InÉire: An Interpretable NLP Pipeline Summarizing Inclusive Policy Making Concerning Migrants in Ireland

Abstract

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.

Keywords

• Natural language processing
• Ireland
• migrant
• Twitter
• summarization
• policy making