Presentation on theme: "Using computer tools to analyze the words in “Judge Dredd”"— Presentation transcript:
Using computer tools to analyze the words in “Judge Dredd”
Beginnings – considering the whys, whats and hows Methodological issues Analysis and some results Conclusions – limitations and further research
2000 AD first appeared in 1977 “Judge Dredd” appeared in issue 2 Publishers 1977: IPC/Fleetway Publishers Now: Rebellion Developments (large gaming developer)
Been around for over 100 years Originally : funny - comical / satirical Poor quality (printing) Considered poor quality (literature)
Largely ignored Commentaries on linguistics features often vague
“ […] 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).
Long history 1977 to present day Same author contributed over that time Access to data
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
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?
1977 All the words from 52 episodes of Judge Dredd 2003 All the words from 52 episodes of Judge Dredd comparison
Not all 52 weeks collected Just one author used (John Wagner) Stopped collecting at around words Time constraints
Corpus name Years used Number of words Number of texts Average words/edition JD7778C1977/ JD0203C2002/
Corpus name Years used Number of words Number of texts Average words/sentence JD7778C1977/ JD0203C2002/
Comic strips: combine words and pictures consist of a number of components (see, for example, McCloud 1994)
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)
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?
Consistently higher frequencies of: prounouns – you / I / we contractions – ‘s / n’t negation – n’t
Consistently lower frequencies of: the / of – fewer nouns / less post modification of nouns conjunctions – and / that
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.
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
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.
“I’ve got to get a recharge” “we’ve got to get away from here” “you’ve got to get out of this”
Get 20% in imperative structures “get away from me” “get after him” “get that garbage cleaned up”
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).
She / her In JD0203C – female pronouns more frequent Female characters more prevalent and important.
Expletives drokk has remained a feature of the comic strip over the twenty-five year history JD0203C - some extra expletives: grud, damn, and freakin,
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
“In the heart of Mega-City 1, huge metropolis of the 22 nd Century, lies a giant building,” “Mega-City 1. Vast metropolis of the 22 nd 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”
“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”
In JD7778C – captions seem to be 3 rd person narration. In JD0203C – the captions often similar to internal monologue
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.
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.
Subject / dummy subject deletion attempts to show that thoughts consist of sentence fragments rather than complete sentences an attempt to differentiate thinking from talking
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”
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.
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
Issue – what counts as picture text? Is the category adequate? Applying categories to data – form of interpretation
Speech Balloons – contain most of the words Features of spoken language Captions – used differently in JD0203C Thought balloons – more frequent in JD7778C Sound effects – more frequent in JD0203C Picture text – difficult to draw conclusions.
Pictures seem to do more of the story telling.
This study deals with only one comic strip from one comic Not possible to make generalizations about the language of all comic strips Any findings only relate to the language used in Judge Dredd as featured in 2000AD when written by John Wagner.
However, this study could be seen as a start in the description of what constitutes comic strip language. And I would suggest that some of the features found here will be found in other comic strips.
Comic strips are fictional narratives that contain mainly characters’ words Interactions between characters
It’s possible … Other modern comic strips will let the pictures tell the story use fewer words than might have been used in the past But these things would have to be explored using more data.
Look at more comic strips (past and present) Different sub-genres – humorous etc. More detailed analysis of how the various components work (captions - internal monologue) Picture text Multi-modal analysis
Eisner, W. (1996) Graphic Storytelling & Visual Narrative McCloud, S. (1994) Understanding Comics: The invisible Art Sabin, R Comics, comix & graphic novels: A history of comic art Saraceni, M. (2003) The Language of Comics