Journal of Social Computing 2022, 3(2): 139-157 https://doi.org/10.23919/JSC.2022.0003

Open Access | Issue | Published: 01 June 2022

Measuring Social Solidarity During Crisis: The Role of Design Choices

Show Author's Information Hide Author's Information Steffen Eger^¹(

), Dan Liu^², Daniela Grunow^³

1 Center for Cognitive Interaction Technology (CITEC), Bielefeld University, Bielefeld 33615, Germany

2 Computer Science Department, Technical University Darmstadt, Darmstadt 64289, Germany

3 Faculty of Social Sciences, Goethe University Frankfurt, Frankfurt 60629, Germany

Keywords:

COVID-19, natural language processing, social solidarity, crises

Cite this article:

Eger S, Liu D, Grunow D. Measuring Social Solidarity During Crisis: The Role of Design Choices. Journal of Social Computing, 2022, 3(2): 139-157. https://doi.org/10.23919/JSC.2022.0003

Download citation

EndNote(RIS)

BibTeX

631

Views

Downloads

Citations

Crossref

N/A

WoS

Scopus

N/A

CSCD

Abstract Full text About this article

Abstract

Building on our previous work, we assess how social solidarity towards migrants and refugees has changed before and after the onset of the COVID-19 pandemic, by collecting and analyzing a large, novel, and longitudinal dataset of migration-related tweets. To this end, we first annotate above 2000 tweets for (anti-) solidarity expressions towards immigrants, utilizing two annotation approaches (experts vs. crowds). On these annotations, we train a BERT model with multiple data augmentation strategies, which performs close to the human upper bound. We use this high-quality model to automatically label over 240 000 tweets between September 2019 and June 2021. We then assess the automatically labeled data for how statements related to migrant (anti-)solidarity developed over time, before and during the COVID-19 crisis. Our findings show that migrant solidarity became increasingly salient and contested during the early stages of the pandemic but declined in importance since late 2020, with tweet numbers falling slightly below pre-pandemic levels in summer 2021. During the same period, the share of anti-solidarity tweets increased in a sub-sample of COVID-19-related tweets. These findings highlight the importance of long-term observation, pre- and post-crisis comparison, and sampling in research interested in crisis related effects. As one of our main contributions, we outline potential pitfalls of an analysis of social solidarity trends: for example, the ratio of solidarity and anti-solidarity statements depends on the sampling design, i.e., tweet language, Twitter-user accounts’ national identification (country known or unknown) and selection of relevant tweets. In our sample, the share of anti-solidarity tweets is higher in native (German) language tweets and among “anonymous” Twitter users writing in German compared to English-language tweets of users located in Germany.

Full text

Abstract

Full text

Outline

About this article

Measuring Social Solidarity During Crisis: The Role of Design Choices

Show Author's information Hide Author's Information Steffen Eger^¹(

), Dan Liu^², Daniela Grunow^³

1 Center for Cognitive Interaction Technology (CITEC), Bielefeld University, Bielefeld 33615, Germany

2 Computer Science Department, Technical University Darmstadt, Darmstadt 64289, Germany

3 Faculty of Social Sciences, Goethe University Frankfurt, Frankfurt 60629, Germany

Abstract

Keywords: COVID-19, natural language processing, social solidarity, crises

References(63)

N. Fenton, Mediating solidarity, Global Media and Communication, vol. 4, no. 1, pp. 37–57, 2008.

DOI Google Scholar

D. Margolin and W. Liao, The emotional antecedents of solidarity in social media crowds, New Media&Society, vol. 20, no. 10, pp. 3700–3719, 2018.

DOI Google Scholar

S. Santhanam, V. Srinivasan, S. Glass, and S. Shaikh, I stand with you: Using emojis to study solidarity in crisis events, arXiv preprint arXiv: 1907.08326, 2019.

Z. Tufekci, Social movements and governments in the digital age: Evaluating a complex landscape, Journal of International Affairs, vol. 68, no. 1, pp. 1–18, 2014.

