A Framework to Understand Attitudes towards Immigration through Twitter

Freire-Vidal, Yerka, Graells-Garrido, Eduardo and Rowe, Francisco ORCID: 0000-0003-4137-0246 (2021) A Framework to Understand Attitudes towards Immigration through Twitter. APPLIED SCIENCES-BASEL, 11 (20). p. 9689.

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Official URL: http://dx.doi.org/10.3390/app11209689

Abstract

<jats:p>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.</jats:p>

Item Type:	Article
Uncontrolled Keywords:	social network analysis, attitude classification, psycholinguistic analysis, public policy, migration
Divisions:	Faculty of Science and Engineering > School of Environmental Sciences
Depositing User:	Symplectic Admin
Date Deposited:	21 Dec 2021 15:47
Last Modified:	15 Mar 2024 12:52
DOI:	10.3390/app11209689
Open Access URL:	https://www.mdpi.com/2076-3417/11/20/9689
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3145806