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

arXiv: https://arxiv.org/abs/2103.01664 · HTML: https://mlsanity.com/paper/2103.01664/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 34 references that are in the map.
- First posted on arXiv 2 Mar 2021.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [Hate Speech Dataset from a White Supremacy Forum](https://mlsanity.com/paper/1809.04444/) (2018)
- [SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)](https://mlsanity.com/paper/2006.07235/) (2020)
- [Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisis](https://mlsanity.com/paper/1701.08118/) (2017)
- [Detecting Online Hate Speech Using Context Aware Models](https://mlsanity.com/paper/1710.07395/) (2017)
- [Stance and Sentiment in Tweets](https://mlsanity.com/paper/1605.01655/) (2016)
- [Simple Open Stance Classification for Rumour Analysis](https://mlsanity.com/paper/1708.05286/) (2017)
- [Stance Detection on Tweets: An SVM-based Approach](https://mlsanity.com/paper/1803.08910/) (2018)
- [Stance Prediction for Russian: Data and Analysis](https://mlsanity.com/paper/1809.01574/) (2018)

## Built on by (most cited first, 30 of 45)

- [You Only Prompt Once: On the Capabilities of Prompt Learning on Large Language Models to Tackle Toxic Content](https://mlsanity.com/paper/2308.05596/) (2023)
- [Robust Hate Speech Detection in Social Media: A Cross-Dataset Empirical Evaluation](https://mlsanity.com/paper/2307.01680/) (2023)
- [Listening to Affected Communities to Define Extreme Speech: Dataset and Experiments](https://mlsanity.com/paper/2203.11764/) (2022)
- [An Investigation of Large Language Models for Real-World Hate Speech Detection](https://mlsanity.com/paper/2401.03346/) (2024)
- [ToxiCraft: A Novel Framework for Synthetic Generation of Harmful Information](https://mlsanity.com/paper/2409.14740/) (2024)
- [Vicarious Offense and Noise Audit of Offensive Speech Classifiers: Unifying Human and Machine Disagreement on What is Offensive](https://mlsanity.com/paper/2301.12534/) (2023)
- [Multi-Task Learning with Sentiment, Emotion, and Target Detection to Recognize Hate Speech and Offensive Language](https://mlsanity.com/paper/2109.10255/) (2021)
- [An Information Retrieval Approach to Building Datasets for Hate Speech Detection](https://mlsanity.com/paper/2106.09775/) (2021)
- [SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research](https://mlsanity.com/paper/2310.14757/) (2023)
- [MFTCXplain: A Multilingual Benchmark Dataset for Evaluating the Moral Reasoning of LLMs through Multi-hop Hate Speech Explanation](https://mlsanity.com/paper/2506.19073/) (2025)
- [Analyzing Hate Speech Data along Racial, Gender and Intersectional Axes](https://mlsanity.com/paper/2205.06621/) (2022)
- [HateModerate: Testing Hate Speech Detectors against Content Moderation Policies](https://mlsanity.com/paper/2307.12418/) (2023)
- [EkoHate: Abusive Language and Hate Speech Detection for Code-switched Political Discussions on Nigerian Twitter](https://mlsanity.com/paper/2404.18180/) (2024)
- [Decoding Hate: Exploring Language Models' Reactions to Hate Speech](https://mlsanity.com/paper/2410.00775/) (2024)
- [From Theories on Styles to their Transfer in Text: Bridging the Gap with a Hierarchical Survey](https://mlsanity.com/paper/2110.15871/) (2021)
- [Cross-Target Stance Detection: A Survey of Techniques, Datasets, and Challenges](https://mlsanity.com/paper/2409.13594/) (2024)
- [Semi-supervised Stance Detection of Tweets Via Distant Network Supervision](https://mlsanity.com/paper/2201.00614/) (2022)
- [Anti-Asian Hate Speech Detection via Data Augmented Semantic Relation Inference](https://mlsanity.com/paper/2204.07010/) (2022)
- [Is It Worth the (Environmental) Cost? Limited Evidence for Temporal Adaptation via Continuous Training](https://mlsanity.com/paper/2210.07365/) (2022)
- [Mitigating Bias in Conversations: A Hate Speech Classifier and Debiaser with Prompts](https://mlsanity.com/paper/2307.10213/) (2023)
- [Towards a Unified Framework for Adaptable Problematic Content Detection via Continual Learning](https://mlsanity.com/paper/2309.16905/) (2023)
- [MasonPerplexity at Multimodal Hate Speech Event Detection 2024: Hate Speech and Target Detection Using Transformer Ensembles](https://mlsanity.com/paper/2402.01967/) (2024)
- [A Federated Approach to Few-Shot Hate Speech Detection for Marginalized Communities](https://mlsanity.com/paper/2412.04942/) (2024)
- [Culture Matters in Toxic Language Detection in Persian](https://mlsanity.com/paper/2506.03458/) (2025)
- [Effectiveness of Counter-Speech against Abusive Content: A Multidimensional Annotation and Classification Study](https://mlsanity.com/paper/2506.11919/) (2025)
- [Bridging Gaps in Hate Speech Detection: Meta-Collections and Benchmarks for Low-Resource Iberian Languages](https://mlsanity.com/paper/2510.11167/) (2025)
- [Political Communities on Twitter: Case Study of the 2022 French Presidential Election](https://mlsanity.com/paper/2204.07436/) (2022)
- [O-Dang! The Ontology of Dangerous Speech Messages](https://mlsanity.com/paper/2207.10652/) (2022)
- [Sociocultural knowledge is needed for selection of shots in hate speech detection tasks](https://mlsanity.com/paper/2304.01890/) (2023)
- [Studying Socially Unacceptable Discourse Classification (SUD) through different eyes: "Are we on the same page ?"](https://mlsanity.com/paper/2308.04180/) (2023)

## Abstract (arXiv)

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.