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We introduce the well-established social scientific concept of socialsolidarity and its contestation, anti-solidarity, as a new problem setting tosupervised machine learning in NLP to assess how European solidarity discourseschanged before and after the COVID-19 outbreak was declared a global pandemic.To this end, we annotate 2.3k English and German tweets for (anti-)solidarityexpressions, utilizing multiple human annotators and two annotation approaches(experts vs.\ crowds). We use these annotations to train a BERT model withmultiple data augmentation strategies. Our augmented BERT model that combinesboth expert and crowd annotations outperforms the baseline BERT classifiertrained with expert annotations only by over 25 points, from 58\% macro-F1 toalmost 85\%. We use this high-quality model to automatically label over 270ktweets between September 2019 and December 2020. We then assess theautomatically labeled data for how statements related to European(anti-)solidarity discourses developed over time and in relation to oneanother, before and during the COVID-19 crisis. Our results show thatsolidarity became increasingly salient and contested during the crisis. Whilethe number of solidarity tweets remained on a higher level and dominated thediscourse in the scrutinized time frame, anti-solidarity tweets initiallyspiked, then decreased to (almost) pre-COVID-19 values before rising to astable higher level until the end of 2020.

Changes in European Solidarity Before and During COVID-19: Evidence from a Large Crowd- and Expert-Annotated Twitter Dataset