Social solidarity with Ukrainian and Syrian refugees in the twitter discourse. A comparison between 2015 and 2022

ABSTRACT

Incoming refugees from Ukraine are currently encountering a wave of solidarity that is seen, according to some, in stark contrast to the solidarity experienced by earlier groups of refugees i.e. from Syria during the so-called ‘immigration crisis’ in 2015. We aim to inform this debate on solidarity bias by collecting and analyzing quantitative data on (anti-)solidarity statements posted on Twitter during both waves of refugee immigration. We assess how social solidarity towards refugees differed between 2015 and the current wave of refugees fleeing Ukraine. To this end, we collect and analyze a longitudinal dataset of refugee-related tweets selected via hashtags and covering the period between January 2015 and August 2022. We first annotate the tweets for (anti-)solidarity expressions towards refugees. On these annotations, we train a supervised machine learning model and use it to automatically label over 2.3 million tweets. We assess the automatically labeled data for how statements related to refugee (anti-)solidarity developed and differed for distinct groups of refugees. Our findings show that in relative terms, refugee solidarity was expressed more often in tweets during September 2015 compared to March 2022. However, we find some evidence of solidarity bias in March 2022.

KEYWORDS:

• Social solidarity
• crises
• refugees
• Natural Language Processing

Acknowledgments

The authors would like to thank the participants of the ConTrust lunch seminar 04–2022, the participants of the Workshop ‘The Comparative Politics of Solidarity’, held at Politicologenetmaal 2023 in Leuven, Belgium, and anonymous reviewers for their constructive comments on earlier drafts of this paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Replication files

The Python code for data cleaning and analysis is available at https://zenodo.org/record/8232678.

Additional information

Funding

Daniela Grunow would like to acknowledge funding from the German Research Foundation (FOR 5173, no. 439346934) and the Federal Ministry of Education and Research (funding code 01UG2114). Steffen Eger gratefully acknowledges support from the German Research Foundation (Heisenberg grant EG 375/5–1). Steffen Eger's NLLG group is further supported by the Federal Ministry of Education and Research via the grant “Metrics4NLG”.