A diachronic investigation of the comic strip “Judge Dredd ”.

1. Using computer tools to analyze the words in “Judge Dredd” A diachronic investigation of the comic strip “Judge Dredd”.

2. Lecture structure • Beginnings – considering the whys, whats and hows • Methodological issues • Analysis and some results • Conclusions – limitations and further research

3. 2000AD and Judge Dredd • 2000 AD first appeared in 1977 • “Judge Dredd” appeared in issue 2 • Publishers 1977: IPC/Fleetway • Publishers Now: Rebellion Developments • (large gaming developer) • http://www.2000adonline.com

4. 2002 1977

5. Why a comic strip? • Been around for over 100 years • Originally : • funny - comical / satirical • Poor quality (printing) • Considered poor quality (literature)

6. Why a comic strip? • Largely ignored • Commentaries on linguistics features often vague

7. Why a comic strip? • “ […] comics are a language […] which has its own syntax, grammar and conventions, and which can communicate ideas in a totally unique fashion.” (Sabin,1996:8).

8. Why Judge Dredd? • Long history 1977 to present day • Same author contributed over that time • Access to data

9. What is being investigated? • Just the words of the comic strip are being analyzed • No visual analysis • Investigating whether the comic strip has changed diachronically • Also whether there are any stable language features

10. How? • I want to compare Judge Dredd at two points in time (1977 and 2003) • I have some comics from 1977 and from 2003 • How do I do the analysis? • How many comics do I need to analyse? • What do I analyse?

11. How? 1977 All the words from 52 episodes of Judge Dredd 2003 All the words from 52 episodes of Judge Dredd comparison

12. Dataset building • Not all 52 weeks collected • Just one author used (John Wagner) • Stopped collecting at around 10000 words • Time constraints

13. Dataset profiles

14. Dataset profiles

15. Components of a comic strip • Comic strips: • combine words and pictures • consist of a number of components • (see, for example, McCloud 1994)

16. Panels and Gutters

17. Speech Balloons Speech Balloons

18. Thought Balloons Thought Balloons

19. Captions Captions

20. Component analysis • Analyses data using an existing framework (or existing categories) • Separates data into categories • Forces decisions about data • Exposes data that does not fit into categories • Can suggest new categories (driven by data)

21. Sound Effects Sound FX

22. Picture Text Picture Text

23. Comic composition

24. Component analysis • Analysis based on forms – what the various components look like. • Speech/thought balloons, and captions look like speech/thought balloons and captions. • but what about their content and function?

25. Speech balloons • Consistently higher frequencies of: • prounouns – you / I / we • contractions– ‘s /n’t • negation–n’t

26. Speech balloons • Consistently lower frequencies of: • the / of – fewer nouns / less post modification of nouns • conjunctions – and / that

27. Speech balloons • you, your, you’re, you’ve, ya • all these pronouns require an addressee and indicate involvement with that addressee • indicates that speech balloon data not only involves characters talking, but talking to an addressee, • interaction between characters is important in comic strip narrative.

28. Speech balloons • I, I’m, Me • seems to indicate that characters also talk about themselves, or to themselves. • 50% of occurrences of I’m are followed by an ing-participle. • shows characters interacting • helps to tell what’s happening (running commentary) • Progressive aspect - on going action

29. Speech balloons • got • 33% of instances of got involve HAVE, forming a semi-modal relating to obligation or necessity • adds a sense of urgency or a degree of compulsion to what the characters say. • heightens the sense of drama in the story.

30. Speech balloons “I’ve got to get a recharge” “we’ve got to get away from here” “you’ve got to get out of this”

31. Speech balloons • Get • 20% in imperative structures “get away from me” “get after him” “get that garbage cleaned up”

32. Speech balloons • gotta and gonna • The orthographic representations of spoken language are more prevalent in JD7778C than JD0203C • seems to reflect the characterization involved in certain stories (baddies).

33. Speech balloons • She / her • In JD0203C – female pronouns more frequent • Female characters more prevalent and important.

