Next Article in Journal
Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera
Previous Article in Journal
Evaluating the Checklist for Artificial Intelligence in Medical Imaging (CLAIM)-Based Quality of Reports Using Convolutional Neural Network for Odontogenic Cyst and Tumor Detection
Previous Article in Special Issue
Enhancement of Text Analysis Using Context-Aware Normalization of Social Media Informal Text
Article

A Framework to Understand Attitudes towards Immigration through Twitter

1
Social Complexity Research Center, Universidad del Desarrollo, Las Condes, Santiago 7610658, Chile
2
Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain
3
Geographic Data Science Lab, Department of Geography and Planning, University of Liverpool, Liverpool L69 7ZT, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Carlos A. Iglesias and Antonio Moreno
Appl. Sci. 2021, 11(20), 9689; https://doi.org/10.3390/app11209689
Received: 23 July 2021 / Revised: 28 August 2021 / Accepted: 17 September 2021 / Published: 18 October 2021
(This article belongs to the Special Issue Sentiment Analysis for Social Media Ⅱ)
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. View Full-Text
Keywords: social network analysis; attitude classification; psycholinguistic analysis; public policy; migration social network analysis; attitude classification; psycholinguistic analysis; public policy; migration
Show Figures

Figure 1

MDPI and ACS Style

Freire-Vidal, Y.; Graells-Garrido, E.; Rowe, F. A Framework to Understand Attitudes towards Immigration through Twitter. Appl. Sci. 2021, 11, 9689. https://doi.org/10.3390/app11209689

AMA Style

Freire-Vidal Y, Graells-Garrido E, Rowe F. A Framework to Understand Attitudes towards Immigration through Twitter. Applied Sciences. 2021; 11(20):9689. https://doi.org/10.3390/app11209689

Chicago/Turabian Style

Freire-Vidal, Yerka, Eduardo Graells-Garrido, and Francisco Rowe. 2021. "A Framework to Understand Attitudes towards Immigration through Twitter" Applied Sciences 11, no. 20: 9689. https://doi.org/10.3390/app11209689

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop