The 2020 US Elections have been, more than ever before, characterized by
social media campaigns and mutual accusations. We investigate in this paper if
this manifests also in online communication of the supporters of the candidates
Biden and Trump, by uttering hateful and offensive communication. We formulate
an annotation task, in which we join the tasks of hateful/offensive speech
detection and stance detection, and annotate 3000 Tweets from the campaign
period, if they express a particular stance towards a candidate. Next to the
established classes of favorable and against, we add mixed and neutral stances
and also annotate if a candidate is mentioned without an opinion expression.
Further, we annotate if the tweet is written in an offensive style. This
enables us to analyze if supporters of Joe Biden and the Democratic Party
communicate differently than supporters of Donald Trump and the Republican
Party. A BERT baseline classifier shows that the detection if somebody is a
supporter of a candidate can be performed with high quality (.89 F1 for Trump
and .91 F1 for Biden), while the detection that somebody expresses to be
against a candidate is more challenging (.79 F1 and .64 F1, respectively). The
automatic detection of hate/offensive speech remains challenging (with .53 F1).
Our corpus is publicly available and constitutes a novel resource for
computational modelling of offensive language under consideration of stances.

本研究分析 2020 年美国大选期间，支持者在社交媒体上对候选人的仇恨和冒犯性言论，建立了针对这些言论的标注任务，并采用 BERT 基线分类器评测了该任务的性能，从而为针对仇恨语言的计算模型提供了一种新的公开资源。