Hate Towards the Political Opponent: A T witter Corpus Study of the 2020 US Elections on the Basis of Offensive Speech and Stance Detection (2021)

Abstract: 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 with- out an opinion expression. Further, we an- notate 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.

## introduction social media are indispensable to political campaigns ever since barack obama used them so successfully in 2008 @xcite . twitter in particular is a much-frequented form of communication with monthly 330 million active users this paper contains offensive language. @xcite . the microblogging platform was credited to have played a key role in donald trump's rise to power @xcite . as twitter enables users to express their opinions about topics and targets, the insights gained from detecting stance in political tweets can help monitor the voting base. in addition to the heated election of trump in 2016, the world has also seen an increase of hate speech @xcite . defined as "any communication that disparages a target group of people based on some characteristic such as race, colour, ethnicity, gender, sexual orientation, nationality, religion, or other characteristic" @xcite , hate speech is considered "a particular form of offensive language" @xcite . however, some authors also conflate hateful and offensive speech and define hate speech as explicitly or implicitly degrading a person or group @xcite . over the years, the use of hate speech in social media has increased (de @xcite . consequently, there is a growing need for approaches that detect hate speech automatically @xcite . from the perspective of natural language processing (nlp), the combination of political stance and hate speech detection provides promising classification tasks, namely determining the attitude a text displays towards a pre-determined target and the presence of hateful and offensive speech. in contrast to prior work on stance detection @xcite , we not only annotate if a text is favorable, against or does not mention the target at all (neither), but include whether the text of the tweet displays a mixed (both favorable and against) or neutral stance towards the targets. with this formulation we are also able to mark tweets that mention a target without taking a clear stance. to annotate hateful and offensive tweets, we follow the def-inition of @xcite and adapt our guidelines to political discourse. our contributions are the following: ## conclusion and future work in this paper, we have investigated stance detection on political tweets and whether or not supporters of trump use more hate speech than supporters of biden (not significantly). we found that manual annotation is possible with acceptable agreement scores, and that automatic stance detection towards political candidates and parties is possible with good performance. the limitations of this study are twofold -on the one side, future work might want to consider to add the nicknames of all main candidates and explicitly include social media posts about the party, not only about the candidate, as we found a separation is often difficult. further, we did not perform extensive hyperparameter optimization in our neural approach. we suggest that future work invests in developing computational models that work across corpora and are able to adapt to domain and time-specific as well as societal and situational expressions of hate and offensive language. this is required, as our corpus shows that some references to offensive content are realized by domain-specific and societal expressions. this might be realized by combining offensive language detection and stance detection in a joint multi-task learning approach, potentially including other aspects like personality traits or specific emotions. we assume that such concepts can benefit from representations in joint models.