Mathew Gillings

Corpus Linguistic Approaches to Deception Detection provides an innovative introduction to the use of the corpus linguistics methodology in the field of deception detection. Bringing together research from both forensic psychology and linguistics, this book uses traditional corpus-assisted methods to reconcile the different approaches used by these two fields and shows how “cues to deception” operate in their linguistic context. Arguing that current methods of analysis do not seem to be fit for purpose, this book shows the need for further development of context-sensitive methods to explore deceptive datasets. This book will be of interest to scholars and postgraduate students in the fields of corpus linguistics, psychology, discourse analysis, and forensic linguistics.

Corpus Linguistic Approaches to Deception Detection provides an innovative introduction to the use of the corpus linguistics methodology in the field of deception detection.Bringing together research from both forensic psychology and linguistics, this book uses traditional corpus-assisted methods to reconcile the different approaches used by these two fields and shows how “cues to deception” operate in their linguistic context. Arguing that current methods of analysis do not seem to be fit for purpose, this book shows the need for further development of context-sensitive methods to explore deceptive datasets.This book will be of interest to scholars and postgraduate students in the fields of corpus linguistics, psychology, discourse analysis, and forensic linguistics.

The breadth and spread of corpus-assisted discourse studies (CADS) indicate its usefulness for exploring language use within a social context. However, its theoretical foundations, limitations, and epistemological implications must be considered so that we can adjust our research designs accordingly. This Element offers a compact guide to which corpus linguistic tools are available and how they can contribute to finding out more about discourse. It will appeal to researchers both new and experienced, within the CADS community and beyond.