Proximity and Networked Responsibility Framing of Anti-Muslim Crime News: Structural Topic Modeling of Twitter Conversations on the Quebec Shooting in 2017

• In Event: When Media Meet Computational Methods

Abstract

Anti-Muslim hate crimes have become a serious concern in many western societies. The current study investigates the ways in which local anti-Muslim crime news transforms into a globalized public agenda. To expand the boundary of crime news audience to a global scope, the study used unsupervised machine learning, i.e., structural topic modeling, and analyzed globally generated, large-scale Twitter conversations surrounding the Quebec mosque shooting in 2017. The results uncovered nine multi-news frame clusters, together reflective of complex processes of responsibility attributions. Moreover, global geographic, cultural, and source proximity influenced the prevalence of these frames. Considering potential resonance of Islamophobia across different societies, local crime news, which are seemingly an isolated incident at a single place, can have ripple effects on faraway global audience’s political lives in their own places.

Authors

• K. Hazel Kwon, Arizona State U

• Monica Chadha, Arizona State U

• Feng` Wang, Arizona State U