This corpus-based study examines how writers of tweets about organic food use hashtags to direct readers towards the preferred tweet interpretation while expressing their stance to organic food. Our aim is to identify the functions stance-conveying hashtags serve in these tweets. To this end, we draw on Du Bois’ (2007) approach to stance and Francis’ (1994) analysis of metalinguistic labels. We analyse the tweets in which the sixteen most frequent stance-conveying hashtags occur in our corpus. We carry out a qualitative analysis where we identify four stance-conveying hashtag functions: (1) taking a stance, (2) expressing the tweet writer’s feelings, (3) invoking the reader’s stance and (4) indicating the intended tweet interpretation, which includes (4.1) expressing a directive and potentially presenting it as being of a specific type (deontic hashtags), and (4.2) commenting on the epistemic status of the information in the tweet (epistemic hashtags). We evaluate the categorisation scheme based on two annotation rounds and measure inter-annotator agreement. The study highlights the role of deontic hashtags (e.g., #advice) and epistemic hashtags (e.g., #truth) in directing the readers towards a particular interpretation, which may cause readers to ignore certain tweet aspects, thus homing in on the interaction between stance-taking hashtags and what is conveyed by the tweet in their scope. We offer explanations for the different roles of these hashtags as meta-discursive instructions, whereby tweeters point out to their readers what they should do, think or feel in relation to the message of the tweet. Our findings illustrate how hashtags are strategically exploited by writers for communicative purposes.
Taking stance towards any topic, event or idea is a common phenomenon on Twitter and social media in general. Twitter users express their opinions about different matters and assess other people’s opinions in various discursive ways. The identification and analysis of the linguistic ways that people use to take different stances leads to a better understanding of the language and user behaviour on Twitter. Stance is a multidimensional concept involving a broad range of related notions such as modality, evaluation and sentiment. In this study, we annotate data from Twitter using six notional stance categories ––contrariety, hypotheticality, necessity, prediction, source of knowledge and uncertainty––¬¬ following a comprehensive annotation protocol including inter-coder reliability measurements. The relatively low agreement between annotators highlighted the challenges that the task entailed, which made us question the inter-annotator agreement score as a reliable measurement of annotation quality of notional categories. The nature of the data, the difficulty of the stance annotation task and the type of stance categories are discussed, and potential solutions are suggested
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