DOI Google Scholar

H. Silver, Social exclusion and social solidarity: Three paradigms, International Labour Review, vol. 133, nos. 5&6, pp. 531–578, 1994.

D. Clarke, Pro-Social and Anti-Social Behaviour. New York, NY, USA: Routledge, Taylor & Francis Group, 2003.https://doi.org/10.4324/9780203414118

DOI

M. Mohler-Kuo, S. Dzemaili, S. Foster, L. Werlen, and S. Walitza, Stress and mental health among children/adolescents, their parents, and young adults during the first COVID-19 lockdown in Switzerland, International Journal of Environmental Research and Public Health, vol. 18, no. 9, p. 4668, 2021.

DOI Google Scholar

sCAN project, Hate speech trends during the COVID-19 pandemic in a digital and globalized age, https://scan-project.eu/resources-and-publications/#Covid-19, 2021.

M. L. Williams, P. Burnap, A. Javed, H. Liu, and S. Ozalp, Hate in the machine: Anti-black and anti-muslim social media posts as predictors of offline racially and religiously aggravated crime, The British Journal of Criminology, vol. 60, no. 1, pp. 93–117, 2020.

DOI Google Scholar

C. R. Seiter and N. S. Brophy, Social support and aggressive communication on social network sites during the COVID-19 pandemic, Health communication, doi: 10.1080/10410236.2021.1886399.https://doi.org/10.1080/10410236.2021.1886399

DOI

J. B. Vieira, S. Pierzchajlo, S. Jangard, A. Marsh, and A. Olsson, Perceived threat and acute anxiety predict increased everyday altruism during the COVID-19 pandemic, http://doi.org/10.31234/osf.io/n3t5c, 2020.https://doi.org/10.31234/osf.io/n3t5c

DOI

A. Stechemesser, L. Wenz, and A. Levermann, Corona crisis fuels racially profiled hate in social media networks, EClinicalMedicine, doi: 10.1016/j.eclinm.2020.100372.https://doi.org/10.1016/j.eclinm.2020.100372

DOI

M. Comerford and L. Gerster, The rise of antisemitism online during the pandemic. A study of French and German content, Publications Office, https://op.europa.eu/en/publication-detail/-/publication/d73c833f-c34c-11eb-a925-01aa75ed71a1, 2021.

O. Ozduzen and U. Korkut, Post-‘refugee crisis’ social media: The unbearable lightness of sharing racist posts, Discover Society, https://archive.discoversociety.org/2020/09/02/post-refugee-crisis-social-media-the-unbearable-lightness-of-sharing-racist-posts/, 2020.

J. Adam-Troian and S. C. Bagci, The pathogen paradox: Evidence that perceived COVID-19 threat is associated with both pro-and antiimmigrant attitudes, International Review of Social Psychology, vol. 34, no. 1, pp. 1–15, 2021.

DOI Google Scholar

S. Masud, S. Dutta, S. Makkar, C. Jain, V. Goyal, A. Das, and T. Chakraborty, Hate is the new infodemic: A topic-aware modeling of hate speech diffusion on twitter, in Proc. 2021 IEEE 37^th International Conference on Data Engineering (ICDE), Chania, Greece, 2021, pp. 504–515.https://doi.org/10.1109/ICDE51399.2021.00050

DOI

M. R. Awal, R. Cao, S. Mitrovic, and R. K. -W. Lee, On analyzing antisocial behaviors amid COVID-19 pandemic, arXiv preprint arXiv: 2007.10712, 2020.

A. Ils, D. Liu, D. Grunow, and S. Eger, Changes in European solidarity before and during COVID-19: Evidence from a large crowd- and expert-annotated Twitter dataset, in Proc. 59^th Annual Meeting of the Association for Computational Linguistics and the 11^th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Online, 2021, pp. 1623–1637.https://doi.org/10.18653/v1/2021.acl-long.129

DOI

L. Guadagno, Migrants and the COVID-19 pandemic: An initial analysis, https://publications.iom.int/system/files/pdf/mrs-60.pdf, 2020.

