An Analysis of Online Twitter Sentiment Surrounding the European Refugee Crisis

Pope, David Watts; Griffith, Josephine

doi:10.5220/0006051902990306

Cited by 16 publications

(12 citation statements)

References 6 publications

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“…Those approaches also can be reflected in our future direction of data construction. There are LIWC analysis studies on the text (Pope and Griffith, 2016;Fast et al, 2016). Previous research could be one of our options to enhance our approach.…”

Section: Discussion and Positive Impact Of This Studymentioning

confidence: 99%

Empirical Analysis of Korean Public AI Hub Parallel Corpora and in-depth Analysis using LIWC

Park¹,

Shim²,

Eo³

et al. 2021

Preprint

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Machine translation (MT) system aims to translate source language into target language. Recent studies on MT systems mainly focus on neural machine translation (NMT). One factor that significantly affects the performance of NMT is the availability of high-quality parallel corpora. However, high-quality parallel corpora concerning Korean are relatively scarce compared to those associated with other highresource languages, such as German or Italian. To address this problem, AI Hub recently released seven types of parallel corpora for Korean. In this study, we conduct an in-depth verification of the quality of corresponding parallel corpora through Linguistic Inquiry and Word Count (LIWC) and several relevant experiments. LIWC is a word-counting software program that can analyze corpora in multiple ways and extract linguistic features as a dictionary base. To the best of our knowledge, this study is the first to use LIWC to analyze parallel corpora in the field of NMT. Our findings suggest the direction of further research toward obtaining the improved quality parallel corpora through our correlation analysis in LIWC and NMT performance.

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Section: Discussion and Positive Impact Of This Studymentioning

confidence: 99%

Empirical Analysis of Korean Public AI Hub Parallel Corpora and in-depth Analysis using LIWC

Park¹,

Shim²,

Eo³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The results of sentiment analysis of the refugee crisis in Germany, Austria, and Switzerland between October 2015 and March 2016 indicated an increase in negative content in old and new media, which was slightly more prevalent on social media (Backfried and Shalunts, 2016). An analysis of English and German tweets also confirmed an increase in the anxiety level among refugees after November 2015 (Pope and Griffith, 2017).…”

Section: Introductionmentioning

confidence: 95%

Religious memory on Facebook in times of refugee crisis

Guzek

2019

Social Compass

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The aim of this article is to analyze the religious discourse on Polish Facebook in relation to the refugee crisis. The primary focus is on how religious memory is presented. A discourse analysis implemented by the author helps to identify and examine certain nodal points related to the religious debate about refugees, and then instruments used to recall religious memory are shown. In the course of the analysis, the author uses the theory of religion as a chain of memory. As a result, a complex attitude of Polish users toward the Pope, Islam, Christian martyrdom and religious buildings is shown. All of them refer to religious traces from the past.

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“…A plethora of studies probed refugee-related issues using microblogging data, such as Twitter (Adler-Nissen et al 2020;Kreis 2017;Pope & Griffith 2016;Khatua & Nejdl, 2021a;2021b). While the relevance of multimodal content for extracting relevant and actionable information was widely acknowledged (Alam et al 2018;Gui et al 2019), refugee-related studies mostly focused on syntactic text processing (Kreis 2017;Pope & Griffith 2016;Siapera et al 2018) and did not probe the richness of multimodal data. A handful of studies also explored the emotional responses to refugee-related visual contents (Ibrahim 2018;Olesen 2018).…”

Section: Introductionmentioning

confidence: 99%

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

Khatua

Nejdl

2022

Proceedings of the 33rd ACM Conference on Hypertext and Social Media

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Social media deliberations allow to explore refugee-related issues. 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-transitionincorporation 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 framework. 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 outperform 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.

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An Analysis of Online Twitter Sentiment Surrounding the European Refugee Crisis

Cited by 16 publications

References 6 publications

Empirical Analysis of Korean Public AI Hub Parallel Corpora and in-depth Analysis using LIWC

Empirical Analysis of Korean Public AI Hub Parallel Corpora and in-depth Analysis using LIWC

Religious memory on Facebook in times of refugee crisis

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

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