3 Conversationalization Hypothesis by Norman Fairclough (CDA) “the modelling of public discourse upon the discursive practices of ordinary life, ‘conversational’ practices in a broad sense” (Fairclough, 1994: 253) movement towards norms of ‘casual’ conversation in university brochures, news reports etc. Example: Di’s butler bows out... in sneakers. (headline Daily Mirror) Conversational vocabulary Graphic devices
4 Di’s butler bows out... in sneakers. description of certain stylistic markers as ‘conversational’ is problematic (Pearce (2005)) Which word is conversational? ‘bows out’ (vs. ‘resigns’)? ‘sneakers’ (vs. ‘trainers’)? ‘Di’ (vs. ‘Diana’ / ‘Princess Diana’)? Lexical density Tense Intuitively plausible but intuitive approach
5 VU-Ster project Goal empirically test Fairclough’s conversationalization hypothesis for Dutch public discourse Corpus analysis Dutch news from 1950 2002 Dutch news from 2002 Dutch conversations from 2002 News 1950: 30,000w 2002: 50,000w 5 national newspapers; different sections Conversations 50,000w from Corpus of Spoken Dutch 30 complete spontaneous conversations
6 To metaphor… Conversationalization includes: colloquial vocabulary; phonic, prosodic and paralinguistic features of colloquial language; direct address (you and we); repetition; lack of subject-verb agreement Biber’s features of involved vs informational production involved: causative subordination; wh-questions/clauses; etc. Can same be observed for metaphor? seen as conventional, stylistic property, rhetorical effect
7 Metaphor in conversation Studies of metaphor in conversations Focus on certain forms and functions (Cameron 2003, 2008; Drew & Holt 1995) in certain settings Idiomatic expressions Delexicalised verbs (lexical density) Position in sentence
8 Idiomatic expressions Their role in English conversations Cheshire (2005): fixed expressions function as a means to help speakers keep up with the demand of online speech production Drew and Holt (1995): idiomatic expressions in naturally occurring conversations seem to be used predominantly for topic summarizing and topic termination purposes Their role in Dutch conversations Termination and summary function; topic transition and start of new topic
9 Example 78: ja je gaat vanavond maar weer flink te s aan de zuip. 79: ik heb een kater vandaag gewoon. 80: ongelooflijk. 81: ik heb uh helse pijnen doorstaan. 82: ik ben nog maar net uit b uit bed. 83: net nou eigenlijk net. 84: ja dat kasteelbier van jou dat uh dat ga dat hakt erin als een kasteel de volgende dag. 85: ja die zijn inderdaad genadeloos ja. 86: ik heb trouwens uh... 87: ben net even naar de videotheek geweest. 88: en daar lag gewoon Lars Von Trier The Idiots bij de videotheek. 89: bij in de vijfhoek. (fn000496)
10 Idioms in news similar? Occur more often in news than in conversation roughly 1 per 500 words Functions are similar a lot of the examples fit within termination, summary, transition function Example: Bijbelimporteur drijft wig tussen China en de VS (Vbu2)
11 Position of metaphor Do metaphor-related words occur at beginning, middle or end of a sentence What is expected? Are expectations different for conversation and news? What are the results? Are results different for conversation and news?
14 Form, function, position Comparison between conversations and news patterns on different levels diachronic element for conversationalization with respect to form and position Conceptual analysis of metaphor patterns in registers
15 To subjectivity Definition: the degree to which the presence of the speaker (/writer) is felt e.g. when speaker gives opinion or shows (un-)certainty Why subjectivity? Presence of speaker in conversations Examples: It is a beautiful city. Maybe your friend will come to the party. John must be ill. SPEAKER subjectivity
17 1. Text level: coherence relations Relations between text parts like Cause-Consequence, Contrast, Evidence etc. Capture part of what makes a text a text (rather than a random set of sentences) Starting point: Rhetorical Structure Theory (RST; Mann & Thompson, 1988) fairly exhaustive list of 24 well-defined relations
18 Subjective relations 10 subjective relations 1. Antithesis 2. Concession 3. Concessive opposition 4. Enablement 5. Enumeration 6. Evidence 7. Evaluation 8. Interpretation 9. Justify 10. Motivation Conversationalization hypothesis: The relative amount of subjective relations has increased over time.
20 Subjective relations in news 19502002 Chi 2 per 10,000 w Antithesis 126.96.36.199149 Concession 28.529.30.033829 Concessive opposition 188.8.131.521172 Enablement 0.01.6*** Enumeration 184.108.40.206456 Evaluation 10.06.42.558607 Evidence 220.127.116.116287 Interpretation 16.26.415.36982 Justify 1.90.2*** Motivation 0.0 *** 107.5102.20.396887 increase decrease no change
21 Conclusion coherence relations Overall number of subjective relations has not changed significantly, but the nature of the textual subjectivity has: ‘Old’ newspapers interpret more, ‘new’ newspapers prove / conclude more Explanation: back to texts
22 2. Sentence/word level Following Bekker (2006), Scheibman (2002), Wiebe (2005) Intensifiers very, enormously Modal verbs must Modal adverbs maybe, presumably, certainly Verbs of cognition think, say First and second person pronouns I, you Direct questions uncertainty; listener is addressed
24 Conclusion lexico-grammatical analysis Only marginal support for conversationalization hypothesis More research is needed Verbs of cognition only 1st person Direct speech exclude character speech not straight-forward: e.g. Semi-Direct Speech
25 Conclusion lexico-grammatical analysis Only marginal support for conversationalization hypothesis More research is needed Verbs of cognition only 1st person Direct speech exclude character speech not straight-forward: e.g. Semi-Direct Speech De enige werkelijke oplossing is de sluiting van het terrein, stelt Molenman. (Nbi1) The only real solution is closing the area, says Molenman. De Ned. marine had een zeer gunstige indruk op hem gemaakt, zo zei hij. (TRObu2) The Dutch navy had made a very favourable impression on him, he said.
26 What’s next? Refine lexico-grammatical analysis Automatic lexico-grammatical analysis of larger corpus Qualitative studies conversation 1950 analysis of perspective etc. Reception experiment Possibly automatic analysis of adjectives and nouns with help from Computational Lexicology & Terminology Lab