S. Baglioni, O. Biosca, and T. Montgomery, Brexit, division, and individual solidarity: What future for Europe? Evidence from eight European countries, American Behavioral Scientist, vol. 63, no. 4, pp. 538–550, 2019.

DOI Google Scholar

J. Gerhards, H. Lengfeld, Z. S. Ignácz, F. K. Kley, and M. Priem, European Solidarity in Times of Crisis: Insights from a Thirteen-Country Survey. London, UK: Routledge, 2019.https://doi.org/10.4324/9780429289453

DOI

S. Koos and V. Seibel, Solidarity with refugees across Europe. A comparative analysis of public support for helping forced migrants, European Societies, vol. 21, no. 5, pp. 704–728, 2019.

DOI Google Scholar

C. Lahusen and M. T. Grasso, eds., Solidarity in Europe: Citizens’ Responses in Times of Crisis. Cham, Switzerland: Springer International Publishing, 2018.https://doi.org/10.1007/978-3-319-73335-7

DOI

M. Franceschelli, Global migration, local communities and the absent state: Resentment and resignation on the Italian island of Lampedusa, Sociology, vol. 54, no. 3, pp. 591–608, 2019.

DOI Google Scholar

M. Gómez Garrido, M. A. Carbonero Gamundí, and A. Viladrich, The role of grassroots food banks in building political solidarity with vulnerable people, European Societies, vol. 21, no. 5, pp. 753–773, 2018.

DOI Google Scholar

C. Heimann, S. Müller, H. Schammann, and J. Stürner, Challenging the nation-state from within: The emergence of transmunicipal solidarity in the course of the EU refugee controversy, Social Inclusion, vol. 7, no. 2, pp. 208–218, 2019.

DOI Google Scholar

A. Nerghes and J. -S. Lee, Narratives of the refugee crisis: A comparative study of mainstream-media and twitter, Media and Communication, vol. 7, no. 2, pp. 275–288, 2019.

DOI Google Scholar

S. Wallaschek, Solidarity in Europe in times of crisis, Journal of European Integration, vol. 41, no. 2, pp. 257–263, 2019.

DOI Google Scholar

S. Wallaschek, Contested solidarity in the Euro crisis and Europe’s migration crisis: A discourse network analysis, Journal of European Public Policy, vol. 27, no. 7, pp. 1034–1053, 2020.

DOI Google Scholar

S. Wallaschek, The discursive construction of solidarity: Analysing public claims in Europe’s migration crisis, Political Studies, vol. 68, no. 1, pp. 74–92, 2019.

DOI Google Scholar

S. Hochreiter and J. Schmidhuber, Long short-term memory, Neural computation, vol. 9, no. 8, pp. 1735–1780, 1997.

DOI Google Scholar

K. Binner and K. Scherschel, eds., Fluchtmigration und Gesellschaft: Von Nutzenkalkulen, Solidaritat und Exklusion. Arbeitsgesellschaft im Wandel, Beltz Juventa, 2019.

G. Dragolov, Z. S. Ignácz, J. Lorenz, J. Delhey, K. Boehnke, and K. Unzicker, Social Cohesion in the Western World: What Holds Societies Together: Insights from the Social Cohesion Radar. Cham, Switzerland: Springer, 2016.https://doi.org/10.1007/978-3-319-32464-7

DOI

S. R. Baker, A. Baksy, N. Bloom, S. J. Davis, and J. A. Rodden, Elections, political polarization, and economic uncertainty, NBER Working Papers, https://www.nber.org/system/files/working_papers/w27961/w27961.pdf, 2020.https://doi.org/10.3386/w27961

DOI

F. Nicoli, Hard-line Euroscepticism and the Eurocrisis: Evidence from a panel study of 108 elections across Europe, JCMS:Journal of Common Market Studies, vol. 55, no. 2, pp. 312–331, 2017.

DOI Google Scholar

A. Bazo Vienrich and M. J. Creighton, What’s left unsaid? In-group solidarity and ethnic and racial differences in opposition to immigration in the United States, Journal of Ethnic and Migration Studies, vol. 44, no. 13, pp. 2240–2255, 2017.

