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。

2020 年美国选举中针对政治对手的仇恨：基于攻击性言论和立场检测的 Twitter 语料库研究

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

One of the hallmarks of a free and fair society is the ability to conduct a
peaceful and seamless transfer of power from one leader to another.
Democratically, this is measured in a citizen population's trust in the
electoral system of choosing a representative government. In view of the well
documented issues of the 2016 US Presidential election, we conducted an
in-depth analysis of the 2018 US Midterm elections looking specifically for
voter fraud or suppression. The Midterm election occurs in the middle of a 4
year presidential term. For the 2018 midterms, 35 senators and all the 435
seats in the House of Representatives were up for re-election, thus, every
congressional district and practically every state had a federal election. In
order to collect election related tweets, we analyzed Twitter during the month
prior to, and the two weeks following, the November 6, 2018 election day. In a
targeted analysis to detect statistical anomalies or election interference, we
identified several biases that can lead to wrong conclusions. Specifically, we
looked for divergence between actual voting outcomes and instances of the
#ivoted hashtag on the election day. This analysis highlighted three states of
concern: New York, California, and Texas. We repeated our analysis discarding
malicious accounts, such as social bots. Upon further inspection and against a
backdrop of collected general election-related tweets, we identified some
confounding factors, such as population bias, or bot and political ideology
inference, that can lead to false conclusions. We conclude by providing an
in-depth discussion of the perils and challenges of using social media data to
explore questions about election manipulation.

本篇论文探讨了 2018 年美国中期选举的选民欺诈和压制问题，并通过分析社交媒体数据，找到了选举干扰的罕见迹象。研究发现，社交媒体数据的分析存在着诸多误差和局限性，因此必须谨慎对待这些数据。