Figures
2.1 Hallidayan SFL model of language 14
2.2 Deictic positioning 23
2.3 The three reading positions 25
3.1 Search filters and expanded context of the CEPIC 37
3.2 The concordance list and word collocation result of a sample search of ‘people’ 41
3.3 The concordance list and word collocation result of a sample search of ‘people’ with its collocate ‘more’ 41
3.4 The expanded context of the sample keyword search ‘people’ 42
3.5 A sample search of pauses longer than 9 seconds (‘[…………]’) 43
3.6 A sample pause longer than 9 seconds in extended context 43
3.7 A sample pause longer than 9 seconds in extended context 45
3.8 CEPIC (2.0) on ELAN 47
4.1 Bi-plot of the Discriminant Correspondence Analysis 64
4.2 Variation between the 239 plenary speeches in the national parliaments 64
4.3 Variation between the 43 plenary speeches in the EP 64
4.4 Variation between the 29 interpretations from English to Dutch in the EP 65
4.5 Variation between the 137 interpretations from French to Dutch in the EP 65
4.6 Statistical differences between the four varieties 66
5.1 Chinese and English Dimension 1 score distributions 89
5.2 Chinese and English Dimensions 2 score distributions 92
5.3 Chinese Dimension 3 and English Dimensions 4 score distributions 94
5.4 Hierarchical agglomerative clustering of OC, IE, NE, and reference registers 95
5.5 Distributions of four features in five 2,000-word samples of OC 98–99
6.1 Screenshot of the Microsoft Word Add-in interface for annotation 123
6.2 A screenshot of the annotation example 124
6.3 Strategic comparison of the whole speech 125
6.4 Strategic comparison of proper names 126
6.5 Strategic comparison of idioms 128
6.6 Strategic comparison of linguistic metaphors 129
7.1 First/opening pages of the four political parties: BJP, Congress, Moderaterna and Socialdemokraterna 149
7.2 Re-cycling of media content by political parties BJP (17/10 2017) and Congress (16/10 2017) on their Facebook pages 150
7.3 Re-cycling of politicians’ personal posts on political parties’ Facebook pages (3/11/2017; 14/11 2017) 151
7.4 Party representatives’ participation in the commentary flow on Facebook pages 151
7.5 Citizens participation on political parties’ mediascapes (28/10 2017; 8/11 2017; 11/10 2017; 8/11 2017) 153
7.6 Some themes in citizen participants’ comments (8/11 2017; 13/11 2017; 17/10 2017) 155
7.7 Mis-match between message and captioning of post and participant comments (30/10 2017) 156
7.8 Rhetorical captioning by opposition parties (18/10 2017) 157
7.9 Recycling of messages in political party posts (13/10 2017) 158
7.10a–b Slogans in Swedish political social mediascapes (10a: 2/10 2017; 24/10 2017; 10b: 12/11 2017; 25/11 2017) 159
7.11 Political party greetings on festivals (19/10 2017) 162
7.12 Political party leaders visiting religious sites (19/10 2017; 11/10 2017) 162
7.13 Identification and allegiance to own party (28/10 2017) 163
7.14 Identification and allegiance to different groups: ‘Proud to be Gujarati’, ‘Garv Se Gujarati’, ‘Proud to be Indian’, ‘Garv Se Congressi’ (15/11 2017; 13/11 2017) 164
7.15 Political leaders – personality cult (13/11 2017; 3/10 2017; 9/11 2017; 2/10 2017) 166
7.16 Subtitled video screengrab from the Sw-dataset (1/11 2017) 168
8.1 The pragmatics of political discourse: an analytical framework 186
8.2 A comparison of the relevant frequency of ‘we’, ‘government’ and ‘people’ in the subsets of the corpus across time 198
8.3 A comparison of the relevant frequency of ‘
8.4 Collocation graphs of ‘government’ in the subsets of the corpus 200
8.5 Collocation graphs of ‘people’ in the subsets of the corpus 201
8.6 Collocation graphs of ‘we’ in the subsets of the corpus 202
8.7 Collocation graphs of ‘government’ the HK subsets across time 203
8.8 Collocation graphs of ‘government’ the UK subsets across time 203
8.9 Collocation graphs of ‘government will’ and ‘we will’ in the subsets of the corpus 205
8.10 Collocation graphs of ‘government will’ in the UK_Budget subset 206
8.11 Collocation graphs of ‘government will’ in the HK subsets overtime 207
8.12 Collocation graphs of ‘government will’ in the UK_Budget subset overtime 208
Tables
2.1 The appraisal framework 17
2.2 Evaluative profile of the attitudinal parameter AFFECT in Trump’s 2017 inaugural speech 18
3.1 The composition of the CEPIC by language 35
3.2 The composition of the CEPIC by speech types 35
3.3 The metadata of CEPIC 36
3.4 A comparison of the PM frequencies by interpreters and native English language speakers 44
4.1 Overview of the corpora 62
4.2 Coefficients of the Mixed-effects Model overview 69
5.1 Corpora used in Chinese and English (Biber, 1988) MD analyses 79
5.2 Corpora composition 83
5.3 Chinese dimension scores by premier 85
5.4 English dimension scores by IE and NE 87
5.5 Features of significantly higher frequencies in Premier Wen’s speech 100
5.6 Features of significantly higher frequencies in Premier Li’s speech 101
5.7 Features of no significant differences between Premiers Wen and Li’s speech 102
5.8 Features of significantly higher frequencies in IE 105
5.9 Features of significantly higher frequencies in NE 106
5.10 Features of no significant differences between IE and NE 107
5.11 Features of significantly higher frequencies in IEW 109
5.12 Features of significantly higher frequencies in IEL 110
5.13 Features of no significant differences between IEW and IEL 110
6.1 CSI translation strategies in literature 120
6.2 Annotation codes 124
6.3 Descriptive statistics at the whole speech level 125
6.4 Descriptive statistics on processing proper names 126
6.5 Descriptive statistics on processing idioms 127
6.6 Descriptive statistics on processing linguistic metaphors 129
7.1 Quantitative overview of the Indian and Swedish Facebook datasets of four political parties 147
8.1 Basic statistics of the corpus 188
8.2 Top 50 high frequency words in the subsets of the corpus 190
8.3 Top 15 2-grams of the subsets 194
9.1 Number of occurrences of 4- to 8-grams in the Chinese Foreign Affairs corpus 222
9.2 The number of mentions of China’s neighbours mentioned in the full corpus of briefings Chinese Foreign Affairs, 2016 228