# Characterization of Local Attitudes Toward Immigration Using Social Media

## TL;DR

This study uses Twitter data and semi-supervised topic modeling to analyze local attitudes toward immigration in Chile, revealing emotional, lexical, and network patterns associated with pro- and anti-immigration sentiments.

## Contribution

It introduces a scalable method leveraging social media to characterize attitudes toward immigration, focusing on emotions, language, and network structure.

## Key findings

- Discussion driven mainly by Haitian immigration
- Temporal trends in attitude polarity observed
- Network behavior varies with attitude type

## Abstract

Migration is a worldwide phenomenon that may generate different reactions in the population. Attitudes vary from those that support multiculturalism and communion between locals and foreigners, to contempt and hatred toward immigrants. Since anti-immigration attitudes are often materialized in acts of violence and discrimination, it is important to identify factors that characterize these attitudes. However, doing so is expensive and impractical, as traditional methods require enormous efforts to collect data. In this paper, we propose to leverage Twitter to characterize local attitudes toward immigration, with a case study on Chile, where immigrant population has drastically increased in recent years. Using semi-supervised topic modeling, we situated 49K users into a spectrum ranging from in-favor to against immigration. We characterized both sides of the spectrum in two aspects: the emotions and lexical categories relevant for each attitude, and the discussion network structure. We found that the discussion is mostly driven by Haitian immigration; that there are temporal trends in tendency and polarity of discussion; and that assortative behavior on the network differs with respect to attitude. These insights may inform policy makers on how people feel with respect to migration, with potential implications on communication of policy and the design of interventions to improve inter-group relations.