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Analyzing Political Communication with Digital Trace Data: The Role of Twitter Messages in Social Science Research 2015 ed. [Kõva köide]

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  • Formaat: Hardback, 220 pages, kõrgus x laius: 235x155 mm, kaal: 4912 g, 11 Illustrations, color; 37 Illustrations, black and white; XXI, 220 p. 48 illus., 11 illus. in color., 1 Hardback
  • Sari: Contributions to Political Science
  • Ilmumisaeg: 25-Jun-2015
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319203185
  • ISBN-13: 9783319203188
Teised raamatud teemal:
Analyzing Political Communication with Digital Trace Data: The Role of Twitter Messages in Social Science Research 2015 ed.Suurem pilt
  • Formaat: Hardback, 220 pages, kõrgus x laius: 235x155 mm, kaal: 4912 g, 11 Illustrations, color; 37 Illustrations, black and white; XXI, 220 p. 48 illus., 11 illus. in color., 1 Hardback
  • Sari: Contributions to Political Science
  • Ilmumisaeg: 25-Jun-2015
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319203185
  • ISBN-13: 9783319203188
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783319203188.html
Märksõnad:
This book offers a framework for the analysis of political communication in election campaigns based on digital trace data that documents political behavior, interests and opinions. The author investigates the data-generating processes leading users to interact with digital services in politically relevant contexts. These interactions produce digital traces, which in turn can be analyzed to draw inferences on political events or the phenomena that give rise to them. Various factors mediate the image of political reality emerging from digital trace data, such as the users of digital services’ political interests, attitudes or attention to politics. In order to arrive at valid inferences about the political reality on the basis of digital trace data, these mediating factors have to be accounted for. The author presents this interpretative framework in a detailed analysis of Twitter messages referring to politics in the context of the 2009 federal elections in Germany. This book will appeal to scholars interested in the field of political communication, as well as practitioners active in the political arena.
1 Introduction: How to Use Twitter in the Social Sciences
1(10)
1.1 Digital Trace Data as Information Source for Political Phenomena
1(3)
1.2 A Map of the Territory
4(2)
1.3 Research Goals
6(1)
1.4 Structure of the Book
7(4)
References
9(2)
2 Twitter, Usage and Research
11(14)
2.1 Why Twitter?
11(1)
2.2 What is Twitter and How is it Used?
12(4)
2.2.1 What is Twitter?
12(3)
2.2.2 Usage Patterns
15(1)
2.3 Political Uses of Twitter: Politicians, Activists, Citizens, and the Media
16(2)
2.4 Rise to Prominence
18(7)
References
21(4)
3 Twitter in the Analysis of Social Phenomena: An Interpretative Framework
25(44)
3.1 Analyzing Social Phenomena with Digital Trace Data
25(2)
3.2 What Are Digital Trace Data?
27(2)
3.3 Computational Social Science, Digital Methods, and Big Data: What Is Missing?
29(6)
3.3.1 Computational Social Science
29(2)
3.3.2 Digital Methods
31(1)
3.3.3 Big Data
32(2)
3.3.4 Why We Need Theory
34(1)
3.4 Twitter: Macro-Phenomena, Micro-Behavior, and Macro-Patterns
35(13)
3.4.1 Approaches to the Analysis of Digital Trace Data
35(2)
3.4.2 Macro-Phenomena, Micro-Behavior, and Macro-Patterns
37(3)
3.4.3 Metrics for the Analysis of Social Phenomena Based on Twitter Data
40(2)
3.4.4 Different Services, Different Uses, Different Data-Generating Processes
42(3)
3.4.5 Politics Through the Lens of Digital Trace Data
45(3)
3.5 A Mechanism Explaining the Publication of Tweets
48(14)
3.5.1 Social Context
50(4)
3.5.2 State of the Twittersphere
54(2)
3.5.3 External Stimuli
56(5)
3.5.4 A Person's Propensity to Tweet
61(1)
