Depictions of deception: A corpus-based analysis of five Shakespearean characters

Abstract

Drawing on the Enhanced Shakespearean Corpus: First Folio Plus and using corpus-based methods, this article explores, quantitatively and qualitatively, Shakespeare’s depictions of five deceptive characters (Aaron, Tamora, Iago, Lady Macbeth and Falstaff). Our analysis adopts three strands: firstly, statistical keywords relating to each character are examined to determine what this tells us about their natures more generally. Secondly, the wordlists produced for each of the five characters are drawn upon to determine the extent to which they make use of linguistic features that have been correlated with, or linked to, acts of deliberate deception in real-world contexts. Thirdly, we make use of the results identified during the two aforementioned strands by using them to identify particular (sequences of) turns that are worthy of more detailed analysis. Here, we are primarily interested in (a) whether these keywords/deceptive indicators cluster or co-occur and (b) whether these interactions are the same as those identified by other scholars exploring depictions of deception in Shakespeare from a literary perspective. The findings indicate that deception-related features are indeed used collectively/in close proximity, by Shakespeare, at points where a character speaks to other characters disingenuously. They also suggest that Shakespeare’s deceptive depictions do change stylistically, from character to character, in line with those characters’ different characterisations and situations, that Shakespeare draws on atypical language features – such as self-oriented references – when it comes to some of his depictions of deception and that Shakespeare uses these various stylistic features to achieve a range of dramatic effect(s).

Corpus Pragmatics

“…For instance, for requests, Culpeper and Archer (2008) identify a tendency of using multiple requests in the same turn for their data from Early Modern English trials and, to a lesser extent, in drama. Likewise, Archer and Gillings (2020), in their study of lying and deception in Shakespeare's plays, note that deceptive features tend to occur in clusters. Vaughan et al (2017) find that certain items of vague language tend to cooccur.…”

Constructions and representations of Chinese identity through England’s curatorial imagination: A corpus-assisted analysis

“…Working from the assumption that the most salient words and semantic domains are meaningful in some way, this initial analysis allows us to get a broad overview of the contents of the dataset before focusing on our specific areas of interest. Keywords and key semantic domains are identified using a three-part filtering procedure, as in Archer and Gillings (2020).…”

Corpus-Assisted Discourse Studies

“…• Type 1: a structured top-down process whereby the researcher applies an a priori framework or set of categories to a number of concordance lines (e.g., Culpeper and Gillings (2018) coding for politeness in the BNC1994/ 2014, and Lutzky (2021b) exploring the pragmatic functions of sorry in customer service interactions on Twitter); • Type 2: a structured bottom-up process whereby the researcher assigns categories to the concordance lines, but these come organically from the corpus rather than being imposed on it (e.g., Kopf (2019) exploring the ways that content policies are enforced on Wikipedia, and Zottola et al (2021) identifying coping strategies that patients use in autobiographical narratives while waiting for assessment at a transgender health clinic); • Type 3: an unstructured bottom-up process whereby the researcher eyeballs the concordance lines and lets that qualitative holistic judgement form the basis of analysis (e.g., McEnery, Baker and Dayrell (2019) identifying previously unrecorded droughts in nineteenth-century Britain, and Levon (2016) exploring the extent to which users on a question-and-answer forum use their replies as an opportunity for stance-taking); • Type 4: an unstructured top-down process whereby the researcher identifies concordance lines which match categories proven to be relevant in other datasets (e.g., Archer and Gillings (2020) identifying potential indicators of deception in Shakespeare's plays, and Appleton (2021) exploring how the unification of Germany is discussed in Hansard). Types 1 and 2 both call for the researcher to sift through each and every concordance line within a sample, but they differ with regard to whether categorisation is something that is applied to, or extracted from, the data.…”