DOI Google Scholar

D. Heerwegh, Mode differences between face-to-face and web surveys: An experimental investigation of data quality and social desirability effects, International Journal of Public Opinion Research, vol. 21, no. 1, pp. 111–121, 2009.

DOI Google Scholar

A. L. Janus, The influence of social desirability pressures on expressed immigration attitudes, Social Science Quarterly, vol. 91, no. 4, pp. 928–946, 2010.

DOI Google Scholar

M. Gangl and C. Giustozzi, The erosion of political trust in the great recession, CORRODE Working Paper, doi: 10.13140/RG.2.2.20930.07366.

C. R. Sunstein, #Republic: Divided Democracy in the Age of Social Media. Princeton, NJ, USA: Princeton University Press, 2018.https://doi.org/10.1515/9781400890521

DOI

D. Demszky, D. Movshovitz-Attias, J. Ko, A. Cowen, G. Nemade, and S. Ravi, GoEmotions: A dataset of fine-grained emotions, in Proc. 58^th Annual Meeting of the Association for Computational Linguistics, Online, 2020, pp. 4040–4054.https://doi.org/10.18653/v1/2020.acl-main.372

DOI

K. Ding, J. Li, and Y. Zhang, Hashtags, emotions, and comments: A large-scale dataset to understand fine-grained social emotions to online topics, in Proc. 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, 2020, pp. 1376–1382.https://doi.org/10.18653/v1/2020.emnlp-main.106

DOI

T. Haider, S. Eger, E. Kim, R. Klinger, and W. Menninghaus, PO-EMO: Conceptualization, annotation, and modeling of aesthetic emotions in German and English poetry, in Proc. 12^th Language Resources and Evaluation Conference, Marseille, France, 2020, pp. 1652–1663.

C. Hutto and E. Gilbert, Vader: A parsimonious rule-based model for sentiment analysis of social media text, Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, no. 1, pp. 216–225, 2014.

L. A. M. Bostan and R. Klinger, An analysis of annotated corpora for emotion classification in text, in Proc. 27^th International Conference on Computational Linguistics, Santa Fe, NM, USA, 2018, pp. 2104–2119.

K. C. Fraser, I. Nejadgholi, and S. Kiritchenko, Understanding and countering stereotypes: A computational approach to the stereotype content model, in Proc. 59^th Annual Meeting of the Association for Computational Linguistics and the 11^th International Joint Conference on Natural Language Processing (Vol. 1: Long Papers), Online, 2021, pp. 600–616.https://doi.org/10.18653/v1/2021.acl-long.50

DOI

T. Beck, J. -U. Lee, C. Viehmann, M. Maurer, O. Quiring, and I. Gurevych, Investigating label suggestions for opinion mining in German COVID-19 social media, in Proc. 59^th Annual Meeting of the Association for Computational Linguistics and the 11^th International Joint Conference on Natural Language Processing (Vol. 1: Long Papers), Online, 2021, pp. 1–13.https://doi.org/10.18653/v1/2021.acl-long.1

DOI

T. Walter, C. Kirschner, S. Eger, G. Glavaš, A. Lauscher, and S. P. Ponzetto, Diachronic analysis of German parliamentary proceedings: Ideological shifts through the lens of political biases, in Proc. 2021 ACM/IEEE Joint Conference on Digital Libraries, Champaign, IL, USA, 2021, pp. 51–60.https://doi.org/10.1109/JCDL52503.2021.00017

DOI

J. J. Jones, M. R. Amin, J. Kim, and S. Skiena, Stereotypical gender associations in language have decreased over time, Sociological Science, vol. 7, no. 1, pp. 1–35, 2020.

DOI Google Scholar

RIAS, Antisemitic incidents in Germany 2020, annual report, https://report-antisemitism.de/en/documents/Antisemitic_incidents_in_Germany_Annual-Report_Federal_Association_RIAS_2020.pdf, 2021.

