Aye or naw, whit dae ye hink? Scottish independence and linguistic identity on social media

Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 1239–1248, Valencia, Spain, April 3-7, 2017. c⃝2017 Association for Computational Linguistics Aye or naw, whit dae ye hink? Scottish independence and linguistic identity on social media Philippa Shoemark∗ p.j.shoemark@ed.ac.uk Debnil Sur† debnil@stanford.edu Luke Shrimpton∗ luke.shrimpton@ed.ac.uk Iain Murray∗ i.murray@ed.ac.uk Sharon Goldwater∗ sgwater@inf.ed.ac.uk ∗School of Informatics University of Edinburgh †Department of Computer Science Stanford University Abstract Political surveys have indicated a relation- ship between a sense of Scottish identity and voting decisions in the 2014 Scot- tish Independence Referendum. Identity is often reflected in language use, suggest- ing the intuitive hypothesis that individ- uals who support Scottish independence are more likely to use distinctively Scot- tish words than those who oppose it. In the first large-scale study of sociolinguistic variation on social media in the UK, we identify distinctively Scottish terms in a data-driven way, and find that these terms are indeed used at a higher rate by users of pro-independence hashtags than by users of anti-independence hashtags. However, we also find that in general people are less likely to use distinctively Scottish words in tweets with referendum-related hashtags than in their general Twitter activity. We attribute this difference to style-shifting rel- ative to audience, aligning with previous work showing that Twitter users tend to use fewer local variants when addressing a broader audience. 1 Introduction A central idea from sociolinguistics is that people’s social identity is reflected in their use of language, and that people modulate their use of language in order to present particular identities in different situations. The recent availability of social media data has raised interest in confirming and extending these results using large scale datasets. For exam- ple, Twitter data has been used to examine patterns of regional variation in general US English (Doyle, 2014; Huang et al., 2015), African American En- glish (Jones, 2015), and global Spanish (Gonc¸alves and S´anchez, 2014), and to study variation asso- ciated with factors such as race/ethnicity (Jones, 2015; Blodgett et al., 2016; Jørgensen et al., 2015) and gender (Bamman et al., 2014). These studies have shown that tweets mirror spoken language in many ways, such as displaying dialect variation not only in the use of distinct lexical items, but also in the use of non-standard spellings to indicate non- standard pronunciation—in fact, these spellings even reflect the phonological processes found in spoken language (Eisenstein, 2015). There is also evidence that, as in spoken language, individuals may shift their style of language in response to the audience. In particular, studies have found that when the expected audience of a tweet is larger, Americans use fewer non-standard and local words (Pavalanathan and Eisenstein, 2015) and Dutch bilingual speakers of a minority language are more likely to use Dutch rather than their other language (Nguyen et al., 2015). A small-scale case study of a single Scottish Twitter user also provides pre- liminary evidence that users may modulate their production of regional variants according …

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