3.5.5 What Does This Mechanism Enable Us to Do?
62(1)
3.6 A Framework for the Analysis of Social Phenomena with Twitter Data
62(7)
References
63(6)
4 Twitter as Political Communication Space: Publics, Prominent Users, and Politicians
69(38)
4.1 The Political Communication Space
69(1)
4.2 Data Collection and Preparation
70(3)
4.3 Publics
73(10)
4.3.1 Publics: Message-Centric Analysis
73(2)
4.3.2 Publics: Dynamics Over Time
75(3)
4.3.3 Publics: User-Centric Analysis
78(1)
4.3.4 Publics: Political Support and Opposition
79(3)
4.3.5 Publics: Patterns in the Use of Twitter
82(1)
4.4 Prominent Users
83(10)
4.4.1 Prominent Users: General Observations
85(1)
4.4.2 Prominent Users: Message-Centric Analysis
86(3)
4.4.3 Prominent Users: User-Centric Analysis
89(2)
4.4.4 Prominent Users: Political Support
91(1)
4.4.5 Prominent Users: Patterns in the Use of Twitter
92(1)
4.5 Politicians
93(9)
4.5.1 Politicians: General Observations
93(1)
4.5.2 Politicians: Message-Centric Analysis
94(1)
4.5.3 Politicians: User-Centric Analysis
95(2)
4.5.4 Politicians: Content
97(4)
4.5.5 Politicians: Patterns in the Use of Twitter
101(1)
4.6 Publics, Prominent Users, and Politicians in Their Use of Twitter
102(5)
References
104(3)
5 Sensor of Attention to Politics
107(48)
5.1 Connecting Politically Relevant Events to Spikes in the Volume of Twitter Messages
107(2)
5.2 How to Detect Spikes in Data Streams?
109(3)
5.3 Identifying Politically Relevant Events
112(5)
5.4 Political Events and Their Shadows on Twitter
117(33)
5.4.1 Event Detection by Differencing
120(3)
5.4.2 Event Detection by Regression Models
123(5)
5.4.3 Index of Political Relevance
128(4)
5.4.4 Parties
132(4)
5.4.5 Candidates
136(3)
5.4.6 Controversies
139(11)
5.5 Twitter as a Sensor of Political Attention
150(5)
References
151(4)
6 The Media Connection
155(34)
6.1 The Connection Between Political Media Coverage and Twitter Activity
155(1)
6.2 Data Set and Method
156(3)
6.3 Temporal Patterns in the Coverage of Politics on Twitter and Traditional Media
159(9)
6.3.1 Temporal Dynamics
159(2)
6.3.2 Common Patterns in the Mentions of Political Actors Across Media Types
161(7)
6.4 What Do Users Tweet About in Reaction to Mediated Events?
168(17)
6.4.1 Ratification of the Access Impediment Act: Temporal Dynamics and Content
170(4)
6.4.2 State Elections: Temporal Dynamics and Content
174(4)
6.4.3 Televised Leaders' Debate: Temporal Dynamics and Content
178(4)
6.4.4 Election Day: Temporal Dynamics and Content
182(3)
6.5 Twitter and Political Coverage by Traditional Media
185(4)
References
186(3)
7 Predictor of Electoral Success and Public Opinion at Large
189(22)
7.1 The Connection Between Attention on Twitter and Electoral Fortunes
189(1)
7.2 Predicting Election Results Using Twitter Data: The Evidence
190(4)
7.3 Mechanisms Linking Twitter Messages with Opinion Polls and Election Results
194(3)
7.4 Method
197(1)
7.5 Twitter Metrics and Their Relationship to the Electoral Fortunes of Parties
198(9)
7.5.1 Aggregates, Twitter Metrics: Users, Mentions and Sentiment
198(5)
7.5.2 Dynamics of Hashtag Mentions Compared to Shifts in Opinion Polls
203(4)
7.6 No Indicator of Political Support or Public Opinion at Large
207(4)
References
208(3)
8 Conclusion: Twitter and the Analysis of Social Phenomena
211
8.1 Early Days
211(1)
8.2 Characteristics of Twitter as a Political Communication Space
212(3)
8.3 A Framework for the Use of Twitter in the Analysis of Social Phenomena
215(2)
8.4 Twitter: Political Communication Space and Mediator of Politics
217
References
219
Andreas Jungherr is a research fellow at the Chair of Political Psychology at the University of Mannheim, Germany. His research focuses on the use of digital trace data in the social sciences and the effects of the Internet on political communication and electoral campaigns. His research has been published in Journal of Communication, Internet Research and Social Science Computer Review.