Challenges of Social Cohesion of Immigrants
| dc.contributor.advisor | Muñoz Reyes, José Antonio | |
| dc.contributor.author | Freire Vidal, Yerka | |
| dc.coverage.spatial | Santiago | |
| dc.date.accessioned | 2024-07-08T21:38:10Z | |
| dc.date.available | 2024-07-08T21:38:10Z | |
| dc.date.issued | 2023 | |
| dc.description | Thesis submitted to the Faculty of Government of the Universidad Del Desarrollo for the academic degree of Doctor in Sciences of Social Complexity | |
| dc.description.abstract | Migration is a phenomenon that has acquired great relevance nowadays, and that can generate diverse attitudes in receiving societies. Many countries have fo cused on understanding and reducing discriminatory and xenophobic attitudes towards immigrants, since such attitudes make social cohesion and healthy coexistence impossible, as well as fostering the marginalization of immigrants. Existing data sources for analyzing attitudes towards immigration, mostly surveys and/or experimental designs of specific case studies, provide valuable information; how ever, they impose some difficulties as they require a lot of resources to implement and analyze; in terms of money, time and people. In addition, due to the temporal spacing with which these data are obtained, it is difficult to access the dynamics of the phenomenon, and therefore, it is only possible to obtain a partial picture of what is happening in a society that is changing rapidly and continuously. Today, social networks provide an opportunity to complement and overcome some of the limitations of the data collected through these traditional means. For example, Twitter not only serves as a public space for the exchange of opinions and ideas on various social issues, but also influences the opinions of its users. In this thesis work, three studies are presented that seek to infer and analyze attitudes towards immigration, taking Chilean society as a case study, and using Twitter data as a source of information. The first study presents a methodology based on topi cal analysis of Twitter data to measure, classify and characterize attitudes. The second and third studies, on the other hand, use an XGBoost classifier to infer attitudes towards immigration. In particular, the second study shows a compara tive pre- and post-pandemic analysis of COVID-19 to test one of the Behavioral Immune System (BIS) hypotheses; which indicates that in a pandemic context dis criminatory and xenophobic attitudes towards immigrants should be accentuated. Finally, the third study presents a methodological framework for the analysis of these attitudes from a perspective that characterizes the content, the psycholin guistic dimensions, and the dynamics associated with these attitudes. In general terms, we found that attitudes towards immigration seem to be influenced by news events related to migration. Furthermore, in the use of language, users with pos itive attitudes reveal greater empathy, while those with negative attitudes show a greater perception of threat, consistent with the social theories that explain the different attitudes. Finally, we find that negative attitude users are more vocifer ous, even as an effect of the Covid-19 pandemic, even though we find no robust evidence to support the BIS hypothesis. Our work presents novel methodologies to study attitudes towards immigration using social network data, and provides valuable information for the Chilean migration context. Our results could support the design of appropriate public and social policies that allow for an effective and peaceful integration of immigrants. | |
| dc.format.extent | 118 p., anexos | |
| dc.identifier.doi | https://doi.org/10.52611/11447/9173 | |
| dc.identifier.uri | https://hdl.handle.net/11447/9173 | |
| dc.language.iso | en | |
| dc.publisher | Universidad del Desarrollo. Facultad de Gobierno | |
| dc.subject | Human migration | |
| dc.subject | Attitudes | |
| dc.subject | 060005S | |
| dc.title | Challenges of Social Cohesion of Immigrants | |
| dc.type | Thesis | |
| dcterms.accessRights | Acceso abierto |