Understanding the role of social media in promoting anti-migration sentiment and hate crime

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Aim and objectives This proposal aims to investigate the role of social media in influencing hate crime in the United Kingdom using Twitter, GPS data, machine learning, network science, longitudinal and causal inference approaches. Specifically, the project seeks to understand the role of social media in influencing anti-immigration sentiment, subsequent acts of violence, and how these patterns may relate to time exposure to ethnic communities. It has three objectives: 1. Identify Twitter anti-migration communities, and examine the digital and geographic context within which they emerge; 2. Assess and correct representativeness biases in Twitter data; and 3. Estimate the causal impact of anti-migration Twitter content on hate crime. The project will advance our understanding of the size and structure of online anti-migration communities and evidence on how online content can reinforce and spread xenophobic sentiment leading to hateful actions. Such evidence will help to design policy programmes to counter discrimination by leveraging on ongoing collaborations between Dr. Rowe (primary supervisor) and the United Nations and the World Bank [1]. Methodologically, the project will innovate training and deploying a machine learning model to identify anti- and pro-migration communities and will use mobile phone data to create a time-dependent measure of exposure to ethnic communities. Background Online social media platforms (OSMPs) play a pivotal role in shaping our society. It has become a main communication channel enabling social connections over distant locations across the world [2]. It has helped businesses to expand their geographical reach and launch mass scale marketing campaigns to promote their products, increasing sales and revenue [3]. At the same time, OSMPs have been under intense scrutiny, particularly during the Brexit referendum [4] and current COVID-19 pandemic [5]. OSMPs have enabled the engendering of new social processes, notably mass scale misinformation, bot farming, digital echo chambers [5], influencing our behaviours in the digital and physical world [6]. Online hate speech has been at the core of intense and polarised debate [7]. Despite growing public concern and calls for policy action, there is little empirical evidence on the ways in which hateful online content translates into real-life behaviour. At the same time, immigration is consistently identified as one of the most divisive social issues globally [8]. Racist and xenophobic content on immigration is prominent on social media. Xenophobic narratives on OSMPs have contributed to shaping migration policy and political outcomes [4]. Such narratives spread sentiments of hate, leading to more polarised societies [9] which can spill onto physical violence [1]. While prior research has used OSMP data to identify and characterise anti-immigrant narratives [10], less is known about the geographical and digital context within which they emerge. For instance, we know little about how time exposure to diverse urban environments relate to local patterns of digital anti-immigration content; how these patterns may vary across neighbourhood demographic and socio-economic features; and the extent to which echo chambers lead to the evolution of anti-migration communities on social media. To address these gaps, the project seeks to leverage on Twitter and GSP mobile phone data to identify online anti-immigration communities, and the digital and geographical contexts within which they occur; address representativeness biases in Twitter data; and assess the influence of anti-migration Twitter content on the occurrence of hate crime. Methodology The project will be divided into three stages (Ss) as outlined in Fig.1 mapping to the project objectives. Four key data sources will be used Twitter, GPS Huq, Census and hate crime data. Given ethical concerns, the data will be stored on password protected local server, accessed in a secure room and a