论文标题
仪式：阐明位移到Twitter上的难民的位置
Rites de Passage: Elucidating Displacement to Emplacement of Refugees on Twitter
论文作者
论文摘要
社交媒体的审议允许探索与难民有关的IS PAS。基于AI的研究主要围绕特定事件调查了难民问题，并考虑了单峰方法。相反，我们采用了多模式建筑来探测他们家到东道国的难民之旅。我们从Arnold Van Gennep的人类学工作“ Les Rites de Passage”中获取见解，该工作系统地分析了一个人从一个群体或社会向另一个群体或社会的过渡。根据Gennep的分离转变框架，我们确定了难民之旅的四个阶段：难民的到来，暂时的住宿，庇护，康复以及难民融入到东道国。从2020年4月至2021年3月，我们收集了203万个多模式推文，用于测试这一拟议的框架工作。我们发现，基于变压器的语言模型和最先进的图像识别模型（例如BERT+LSTM和INCEPTIONV4的融合）的组合可以超过表演的单峰模型。随后，为了实时测试我们提议的模型的实际含义，我们考虑了010万个多模式推文与2022年乌克兰难民危机有关。在这场2022年的危机中，F1得分为71.88％，证实了我们提议的框架的普遍性。
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.
