Blame Avoidance in Twitter

Project Information

BAIT

Grant agreement ID: 891933

DOI

10.3030/891933

Project closed

EC signature date 28 March 2020

Start date 12 October 2020

End date 11 October 2022

Funded under

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Total cost

€ 212 933,76

EU contribution

€ 212 933,76

Coordinated by

THE UNIVERSITY OF BIRMINGHAM
United Kingdom

Periodic Reporting for period 1 - BAIT (Blame Avoidance in Twitter)

Reporting period: 2020-10-12 to 2022-10-11

Summary of the context and overall objectives of the project

Blame avoidance is a communication strategy used by government officials when they initiate unpopular policies. However, blame avoidance may result in disaffection rather than support from citizens. The key problem addressed in the Blame Avoidance in Twitter project (BAIT) is to investigate how the blame avoidance by government officials is expressed in language and find out how citizens respond to the ways in which blame avoidance is expressed. Better understanding of how blame avoidance is used on Twitter and how citizens respond to this has important consequences for society. Perceptions of blame avoidance impact how citizens think and feel about politicians and how citizens behave in the real world (for example, by expressing protest or making voting choices).
The overall aim of the BAIT project is to generate new knowledge about blame-related political communication by analysing data from Twitter as an influential site used by politicians and citizens. The specific objectives are to
1.Establish and quantify the discursive features of blame avoidance used in Twitter debates
2.Evaluate the effects of blame avoidance expressed in Twitter, through analysing the responses that arise in the form of replies and retweets
3.Investigate how blame avoidance affects how people think and feel
4.Disseminate the findings of the project to academics and the wider public, including politicians and activists.
The actions in BAIT comprised three studies, which use tools from corpus linguistics, discourse analysis and political science to investigate the linguistic patterns of blame avoidance in Twitter and the effects these have on citizens.
The first study analysed the blame avoidance strategies used in the Twitter posts of the British government’s Brexit department during its lifespan.
The second study analysed a corpus of replies by Twitter users to tweets by British government ministers about two highly contentious issues, Covid-19 and Brexit, in 2020-2021.
The third study used two survey experiments to investigate the effects of blaming discourse on Twitter, identifying which forms of blaming were perceived as the most negative and which were most likely to be retweeted.
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.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

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.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

The project has contributed new knowledge which has potential impact within applied linguistics, media studies and political science.
The results of study 1 will open up new areas of inquiry in Critical Discourse Studies, where legitimation is a key aspect of communication used not only by governments but many other kinds of organisations and individuals.
The research in study 2 was used to develop a typology of blaming strategies built on two dimensions: the judgemental basis and focus of blaming. We demonstrated the value of this typology in relation to original data relating to two high-level blame storms in the United Kingdom in 2021 (Brexit and the government’s actions in response to the Covid-19 Pandemic). The typology developed in this paper sets out the methodological groundwork for future comparisons of blame games in other contexts.
The results of study 3 were from two survey experiments which tested the effect of the micro-linguistic features of blaming on the perceived negativity and retweetability of the blaming messages. The results showed that blaming a politician for their character was more perceived as more negative than blaming based on outcomes, that blaming based on veracity was perceived as more negative than capacity. The results for retweetability showed that the blaming messages focused on outcomes were the most retweetable. Rather than assuming that Twitter is primarily an incivil context for political discourse, our study suggests a more nuanced picture, where concerns for sharing information are more important than promoting personal attacks.
There are societal impacts for the research in the BAIT project. In sharing our research with school students, we have helped shape their understanding of how blame avoidance and blaming is part of a democratic society. In our policy brief, we recommend that as policy makers interact in social media spaces they prioritise interactions which focus on policy decisions and outcomes, as these are most likely to gain visibility and traction via retweets.

BAIT framework for blaming discourse