Author ORCID Identifier
Computer Sciences, Information Science
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 ( >80% ) 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.
A. Kazemi, A. Younus, M. Jeon, M. A. Qureshi and S. Caton, "InÉire: An Interpretable NLP Pipeline Summarizing Inclusive Policy Making Concerning Migrants in Ireland," in IEEE Access, vol. 11, pp. 88807-88823, 2023, doi: 10.1109/ACCESS.2023.3303105.
Irish Research Council
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