The actions in BAIT comprised three studies.
The first study met RO1: To establish and quantify the discursive features of blame avoidance used in Twitter debates
We focused on how legitimations were expressed in government social media communication, using the tweets from the UK Brexit department as an example. The study used corpus techniques to quantify forms of blame avoidance. We identified six types of blame avoidance strategies and the lexical bundles that characterised them.
The second study met RO2 and evaluated the effects of blame avoidance expressed in Twitter, through analysing the responses that arise in the form of replies and retweets.
We developed a framework for identifying different strategies of blaming used on social media to criticise governments. Drawing on the linguistic theory of Appraisal, we distinguished between blame attributions based on negative judgements of the target’s capacity, veracity, propriety and tenacity. We added a three-fold distinction based on the target’s (a) bad character, (b) bad behaviour, or (c) negative outcomes. We applied this to data from a corpus of replies by Twitter users to tweets by British government ministers in the UK (11,572,787 words). Our results showed that the blame avoidance varied according to the topic of the blame storm. The proposed typology of blaming strategies built along judgemental basis and focus offers a structured template for analysing blaming discourse in political text and talk in large datasets of online conflict talk, offering a fine-grained understanding of the practices of protest.
The third study met RO3 and investigated how blame avoidance affects how people think and feel.
We designed and carried out two online survey experiments to investigate the effects of blaming discourse on Twitter. We created stimulus texts modelled on authentic examples from WP2, created questions for participants to test measures of blameworthiness and ‘retweetability’. The results suggested that tweets blaming politicians for the outcome of their actions were perceived as less critical than those blaming politicians for their behaviour or character. However, tweets focusing on character less retweetable than the other two types. Experiment 2 suggested a possible interaction between the two predictors. Blaming focused on character was always evaluated as most negative, regardless of the basis of blame. However, for blaming focused on behaviour and outcome, blaming based on veracity was perceived as significantly more negative.
We disseminated the results of the project through three articles in three academic journals; held an international roundtable for blame studies in political discourse, presented seven international papers at academic conferences, held a masterclass for school students in the UK and offered a policy brief to government policy makers.
