This project analyses the relations on Twitter between politicians and journalists in the triangle of political communication in a hybrid media system (Chadwick, 2013).
AI Virtual Influencers: The Future of Influencer Marketing
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Automated Analysis of Journalists' and Politicians' Online Behavior on Social Media
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Automated Analysis of Journalistsâ and
Politiciansâ 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.
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1. Theoretical Background
ďThe triangle of political communication in a hybrid media
system (Chadwick, 2013).
Politicians Citizens
Journalists
⢠Blue: Journalist
⢠Green: Politician
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1. Theoretical Background
ďPoliticiansâ use of Twitter mimic that of journalists (Broersma
and Graham, 2016):
ďśMonitor social media
ďśNetwork with reporters
ďśHarvest stories of citizens
ďśPublish information
ďśBrand themselves
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1. Theoretical Background
ďThe triangle of political communication in a hybrid
media system (example)
âWoodsteinâ @realDonaldTrump
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1. Theoretical Background
ďLimitations of the current research:
â Who are communicating with who: network analysis.
â And about what and to what extent: topic modelling.
ďBut it remains largely unknown how they are doing this and
with what aim and effect.
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2. Research Questions
ďRQ1: Which discursive practices do politicians and journalists
use on Twitter and how do these change?
ďRQ2: To what extent do institutional differences between agents
still matter, or event exist, now they all have the power to
publish on social media?
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3. Twitter
ď11th most popular site in the
world (Alexa rank, June,
2017).
ď317 million users (Twitter,
2017).
ď500 million tweets a day
(Twitter, 2017).
ďRelatively easy access to its
data.
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4. Methods
ďContent Analysis (Coding Scheme)
ďśUnit of analysis: tweet
ďśCategories:
⢠18 for the journalists
⢠12 for the politicians
ďźExamples of categories:
⢠Campaigning
⢠Critic
⢠News
⢠Personal
Develop algorithms
that allow for
automated content
analysis (Machine
Learning)
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5. Preliminary results
ďRefining 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%
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5. Conclusions
ďFinalize the TwitterCrawler
ďCode new datasets
ďIncrease the accuracy of the algorithm
ďSolve the annotation problem of difficult tweets:
⢠Context of the tweet
⢠Collapse categories of coding scheme