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

Hate Towards the Political Opponent: A Twitter Corpus Study of the 2020 US Elections on the Basis of Offensive Speech and Stance Detection