Friday, April 22, 3:00 p.m.
Center for Social Complexity
3rd Floor Research Hall

Talha Oz, PhD Student
Computational Social Science Program
Department of Computational and Data Sciences
George Mason University

Computational methods to greater understand xenophobia

ABSTRACT: In order to achieve a healthy multiculturalism and to enable smooth integration of newcomers, we need to understand how xenophobia (fear, intolerance and stereotyping towards minorities and immigrants) develops, and why it is increasing in the West. While reviewing the literature on xenophobia, I have come across several studies focusing on issues around this phenomenon, which adapt different computational social science approaches. In this talk, I would like to first briefly review them* and then propose and discuss new computational methods to greater understand xenophobia.

*: Hammond and Axelrod (2006) explain "the evolution of ethnocentrism" with a very simple agent-based model, in which they find out that in-group favoritism can emerge "with only minimal cognitive requirements and in the absence of [...] complex mechanisms". "On the coevolution of stereotype, culture, and social relationships: an agent-based model" Joseph et al. (2014) from CASOS develop a more complicated model to "explore how ethnocentric stereotypes affect intergroup relationships in a society". Finally, in his 2015 book, "Terrified: How anti-Muslim fringe organizations became mainstream" Chris Bail proposes an evolutionary theory of collective behavior and cultural change, and support his thesis with computational methods such as text mining and social network analysis to show how the U.S. society settled into a new status quo after 9/11 attacks.