Page | 1 Using Twitter data to analyse the spatial patterns of online anti- immigration sentiment in the UK Matt Mason*1 and Francisco Rowe†1 1Geographic Data Science Lab, The University of Liverpool GISRUK 2023 (Preliminary Abstract) Summary There is a growing academic literature examining anti-immigration sentiment posted onto social media platforms, with evidence emerging of its impact on rises in “real physical” incidents of hate. Despite this, little is understood about the spatial pattern of the production of online anti-immigration content and contextual factors contributing to shaping its spatial configuration. This study aims to use Twitter data and natural language processing to analyse spatial patterns of online sentiment towards immigration across sub-regional areas of the UK, and identify key demographic and contextual factors associated with the production of anti-immigration sentiment on social media platforms. KEYWORDS: anti-immigration sentiment, social media, natural language processing, early career 1. Research Context Recent decades have seen anti-immigration rhetoric pervade public discourse across much of the global North. This has led to an upsurge in support for populist right-wing political parties (Halla et al., 2017) and the implementation of harsher policies towards migrants and refugees across many countries. Public opinion towards immigration is influenced increasingly by social media platforms (Ekham, 2019) and evidence is emerging of a spatial relationship between the production and consumption of hate speech against minority groups on social media and incidents of offline hate crimes (Williams et al., 2020; Müller and Schwarz, 2021). Despite this, little is understood about how online anti- immigration sentiment is locally distributed across space. This study aims to analyse local spatial patterns of the production of anti-immigration sentiment on social media and identify the key demographic and contextual factors associated with this, using the example of the UK and data collected from Twitter. This work is of interest to both researchers interested in understanding public attitudes to immigration and to policy makers who must navigate the societal impact social media platforms can have. Production of online anti-immigration sentiment is likely to be unequally distributed across local areas inside a country. Prior quantitative analysis has demonstrated that the amount of online content targeting migrant and ethnic minority communities differs between nations (Mondal et al., 2017; Rowe et al., 2021). Additionally, attitudes to immigration have been shown to vary depending on intergroup contact between native and foreign-born individuals; some studies have provided evidence for interaction dampening xenophobic views (Jolly and DiGiusto, 2014), with others finding the opposite in areas with challenging economic conditions (Hjerm, 2009). Levels of interaction between native and migrant communities are likely to differ significantly across areas of the UK given the unequal spatial distribution of migrants. Thus, we expect our results will show a distribution of relative levels of online anti-immigration sentiment production associated to a degree with levels of intergroup contact across areas. Previous research has found that demographic characteristics, such as education and income levels, can influence attitudes towards immigration also, with both having a negative relationship with holding views hostile to immigration (McLaren and Johnson, 2007). These contextual forces are also * matt.mason@liverpool.ac.uk † fcorowe@liverpool.ac.uk