InÉire: An Interpretable NLP Pipeline Summarizing Inclusive Policy Making Concerning Migrants in Ireland
Reaching marginal and other migrant communities to elicit their political views and opinions is a well-known challenge. Social media has enabled a certain amount of online activism and participation, especially in societies with abundant multicultural identities. However, it can be quite challenging...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10210388/ |
| _version_ | 1797736946520293376 |
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| author | Arefeh Kazemi Arjumand Younus Mingyeong Jeon M. Atif Qureshi Simon Caton |
| author_facet | Arefeh Kazemi Arjumand Younus Mingyeong Jeon M. Atif Qureshi Simon Caton |
| author_sort | Arefeh Kazemi |
| collection | DOAJ |
| description | Reaching marginal and other migrant communities to elicit their political views and opinions is a well-known challenge. Social media has enabled a certain amount of online activism and participation, especially in societies with abundant multicultural identities. However, it can be quite challenging to isolate the voice of the migrant in English-speaking countries, especially with an abundance of content in English on social media. In this paper, we pursue a case study of Ireland’s Twitter landscape, specifically migrant and native activists. We present a methodology that can accurately (<inline-formula> <tex-math notation="LaTeX">$>80\%$ </tex-math></inline-formula>) isolate the Irish migrant voice with as little as 25 English tweets without relying on user metadata and using simple, highly explainable, out-of-the-box machine learning methods. Using this, we distil (via sentiment analysis) polarities of views, segment (via BERT-based topic modelling) and summarise (via ChatGPT) differentiated views in a consumable manner for policymakers. Our approach enables policymakers to further their understanding of multicultural communities and use this to inform their decision-making processes. |
| first_indexed | 2024-03-12T13:21:16Z |
| format | Article |
| id | doaj.art-4b1e6bc98da442ec8fb8635cf6287bc5 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T13:21:16Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4b1e6bc98da442ec8fb8635cf6287bc52023-08-25T23:00:20ZengIEEEIEEE Access2169-35362023-01-0111888078882310.1109/ACCESS.2023.330310510210388InÉire: An Interpretable NLP Pipeline Summarizing Inclusive Policy Making Concerning Migrants in IrelandArefeh Kazemi0https://orcid.org/0000-0002-8643-9713Arjumand Younus1https://orcid.org/0000-0001-7748-2050Mingyeong Jeon2M. Atif Qureshi3https://orcid.org/0000-0003-4413-4476Simon Caton4https://orcid.org/0000-0001-9379-3879School of Computer Science, University College Dublin, Dublin 4, IrelandSchool of Sociology, University College Dublin, Dublin 4, IrelandADAPT Centre, eXplainable Analytics Group, Faculty of Business, Technological University Dublin, Dublin 2, IrelandADAPT Centre, eXplainable Analytics Group, Faculty of Business, Technological University Dublin, Dublin 2, IrelandSchool of Computer Science, University College Dublin, Dublin 4, IrelandReaching marginal and other migrant communities to elicit their political views and opinions is a well-known challenge. Social media has enabled a certain amount of online activism and participation, especially in societies with abundant multicultural identities. However, it can be quite challenging to isolate the voice of the migrant in English-speaking countries, especially with an abundance of content in English on social media. In this paper, we pursue a case study of Ireland’s Twitter landscape, specifically migrant and native activists. We present a methodology that can accurately (<inline-formula> <tex-math notation="LaTeX">$>80\%$ </tex-math></inline-formula>) isolate the Irish migrant voice with as little as 25 English tweets without relying on user metadata and using simple, highly explainable, out-of-the-box machine learning methods. Using this, we distil (via sentiment analysis) polarities of views, segment (via BERT-based topic modelling) and summarise (via ChatGPT) differentiated views in a consumable manner for policymakers. Our approach enables policymakers to further their understanding of multicultural communities and use this to inform their decision-making processes.https://ieeexplore.ieee.org/document/10210388/Natural language processingIrelandmigrantTwittersummarizationpolicy making |
| spellingShingle | Arefeh Kazemi Arjumand Younus Mingyeong Jeon M. Atif Qureshi Simon Caton InÉire: An Interpretable NLP Pipeline Summarizing Inclusive Policy Making Concerning Migrants in Ireland IEEE Access Natural language processing Ireland migrant summarization policy making |
| title | InÉire: An Interpretable NLP Pipeline Summarizing Inclusive Policy Making Concerning Migrants in Ireland |
| title_full | InÉire: An Interpretable NLP Pipeline Summarizing Inclusive Policy Making Concerning Migrants in Ireland |
| title_fullStr | InÉire: An Interpretable NLP Pipeline Summarizing Inclusive Policy Making Concerning Migrants in Ireland |
| title_full_unstemmed | InÉire: An Interpretable NLP Pipeline Summarizing Inclusive Policy Making Concerning Migrants in Ireland |
| title_short | InÉire: An Interpretable NLP Pipeline Summarizing Inclusive Policy Making Concerning Migrants in Ireland |
| title_sort | in x00c9 ire an interpretable nlp pipeline summarizing inclusive policy making concerning migrants in ireland |
| topic | Natural language processing Ireland migrant summarization policy making |
| url | https://ieeexplore.ieee.org/document/10210388/ |
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