Rites de Passage: Elucidating Displacement to Emplacement of Refugees on Twitter

TL;DR

This paper introduces a multimodal framework using transformer-based language models and image recognition to analyze refugee journeys on Twitter, inspired by anthropological rites of passage, demonstrating effective classification across different refugee phases and crises.

Contribution

It proposes a novel multimodal approach grounded in anthropological theory to analyze refugee experiences on social media, outperforming unimodal models and validating on real-time crisis data.

Findings

Multimodal models outperform unimodal models in classifying refugee phases.

The framework achieves an F1-score of 71.88% on Ukrainian refugee tweets.

The approach generalizes well across different refugee crises.

Abstract

Social media deliberations allow to explore refugee-related is-sues. AI-based studies have investigated refugee issues mostly around a specific event and considered unimodal approaches. Contrarily, we have employed a multimodal architecture for probing the refugee journeys from their home to host nations. We draw insights from Arnold van Gennep's anthropological work 'Les Rites de Passage', which systematically analyzed an individual's transition from one group or society to another. Based on Gennep's separation-transition-incorporation framework, we have identified four phases of refugee journeys: Arrival of Refugees, Temporal stay at Asylums, Rehabilitation, and Integration of Refugees into the host nation. We collected 0.23 million multimodal tweets from April 2020 to March 2021 for testing this proposed frame-work. We find that a combination of transformer-based language models and state-of-the-art image recognition models, such as fusion of BERT+LSTM and InceptionV4, can out-perform unimodal models. Subsequently, to test the practical implication of our proposed model in real-time, we have considered 0.01 million multimodal tweets related to the 2022 Ukrainian refugee crisis. An F1-score of 71.88 % for this 2022 crisis confirms the generalizability of our proposed framework.