Corpus-assisted analysis of legitimation strategies in government social media communication

When governments introduce controversial policies that many citizens disapprove of, officeholders increasingly use discursive legitimation strategies in their public communication to ward off blame. In this paper, we contribute to the study of blame avoidance in government social media communication by exploring how corpus- assisted discourse analysis helps to identify three types of common legitimations: self-defensive appeals to (1) personal authority of policymakers, (2) impersonal authority of rules or documents and (3) goals or effects of policies. We use a specialised corpus of tweets by the Brexit department of the British government (42,618 words) which we analyse both qualitatively and quantitatively. We demonstrate how the analysis of lexical bundles that characterise each type of legitimation might provide a new avenue for identifying the presence, characteristics and uses of these legitimations in larger datasets.