34. Speech balloons • Expletives • drokk has remained a feature of the comic strip over the twenty-five year history • JD0203C - some extra expletives: grud, damn, and freakin,

35. Caption data - comparison • In JD7778C, ‘the’ and ‘of’ more frequent Also ‘meanwhile’, ‘soon’, ‘suddenly’, ‘later’ And ‘ahead’ ‘behind’ • In JD0203C pronouns he, him, she, her, it more frequent • Differences reflect change in caption usage

36. JD7778C - examples “In the heart of Mega-City 1, huge metropolis of the 22ndCentury, lies a giant building,” “Mega-City 1. Vast metropolis of the 22nd Century.” “Slick Willy pointed to a map of the old New York subway –” “Two Troggies were left to guard the work squad. The minutes ticked by …” “Dredd pulled away some of the rubble”

37. JD0203C - examples “Dredd had the bit between his teeth. He wouldn't let up. They'd look into Bubba O‘Kelly, find the connection.” “He'd tried to put things right, only made them worse. Killed a civilian –” “But he'd been right! If they'd only opened their eyes to see ... He'd been doing good work”

39. In JD7778C – captions seem to be 3rd person narration. • In JD0203C – the captions often similar to internal monologue

40. Thought balloons • Only used in one story in JD0203C • Used more in JD7778C • Provide a running commentary – bring the reader up to speed with events in the story.

41. Thought balloons – JD0203C • HOW LONG HAVE I BEEN OUT ...? • MUST'VE PULLED ME CLEAR ... • SOMETHING'S GOING ON IN THAT BRAIN AND IT'S NOT JUST BLOOD LUST. • NOT GIVING UP ON YOU YET, PAL ... ! • PRAGER'S GOT TROUBLE! • I'VE STUMBLED ON AN UPRISING! • NO CATCHING THEM NOW ... • THEY THINK GILL'S GOING TO SQUASH EASY. THEY DON'T KNOW WHAT THEY'RE UP AGAINST.

42. Thought balloons – JD0203C • Subject / dummy subject deletion • attempts to show that thoughts consist of sentence fragments rather than complete sentences • an attempt to differentiate thinking from talking

43. Example from JD7778C

44. But … Use of conjunction but also creates tension/drama “So far so good – but the rookie’s still got to rescue the Anderson boy” “That cadet’s skills are good, but he’s not watching the alley up ahead”

45. Example from JD7778C

46. Sounds effects • JD7778C: HA, CLUNK, CRASH, AAAGH, AAAIEEE, AAARGH, BAROOM, BBAM, GULP, HAAAH, KERAAM, KERANG, NOOOOOOOO, SPLAT, SPLOSH, SPLOT, THUNK

47. Sound effects • JD0203C: BDAM, BONG, BLEE, PING, FTOOOM, WHISSSSHH, AAAHH, BUDDA, BZZZ, SHRANGGG, SPANG, SSIFFFFF, SWAKK, VZZATTTTTT, AAIEE, BDAMM, BLAMM, CHUNKK, CLANGGG, FTOMPHH, FWOOOMPHHH, GGGRUNCHH, GLURRR, GRRAAARRRR, GRRRNNNNNNN, KERRANGGGG, KRAKKOOOOOOMM, KRUNNGGG, KZANNG, NRRRRR, SHRANNGG, SKASHH, SKRREEEEEEEEEE, SLASH, SPAK, SPAPPPP, SPLATT, SPLOT, SWAKKKKKK, SZZZ, THRUMMM, THUD, THWAP, UHHH, UNGH, UNNFFFFF, URRNHH, VAWOOOOOOOM, WHUMPHH, WHUNK, WHOINININ, YAAAAYYYYYYYYY, ZINNG, ZWAKK

48. Sound effects • Sound effects more prevalent in JD0203C than they are in JD7778C • Greater variety of sounds • Adds a ‘soundtrack’ to the actions • Better printing seems to allow more to be going on in the picture without loss of clarity or cluttering.

49. Picture text • Category very small for both datasets • Some represents writing – letters etc. – important to the story • Other PT adds detail to the pictures • Can help to form meaning or provide extra information

50. Example from JD7778C