F. Tahmasbi, L. Schild, C. Ling, J. Blackburn, G. Stringhini, Y. Zhang, and S. Zannettou, “Go Eat a Bat, Chang!”: On the emergence of sinophobic behavior on web communities in the face of COVID-19, in Proc. Web Conference 2021, Ljubljana, Slovenia, 2021, pp. 1122–1133.https://doi.org/10.1145/3442381.3450024

DOI

S. A. Bin-Nashwan, M. Al-Daihani, H. Abdul-Jabbar, and L. H. A. Al-Ttaffi, Social solidarity amid the COVID-19 outbreak: Fundraising campaigns and donors’ attitudes, International Journal of Sociology and Social Policy, vol. 42, nos. 3&4, pp. 232–247, 2020.https://doi.org/10.1108/IJSSP-05-2020-0173

DOI

S. Zajak, K. Stjepandić, and E. Steinhilper, Pro-migrant protest in times of COVID-19: Intersectional boundary spanning and hybrid protest practices, European Societies, vol. 23, no. sup1, pp. S172–S183, 2021.

DOI Google Scholar

J. Devlin, M. -W. Chang, K. Lee, and K. Toutanova, BERT: Pre-training of deep bidirectional transformers for language understanding, in Proc. 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Vol. 1 (Long and Short Papers), Minneapolis, MN, USA, 2019, pp. 4171–4186.

A. Conneau, K. Khandelwal, N. Goyal, V. Chaudhary, G. Wenzek, F. Guzmán, E. Grave, M. Ott, L. Zettlemoyer, and V. Stoyanov, Unsupervised cross-lingual representation learning at scale, in Proc. 58^th Annual Meeting of the Association for Computational Linguistics, Online, 2020, pp. 8440–8451.https://doi.org/10.18653/v1/2020.acl-main.747

DOI

J. He, J. Gu, J. Shen, and M. Ranzato, Revisiting self-training for neural sequence generation, presented at International Conference on Learning Representations （ICLR）2020, Addis Ababa, Ethiopia, 2020.

S. Cao, N. Kitaev, and D. Klein, Multilingual alignment of contextual word representations, presented at 8^th International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia, 2020.

W. Zhao, G. Glavaš, M. Peyrard, Y. Gao, R. West, and S. Eger, On the limitations of cross-lingual encoders as exposed by reference-free machine translation evaluation, in Proc. 58^th Annual Meeting of the Association for Computational Linguistics, Online, 2020, pp. 1656–1671.https://doi.org/10.18653/v1/2020.acl-main.151

DOI

W. Zhao, S. Eger, J. Bjerva, and I. Augenstein, Inducing language-agnostic multilingual representations, in Proc. *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics, Online, 2021, pp. 229–240.https://doi.org/10.18653/v1/2021.starsem-1.22

DOI

M. T. Ribeiro, S. Singh, and C. Guestrin, “Why should I trust you?”: Explaining the predictions of any classifier, in Proc. 22^nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 2016, pp. 1135–1144.https://doi.org/10.1145/2939672.2939778

DOI

S. F. Waterloo, S. E. Baumgartner, J. Peter, and P. M. Valkenburg, Norms of online expressions of emotion: Comparing Facebook, Twitter, Instagram, and Whatsapp, New Media&Society, vol. 20, no. 5, pp. 1813–1831, 2018.

DOI Google Scholar

ECDC, European centre for disease prevention and control, data on country response measures to COVID-19, https://www.ecdc.europa.eu/sites/default/files/documents/response_graphs_data_2022-06-02.csv, 2020.

I. Stewart, Y. Pinter, and J. Eisenstein, Si O no, que penses? Catalonian independence and linguistic identity on social media, in Proc. 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, New Orleans, LA, USA, 2018, pp. 136–141.https://doi.org/10.18653/v1/N18-2022

DOI

About this article

Publication history

Rights and permissions

Publication history

Received: 16 September 2021

Revised: 16 February 2022

Accepted: 15 May 2022

Published: 01 June 2022

Issue date: June 2022

Copyright

Rights and permissions

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).