Changes in European Solidarity Before and During COVID-19: Evidence from a Large Crowd- and Expert-Annotated Twitter Dataset
Changes in European Solidarity Before and During COVID-19: Evidence from a Large Crowd- and Expert-Annotated Twitter Dataset
复制标题
COVID-19 之前和期间欧洲团结的变化：来自大量人群和专家注释的 Twitter 数据集的证据
DOI：
--
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Steffen Eger
中科院分区：
文献类型：
--
作者：
A. Ils;Dan Liu;Daniela Grunow;Steffen Eger
我们引入了社会团结的社会科学概念及其定义，反团结，作为NLP中监督机器学习的新问题设置，以评估COVID-19爆发被宣布为全球大流行病之前和之后欧洲团结话语的变化。为此，我们注释2.3k英语和德语推文（反）团结的表达，利用多个人类注释和两种注释方法（专家与人群）。我们使用这些注释来训练具有多种数据增强策略的BERT模型。我们的增强BERT模型结合了专家和人群注释，仅比用专家注释训练的基线BERT分类器高出25个点，从58%的宏F1到近85%。我们使用这个高质量的模型来自动标记2019年9月至2020年12月之间的超过27万条推文。然后，我们评估了自动标记的数据，以了解在COVID-19危机之前和期间，与欧洲（反）团结话语相关的声明如何随着时间的推移以及彼此之间的关系而发展。我们的研究结果表明，在危机期间，团结变得越来越突出和有争议。虽然声援推文的数量保持在较高水平，并在仔细审查的时间框架内占据主导地位，但反团结推文最初飙升，然后下降到（几乎）COVID-19前的值，然后上升到稳定的较高水平，直到2020年底。
We introduce the well-established social scientific concept of social solidarity and its contestation, anti-solidarity, as a new problem setting to supervised machine learning in NLP to assess how European solidarity discourses changed 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-)solidarity expressions, utilizing multiple human annotators and two annotation approaches (experts vs. crowds). We use these annotations to train a BERT model with multiple data augmentation strategies. Our augmented BERT model that combines both expert and crowd annotations outperforms the baseline BERT classifier trained with expert annotations only by over 25 points, from 58% macro-F1 to almost 85%. We use this high-quality model to automatically label over 270k tweets between September 2019 and December 2020. We then assess the automatically labeled data for how statements related to European (anti-)solidarity discourses developed over time and in relation to one another, before and during the COVID-19 crisis. Our results show that solidarity became increasingly salient and contested during the crisis. While the number of solidarity tweets remained on a higher level and dominated the discourse in the scrutinized time frame, anti-solidarity tweets initially spiked, then decreased to (almost) pre-COVID-19 values before rising to a stable higher level until the end of 2020.
DOI：
10.18653/v1/2020.acl-main.151
发表时间：
2020-05
期刊：
--
影响因子：
--
作者：
Wei Zhao;Goran Glavavs;Maxime Peyrard;Yang Gao-;Robert West;Steffen Eger
通讯作者：
Wei Zhao;Goran Glavavs;Maxime Peyrard;Yang Gao-;Robert West;Steffen Eger
DOI：
10.18653/v1/2021.starsem-1.22
发表时间：
2020-08
期刊：
--
影响因子：
--
作者：
Wei Zhao;Steffen Eger;Johannes Bjerva;Isabelle Augenstein
通讯作者：
Wei Zhao;Steffen Eger;Johannes Bjerva;Isabelle Augenstein
影响因子：
5
作者：
Margolin, Drew;Liao, Wang
通讯作者：
Liao, Wang