A Framework to Understand Attitudes towards Immigration through Twitter
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 u...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/20/9689 |
| _version_ | 1797515380022837248 |
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| author | Yerka Freire-Vidal Eduardo Graells-Garrido Francisco Rowe |
| author_facet | Yerka Freire-Vidal Eduardo Graells-Garrido Francisco Rowe |
| author_sort | Yerka Freire-Vidal |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T06:44:40Z |
| format | Article |
| id | doaj.art-c3531481a9d14118b7b58436542293ea |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T06:44:40Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-c3531481a9d14118b7b58436542293ea2023-11-22T17:22:47ZengMDPI AGApplied Sciences2076-34172021-10-011120968910.3390/app11209689A Framework to Understand Attitudes towards Immigration through TwitterYerka Freire-Vidal0Eduardo Graells-Garrido1Francisco Rowe2Social Complexity Research Center, Universidad del Desarrollo, Las Condes, Santiago 7610658, ChileSocial Complexity Research Center, Universidad del Desarrollo, Las Condes, Santiago 7610658, ChileGeographic Data Science Lab, Department of Geography and Planning, University of Liverpool, Liverpool L69 7ZT, UKUnderstanding 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.https://www.mdpi.com/2076-3417/11/20/9689social network analysisattitude classificationpsycholinguistic analysispublic policymigration |
| spellingShingle | Yerka Freire-Vidal Eduardo Graells-Garrido Francisco Rowe A Framework to Understand Attitudes towards Immigration through Twitter Applied Sciences social network analysis attitude classification psycholinguistic analysis public policy migration |
| title | A Framework to Understand Attitudes towards Immigration through Twitter |
| title_full | A Framework to Understand Attitudes towards Immigration through Twitter |
| title_fullStr | A Framework to Understand Attitudes towards Immigration through Twitter |
| title_full_unstemmed | A Framework to Understand Attitudes towards Immigration through Twitter |
| title_short | A Framework to Understand Attitudes towards Immigration through Twitter |
| title_sort | framework to understand attitudes towards immigration through twitter |
| topic | social network analysis attitude classification psycholinguistic analysis public policy migration |
| url | https://www.mdpi.com/2076-3417/11/20/9689 |
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