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Pilot to Examine the Potential of Twitter and Facebook in the German Bundestag Elections 2013 (PEP-TF): First Results
Pilot to Examine the Potential of Twitter and Facebook in the German Bundestag Elections 2013 (PEP-TF): First Results
Pilot to Examine the Potential of Twitter and Facebook in the German Bundestag Elections 2013 (PEP-TF): First Results
Pilot to Examine the Potential of Twitter and Facebook in the German Bundestag Elections 2013 (PEP-TF): First Results
Pilot to Examine the Potential of Twitter and Facebook in the German Bundestag Elections 2013 (PEP-TF): First Results
Pilot to Examine the Potential of Twitter and Facebook in the German Bundestag Elections 2013 (PEP-TF): First Results
Pilot to Examine the Potential of Twitter and Facebook in the German Bundestag Elections 2013 (PEP-TF): First Results
Pilot to Examine the Potential of Twitter and Facebook in the German Bundestag Elections 2013 (PEP-TF): First Results
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Pilot to Examine the Potential of Twitter and Facebook in the German Bundestag Elections 2013 (PEP-TF): First Results

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  • 1. CSS Workshop, GESIS, Köln Pilot to Examine the Potential of Twitter and Facebook in the German Bundestag Elections 2013 (PEP-TF): First Results Haiko Lietz, Claudia Wagner, Arnim Bleier & Markus Strohmaier 16 December 2013
  • 2. Subject • Role of Twitter in political conversation • How do politicians use Twitter? – As a conversational tool to drive and set political topics online or – As a way to react to offline events. • We propose a framework to study conversational dynamics 2
  • 3. Data • 1,031 candidates for the Bundestag identifiable on Twitter • June to November 2013 • 8.7 million tweets • Details: Kaczmirek et al., 2013, http://arxiv.org/abs/1312.4476 3
  • 4. Data → Expect differences in practice 4
  • 5. Metrics Tagging Retweeting Mentioning Similarity of parties Jaccard overlap [0, 1] of used hashtags e ti‘ed politicians retweeted politicians Focus of party Normalized Entropy [0, 1] of used hashtags e t‘ed politicians retweeted politicians Stability of party Rank Biased Overlap [0, 1] of used hashtags e t‘ed politicians retweeted politicians 5
  • 6. 1 3 Tagging 5 7 9 11 13 1 3 Retweeting 5 7 9 11 13 1 3 Mentioning 5 7 9 11 13 0.12 0.1 0.1 0.08 0.08 0.06 0.06 0.04 0.04 0.02 Similarity 0.12 0.02 0 1 0 1 Entropy 0.9 0.9 0.8 0.8 0.7 TV Election Debate 0.7 Election 0.6 0.5 0.8 0.5 0.8 0.7 0.7 0.6 Stability 0.6 0.6 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0 0 1 3 5 7 9 Week 11 13 1 3 5 7 9 Week 11 13 1 3 5 7 9 Week 11 13
  • 7. 1 3 Tagging 5 7 9 11 13 1 3 Retweeting 5 7 9 11 13 1 3 Mentioning 5 7 9 11 13 0.12 0.1 0.1 0.08 0.08 0.06 0.06 0.04 0.04 0.02 Similarity 0.12 0.02 0 1 0 1 0.9 0.9 Entropy Average 0.8 0.8 CDU/CSU SPD 0.7 TV Election Debate 0.6 0.7 FDP Election Grüne Linke 0.6 Piraten 0.5 0.8 0.7 0.7 0.6 Stability 0.5 0.8 0.6 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0 0 1 3 5 7 9 Week 11 13 1 3 5 7 9 Week 11 13 1 3 5 7 9 Week 11 13
  • 8. Discussion • Similarity: Offline events cause parties to become more similar in terms of what and who they talk about (But do parties have different positions?) • Entropy: Offline events increase parties‘ focus on topics but not on persons • Stability: Before election parties try to maintain topical and personal focus; election debate forces them to address new topics but not persons • After the election: Time for all to restructure (Who? How?) • There are differences according to party – Greens are most similar to others – Pirates are most dissimilar to others (How do they differ exectly in terms of topics an persons?) 8

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