Measuring Social Solidarity During Crisis:The Role of Design Choices

作     者：Steffen Eger Dan Liu Daniela Grunow 

作者机构：the Center for Cognitive Interaction Technology(CITEC)Bielefeld UniversityBielefeld 33615Germany the Computer Science DepartmentTechnical University DarmstadtDarmstadt 64289Germany the Faculty of Social SciencesGoethe University FrankfurtFrankfurt 60629Germany 

出 版 物：《Journal of Social Computing》 (社会计算（英文）)

年 卷 期：2022年第3卷第2期

页      面：139-157页

核心收录：

学科分类：0502[文学-外国语言文学] 050201[文学-英语语言文学] 05[文学] 

主　　题：social solidarity crises COVID-19 natural language processing 

摘      要：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 *** this end,we first annotate above 2000 tweets for(anti-)solidarity expressions towards immigrants,utilizing two annotation approaches(experts ***).On these annotations,we train a BERT model with multiple data augmentation strategies,which performs close to the human upper *** use this high-quality model to automatically label over 240000 tweets between September 2019 and June *** then assess the automatically labeled data for how statements related to migrant(anti-)solidarity developed over time,before and during the COVID-19 *** 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 *** the same period,the share of anti-solidarity tweets increased in a sub-sample of COVID-19-related *** findings highlight the importance of long-term observation,pre-and post-crisis comparison,and sampling in research interested in crisis related *** 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 *** our sample,the share of anti-solidarity tweets is higher in native(German)language tweets and among“anonymousTwitter users writing in German compared to English-language tweets of users located in Germany.