Abstract
Understanding public opinion towards immigrants is key to prevent acts of violence, discrimination and abuse. Traditional data sources, such as surveys, provide rich insights into the formation of such attitudes; yet, they are costly and offer limited temporal granularity, providing only a partial understanding of the dynamics of attitudes towards immigrants. Leveraging Twitter data and natural language processing, we propose a framework to measure attitudes towards immigration in online discussions. Grounded in theories of social psychology, the proposed framework enables the classification of users’ into profile stances of positive and negative attitudes towards immigrants and characterisation of these profiles quantitatively summarising users’ content and temporal stance trends. We use a Twitter sample composed of 36 K users and 160 K tweets discussing the topic in 2017, when the immigrant population in the country recorded an increase by a factor of four from 2010. We found that the negative attitude group of users is smaller than the positive group, and that both attitudes have different distributions of the volume of content. Both types of attitudes show fluctuations over time that seem to be influenced by news events related to immigration. Accounts with negative attitudes use arguments of labour competition and stricter regulation of immigration. In contrast, accounts with positive attitudes reflect arguments in support of immigrants’ human and civil rights. The framework and its application can inform policy makers about how people feel about immigration, with possible implications for policy communication and the design of interventions to improve negative attitudes.
Highlights
International migration has emerged as a major divisive global political and societal issue during the 21st Century, with increasing expressions of anti-migration sentiment [1].Immigration has been portrayed as a major threat to social cohesion, notably during the UKBrexit Referendum and Trump presidential campaign, and drawn attention towards more restrictive migration policies, in Western European countries and the UnitedStates [2,3,4]
Such content has the potential to cause harm to individuals. It often translates into social tension outside the digital world and has played a role in the spread of hate speech during the COVID19 pandemic [14]. With this context in mind, we aim to answer the following research questions: (RQ1) Can we identify, quantify and classify attitudes towards immigration from social network data? (RQ2) What characteristics differentiate the content emitted by users with different attitudes? (RQ3)
We describe the potential of the social network Twitter in this regard
Summary
International migration has emerged as a major divisive global political and societal issue during the 21st Century, with increasing expressions of anti-migration sentiment [1].Immigration has been portrayed as a major threat to social cohesion, notably during the UKBrexit Referendum and Trump presidential campaign, and drawn attention towards more restrictive migration policies, in Western European countries and the UnitedStates [2,3,4]. International migration has emerged as a major divisive global political and societal issue during the 21st Century, with increasing expressions of anti-migration sentiment [1]. Global Compact for Safe, Orderly and Regular Migration (goals 16 and 17) [9] to tackle anti-immigrant behaviour and facilitate migration integration. The anti-migration sentiment is generally shaped by misconception [1], and social media has become a key channel to spread misinformation, contributing to the formation of misconceptions and manifestation of discriminatory acts against immigrants [10]. Two theoretical models are often used to describe the formation of attitudes towards immigration. These are the Intergroup Contact Theory (ICT) [28] and the Integrated Threat. ICT explains how positive attitudes form, while ITT describes how negative attitudes are created
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