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Analyzing European Migrant-related Twitter Deliberations

Published: 03 June 2021 Publication History

Abstract

Machine-driven topic identification of online contents is a prevalent task in the natural language processing (NLP) domain. Social media deliberation reflects society's opinion, and a structured analysis of these contents allows us to decipher the same. We employ an NLP-based approach for investigating migration-related Twitter discussions. Besides traditional deep learning-based models, we have also considered pre-trained transformer-based models for analyzing our corpus. We have successfully classified multiple strands of public opinion related to European migrants. Finally, we use 'BertViz' to visually explore the interpretability of better performing transformer-based models.

References

[1]
Alcántara-Plá, M. and Ruiz-Sánchez, A. 2018. Not for Twitter: Migration as a Silenced Topic in the 2015 Spanish General Election. Exploring Silence and Absence in Discourse. M. Schröter and C. Taylor, eds. Springer International Publishing. 25–64.
[2]
Aswad, F. and Menezes, R. Refugee and Immigration: Twitter as a Proxy for Reality. 6.
[3]
Calderón, C.A. 2020. Topic Modeling and Characterization of Hate Speech against Immigrants on Twitter around the Emergence of a Far-Right Party in Spain. Social Sciences. 9, 11 (Oct. 2020), 188.
[4]
Capozzi, A. 2020. Facebook Ads: Politics of Migration in Italy. In: Aref S. (eds) Social Informatics. SocInfo 2020. Lecture Notes in Computer Science, vol 12467. Springer, Cham. https://doi.org/10.1007/978-3-030-60975-7_4.
[5]
Conneau, A. 2017. Very Deep Convolutional Networks for Text Classification. Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers (Valencia, Spain, 2017), 1107–1116.
[6]
Devlin, J. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv:1810.04805 [cs]. (May 2019).
[7]
Dilrukshi, I. and Zoysa, K. de 2014. A Feature Selection Method for Twitter News Classification. International Journal of Machine Learning and Computing. 4, 4 (2014), 365–370.
[8]
Gualda, E. and Rebollo, C. 2016. The Refugee Crisis On Twitter: A Diversity Of Discourses At A European Crossroads. 3 (2016), 14.
[9]
Guidry, J.P.D. 2018. Welcome or Not: Comparing #Refugee Posts on Instagram and Pinterest. American Behavioral Scientist. 62, 4 (Apr. 2018), 512–531.
[10]
Hadgu, A.T. 2016. Welcome or Not-Welcome: Reactions to Refugee Situation on Social Media. arXiv:1610.02358 [cs]. (Oct. 2016).
[11]
Hrdina, M. 2016. Identity, Activism and Hatred: Hate Speech against Migrants on Facebook in the Czech Republic in 2015. Naše společnost. 1, 14 (2016), 38.
[12]
Kalchbrenner, N. 2014. A Convolutional Neural Network for Modelling Sentences. arXiv:1404.2188 [cs]. (Apr. 2014).
[13]
Kreis, R. 2017. #refugeesnotwelcome: Anti-refugee discourse on Twitter. Discourse & Communication. 11, 5 (Oct. 2017), 498–514.
[14]
Lee, J.-S. and Nerghes, A. 2018. Refugee or Migrant Crisis? Labels, Perceived Agency, and Sentiment Polarity in Online Discussions. Social Media + Society. 4, 3 (Jul. 2018), 205630511878563.
[15]
Liu, Y. 2019. RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv:1907.11692 [cs]. (Jul. 2019).
[16]
Llion, J. 2017. Tensor2tensor transformer visualization. https://github.com/ tensorflow/tensor2tensor/tree/ master/tensor2tensor/visualization.
[17]
McAuliffe, M., James 2019. World migration report 2020.
[18]
Nerghes, A. and Lee, J.-S. 2019. Narratives of the Refugee Crisis: A Comparative Study of Mainstream-Media and Twitter. Media and Communication. 7, 2 (Jun. 2019), 275–288.
[19]
Nerghes, A. and Lee, J.-S. 2018. The Refugee/Migrant Crisis Dichotomy on Twitter: A Network and Sentiment Perspective. Proceedings of the 10th ACM Conference on Web Science (Amsterdam Netherlands, May 2018), 271–280.
[20]
Özerim, M.G. and Tolay, J. 2020. Discussing the populist features of anti-refugee discourses on social media: an anti-Syrian hashtag in Turkish Twitter. Journal of Refugee Studies. (May 2020), feaa022.
[21]
Pope, D. and Griffith, J. 2016. An Analysis of Online Twitter Sentiment Surrounding the European Refugee Crisis: Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (Porto, Portugal, 2016), 299–306.
[22]
Rettberg, J.W. and Gajjala, R. 2016. Terrorists or cowards: negative portrayals of male Syrian refugees in social media. Feminist Media Studies. 16, 1 (Jan. 2016), 178–181.
[23]
Sajir, Z. 2019. Solidarity, Social Media, and the “Refugee Crisis”: Engagement Beyond Affect. (2019), 28.
[24]
Siapera, E. 2018. Refugees and Network Publics on Twitter: Networked Framing, Affect, and Capture. Social Media + Society. 4, 1 (Jan. 2018), 205630511876443.
[25]
Urchs, S. 2019. MMoveT15: A Twitter Dataset for Extracting and Analysing Migration-Movement Data of the European Migration Crisis 2015. 2019 IEEE 28th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE) (Napoli, Italy, Jun. 2019), 146–149.
[26]
Vig, J. 2019. A Multiscale Visualization of Attention in the Transformer Model. arXiv:1906.05714 [cs]. (Jun. 2019).
[27]
Vig, J. and Belinkov, Y. 2019. Analyzing the Structure of Attention in a Transformer Language Model. arXiv:1906.04284 [cs, stat]. (Jun. 2019).
[28]
Wolf, T. 2020. HuggingFace's Transformers: State-of-the-art Natural Language Processing. arXiv:1910.03771 [cs]. (Jul. 2020).
[29]
Young, T. Recent Trends in Deep Learning Based Natural Language Processing. 23.
[30]
Zagheni, E. 2014. Inferring international and internal migration patterns from Twitter data. Proceedings of the 23rd International Conference on World Wide Web - WWW ’14 Companion (Seoul, Korea, 2014), 439–444.

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cover image ACM Conferences
WWW '21: Companion Proceedings of the Web Conference 2021
April 2021
726 pages
ISBN:9781450383134
DOI:10.1145/3442442
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 03 June 2021

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Author Tags

  1. BERT
  2. BertViz
  3. Migration
  4. RoBERTa
  5. Twitter

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WWW '21
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WWW '21: The Web Conference 2021
April 19 - 23, 2021
Ljubljana, Slovenia

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