Unmasking Public Perception: A Mixed-Methods Exploration of Social Media Discourse on U.S. Illegal Immigration

Research output: Contribution to book or proceedingConference articlepeer-review

Abstract

With illegal immigration remaining a contentious issue in the United States, understanding public perception - particularly as expressed on social media - is vital for informed policymaking and advocacy. This study investigates the discourse surrounding illegal immigration in the U.S. through both quantitative and qualitative analyses of data from Reddit. We identify key themes and perspectives on four critical aspects: (i) U.S. policy toward illegal immigration, (ii) strategies for mitigating illegal immigration, (iii) issues arising from illegal immigration, and (iv) hostile rhetoric targeting undocumented immigrants. Additionally, we conduct emotion analysis to ascertain prevalent emotional responses within the discourse. Our findings reveal both challenges and perceived benefits associated with lenient border policies, including difficulties in monitoring extensive border areas and the exploitation of low-wage labor. We observe a wide range of suggested measures to reduce illegal migration, including stringent actions against employers of undocumented workers, proposals to abolish birthright citizenship, and calls for increased legal migration. Emotion analysis indicates significant levels of anger and disgust expressed in the comments. Furthermore, we document instances of anti-immigrant rhetoric, some of which are inhumane and include calls for violence against undocumented individuals. This research provides valuable insights into societal attitudes toward illegal immigration, informing policymakers and advocacy groups in their decision-making.

Original languageEnglish
Title of host publicationProceedings - 2024 IEEE International Conference on Big Data, BigData 2024
EditorsWei Ding, Chang-Tien Lu, Fusheng Wang, Liping Di, Kesheng Wu, Jun Huan, Raghu Nambiar, Jundong Li, Filip Ilievski, Ricardo Baeza-Yates, Xiaohua Hu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages7151-7160
Number of pages10
ISBN (Electronic)9798350362480
DOIs
StatePublished - 2024
Event2024 IEEE International Conference on Big Data, BigData 2024 - Washington, United States
Duration: Dec 15 2024Dec 18 2024

Publication series

NameProceedings - 2024 IEEE International Conference on Big Data, BigData 2024
ISSN (Print)2639-1589
ISSN (Electronic)2573-2978

Conference

Conference2024 IEEE International Conference on Big Data, BigData 2024
Country/TerritoryUnited States
CityWashington
Period12/15/2412/18/24

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 10 - Reduced Inequalities
    SDG 10 Reduced Inequalities
  2. SDG 16 - Peace, Justice and Strong Institutions
    SDG 16 Peace, Justice and Strong Institutions

Scopus Subject Areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Modeling and Simulation

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