MigrationsKB: A Knowledge Base of Public Attitudes towards Migrations and their Driving Factors
Yiyi Chen, Harald Sack, Mehwish Alam

TL;DR
This paper presents MigrationsKB, a comprehensive knowledge base that analyzes social media data to understand public attitudes towards migration in Europe, incorporating sentiment, hate speech, and socio-economic factors.
Contribution
It introduces a novel interdisciplinary knowledge base integrating social media analysis with socio-economic data to study migration attitudes.
Findings
Identified key social and economic factors influencing migration attitudes.
Developed a publicly accessible, FAIR-compliant knowledge base.
Applied advanced NLP techniques for sentiment and hate speech detection.
Abstract
With the increasing trend in the topic of migration in Europe, the public is now more engaged in expressing their opinions through various platforms such as Twitter. Understanding the online discourses is therefore essential to capture the public opinion. The goal of this study is the analysis of social media platform to quantify public attitudes towards migrations and the identification of different factors causing these attitudes. The tweets spanning from 2013 to Jul-2021 in the European countries which are hosts to immigrants are collected, pre-processed, and filtered using advanced topic modeling technique. BERT-based entity linking and sentiment analysis, and attention-based hate speech detection are performed to annotate the curated tweets. Moreover, the external databases are used to identify the potential social and economic factors causing negative attitudes of the people about…
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Taxonomy
TopicsMigration, Refugees, and Integration
