and to what extent: topic modelling.
But it remains largely unknown how they are doing this and
with what aim and effect.

6. 6
2. Research Questions
RQ1: Which discursive practices do politicians and journalists
use on Twitter and how do these change?
RQ2: To what extent do institutional differences between agents
still matter, or event exist, now they all have the power to
publish on social media?

7. 7
3. Twitter
11th most popular site in the
world (Alexa rank, June,
2017).
317 million users (Twitter,
2017).
500 million tweets a day
(Twitter, 2017).
Relatively easy access to its
data.

8. 8
4. Methods
Content Analysis (Coding Scheme)
Unit of analysis: tweet
Categories:
• 18 for the journalists
• 12 for the politicians
Examples of categories:
• Campaigning
• Critic
• News
• Personal
Develop algorithms
that allow for
automated content
analysis (Machine
Learning)

9. 9
4. Methods
28,045
26,282
55,992
31,796
5,972
35,000
0
10,000
20,000
30,000
40,000
50,000
60,000
Dutch political
candidates 2010
elections
British political
candidates 2010
elections
Dutch political
candidates 2012
elections
British candidate's
tweets 2015 elections
Dutch and Belgian
journalists 2 random
weeks
Dutch and British
journalists
Datasets Total= 183,087

10. 1
0
4. Methods
 TwitterCrawler

11. 1
1
4. Methods
 TwitterCrawler

12. 1
2
5. Preliminary results
Refining the algorithm:
Methods Accuracy level
Fastext Software 51.8%
Language models 0.25 million
tweets: 53.3%
21 million tweets:
55.5%
Dutch Wikipedia:
54.1%
Annotation of
1,000 difficult
tweets
51.5%

13. 1
3
5. Conclusions
Finalize the TwitterCrawler
Code new datasets
Increase the accuracy of the algorithm
Solve the annotation problem of difficult tweets:
• Context of the tweet
• Collapse categories of coding scheme

14. 1
4
Thank you!
Marc Esteve Del Valle, PhD
E-mail: m.esteve.del.valle@rug.nl
Twitter: @NetMev