当代在线反犹太主义和阴谋叙事的代码、模式和形态——2019冠状病毒病背景下的注释指南和标记德语数据集

Elisabeth Steffen, Helena Mihaljevic, Milena Pustet, Nyco Bischoff, Maria Do Mar Castro Varela, Yener Bayramoglu, Bahar Oghalai
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引用次数: 0

摘要

在2019冠状病毒病大流行期间,现有的阴谋论被刷新,新的阴谋论被创造出来,往往与反犹主义的叙述、刻板印象和准则交织在一起。互联网上大量的反犹主义和阴谋论内容使得数据驱动的算法方法对反歧视组织和研究人员至关重要。然而,在大文本语料库的学术实证研究中,对这两种相互关联的现象的表现和传播的研究还很不足。用于检测和分类特定内容的算法方法通常需要标记数据集,并根据概念上合理的指南进行注释。虽然针对更普遍的仇恨言论现象的数据集越来越多,但针对反犹主义和阴谋内容的语料库和注释指南的开发仍处于起步阶段,尤其是针对英语以外的语言。为了弥补这一差距,我们为2019冠状病毒病大流行背景下的反犹太主义和阴谋论在线内容制定了一份注释指南,其中包括工作定义,例如编码反犹太主义和大屠杀后反犹太主义等特定形式的反犹太主义。我们使用该指南来注释一个由$\sim \!在2020年3月至2021年12月之间发送的电报信息3,700美元。
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Codes, Patterns and Shapes of Contemporary Online Antisemitism and Conspiracy Narratives – an Annotation Guide and Labeled German-Language Dataset in the Context of COVID-19
Over the course of the COVID-19 pandemic, existing conspiracy theories were refreshed and new ones were created, often interwoven with antisemitic narratives, stereotypes and codes. The sheer volume of antisemitic and conspiracy theory content on the Internet makes data-driven algorithmic approaches essential for anti-discrimination organizations and researchers alike. However, the manifestation and dissemination of these two interrelated phenomena is still quite under-researched in scholarly empirical research of large text corpora. Algorithmic approaches for the detection and classification of specific contents usually require labeled datasets, annotated based on conceptually sound guidelines. While there is a growing number of datasets for the more general phenomenon of hate speech, the development of corpora and annotation guidelines for antisemitic and conspiracy content is still in its infancy, especially for languages other than English. To address this gap, we have developed an annotation guide for antisemitic and conspiracy theory online content in the context of the COVID-19 pandemic that includes working definitions, e.g. of specific forms of antisemitism such as encoded and post-Holocaust antisemitism. We use the guide to annotate a German-language dataset consisting of $\sim \! 3,700$ Telegram messages sent between 03/2020 and 12/2021.
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