A Framework to Understand Attitudes towards Immigration through Twitter
| Autor: | Eduardo Graells-Garrido, Francisco Rowe, Yerka Freire-Vidal |
|---|---|
| Přispěvatelé: | Barcelona Supercomputing Center |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Social psychology (sociology)
Technology social network analysis Public policy QH301-705.5 media_common.quotation_subject public policy QC1-999 Immigration Psychological intervention Sample (statistics) attitude classification Psycholinguistic analysis Public opinion migration Social media Social network analysis Competition (economics) Machine learning Attitude classification General Materials Science Informàtica::Aspectes socials [Àrees temàtiques de la UPC] Sociology psycholinguistic analysis Biology (General) Instrumentation QD1-999 Migration media_common Fluid Flow and Transfer Processes business.industry Process Chemistry and Technology Physics General Engineering Mitjans de comunicació social Engineering (General). Civil engineering (General) Computer Science Applications Chemistry TA1-2040 business Social psychology |
| Zdroj: | Applied Sciences, Vol 11, Iss 9689, p 9689 (2021) UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) Applied Sciences Volume 11 Issue 20 |
| ISSN: | 2076-3417 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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