2022/06/17 by Keyu Chen, Marzieh Babaeianjelodar, Chen, Keyu +29 · 1 citation
Computer Science · Social Sciences · #Education and experiences of immigrants and refugees #FOS: Computer and information sciences #Hate Speech and Cyberbullying Detection #Social and Information Networks (cs.SI) #Terrorism, Counterterrorism, and Political Violence
paper · pdf · doi:10.48550/arxiv.2206.09024
openalex publication_date 2022/06/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We investigate how representations of Syrian refugees (2011-2021) differ across US partisan news outlets. We analyze 47,388 articles from the online US media about Syrian refugees to detail differences in reporting between left- and right-leaning media. We use various NLP techniques to understand these differences. Our polarization and question answering results indicated that left-leaning media tended to represent refugees as child victims, welcome in the US, and right-leaning media cast refugees as Islamic terrorists. We noted similar results with our sentiment and offensive speech scores over time, which detail possibly unfavorable representations of refugees in right-leaning media. A strength of our work is how the different techniques we have applied validate each other. Based on our results, we provide several recommendations. Stakeholders may utilize our findings to intervene around refugee representations, and design communications campaigns that improve the way society sees refugees and possibly aid refugee outcomes.