Analyzing social media for measuring public attitudes toward controversies and their driving factors

A Case Study of Migration

Dados Bibliográficos

ID4658151
AutoresYiyi Chen (0009-0000-4437-9946, Karlsruhe Institute of Technology, autor correspondente), Harald Sack (0000-0001-7069-9804, Karlsruhe Institute of Technology), Mehwish Alam (0000-0002-7867-6612, Karlsruhe Institute of Technology)
Ano2022
Volume12
Fascículo1
Páginas135-135
Data de publicação2022-12-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoSocial Network Analysis and Mining (JOURNAL)
Identificadores do periódicoISSN: 1869-5450 • E-ISSN: 1869-5469
EditoraSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s13278-022-00915-7
PMID36105922
OpenAlexW4295900707
IdiomaEN
Citações recebidas4
Referências citadas40

Among other ways of expressing opinions on media such as blogs, and forums, social media (such as Twitter) has become one of the most widely used channels by populations for expressing their opinions. With an increasing interest in the topic of migration in Europe, it is important to process and analyze these opinions. To this end, this study aims at measuring the public attitudes toward migration in terms of sentiments and hate speech from a large number of tweets crawled on the decisive topic of migration. This study introduces a knowledge base (KB) of anonymized migration-related annotated tweets termed as (MGKB). The tweets from 2013 to July 2021 in the European countries that are hosts of immigrants are collected, pre-processed, and filtered using advanced topic modeling techniques. BERT-based entity linking and sentiment analysis, complemented by attention-based hate speech detection, are performed to annotate the curated tweets. Moreover, external databases are used to identify the potential social and economic factors causing negative public attitudes toward migration. The analysis aligns with the hypothesis that the countries with more migrants have fewer negative and hateful tweets. To further promote research in the interdisciplinary fields of social sciences and computer science, the outcomes are integrated into MGKB, which significantly extends the existing ontology to consider the public attitudes toward migrations and economic indicators. This study further discusses the use-cases and exploitation of MGKB. Finally, MGKB is made publicly available, fully supporting the FAIR principles

Data science · Epistemology · Immigration · Information retrieval · Ontology · Political science · Public opinion · Public relations · Sentiment analysis · Social media · Topic model · World Wide Web · Computer Science · Hate Speech and Cyberbullying Detection · Media Influence and Politics · Social Media and Politics · Artificial Intelligence