# Us vs. Them: A Dataset of Populist Attitudes, News Bias and Emotions

arXiv: https://arxiv.org/abs/2101.11956 · HTML: https://mlsanity.com/paper/2101.11956/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 90 references that are in the map.
- First posted on arXiv 28 Jan 2021.

## Builds on (most cited first, 15 of 15)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [BERT Rediscovers the Classical NLP Pipeline](https://mlsanity.com/paper/1905.05950/) (2019)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)](https://mlsanity.com/paper/1903.08983/) (2019)
- [SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)](https://mlsanity.com/paper/2006.07235/) (2020)
- [FANG: Leveraging Social Context for Fake News Detection Using Graph Representation](https://mlsanity.com/paper/2008.07939/) (2020)
- [Hate Lingo: A Target-based Linguistic Analysis of Hate Speech in Social Media](https://mlsanity.com/paper/1804.04257/) (2018)
- [Analyzing the Targets of Hate in Online Social Media](https://mlsanity.com/paper/1603.07709/) (2016)
- [Tackling Online Abuse: A Survey of Automated Abuse Detection Methods](https://mlsanity.com/paper/1908.06024/) (2019)
- [CrowdTruth 2.0: Quality Metrics for Crowdsourcing with Disagreement](https://mlsanity.com/paper/1808.06080/) (2018)
- [Neural Discourse Structure for Text Categorization](https://mlsanity.com/paper/1702.01829/) (2017)
- [Graph-based Modeling of Online Communities for Fake News Detection](https://mlsanity.com/paper/2008.06274/) (2020)
- [Issue Framing in Online Discussion Fora](https://mlsanity.com/paper/1904.03969/) (2019)

## Built on by (most cited first, 9 of 9)

- [Introducing MBIB -- the first Media Bias Identification Benchmark Task and Dataset Collection](https://mlsanity.com/paper/2304.13148/) (2023)
- [From Melting Pots to Misrepresentations: Exploring Harms in Generative AI](https://mlsanity.com/paper/2403.10776/) (2024)
- [The Media Bias Taxonomy: A Systematic Literature Review on the Forms and Automated Detection of Media Bias](https://mlsanity.com/paper/2312.16148/) (2023)
- [Exploiting Transformer-based Multitask Learning for the Detection of Media Bias in News Articles](https://mlsanity.com/paper/2211.03491/) (2022)
- [MAGPIE: Multi-Task Media-Bias Analysis Generalization for Pre-Trained Identification of Expressions](https://mlsanity.com/paper/2403.07910/) (2024)
- [Expanding the Role of Affective Phenomena in Multimodal Interaction Research](https://mlsanity.com/paper/2305.10827/) (2023)
- [HebID: Detecting Social Identities in Hebrew-language Political Text](https://mlsanity.com/paper/2508.15483/) (2025)
- [From Emotion to Expression: Theoretical Foundations and Resources for Fear Speech](https://mlsanity.com/paper/2601.17132/) (2026)
- [Causal Effect Estimation with Latent Textual Treatments](https://mlsanity.com/paper/2602.15730/) (2026)

## Abstract (arXiv)

Computational modelling of political discourse tasks has become an increasingly important area of research in natural language processing. Populist rhetoric has risen across the political sphere in recent years; however, computational approaches to it have been scarce due to its complex nature. In this paper, we present the new textitUs vs. Them dataset, consisting of 6861 Reddit comments annotated for populist attitudes and the first large-scale computational models of this phenomenon. We investigate the relationship between populist mindsets and social groups, as well as a range of emotions typically associated with these. We set a baseline for two tasks related to populist attitudes and present a set of multi-task learning models that leverage and demonstrate the importance of emotion and group identification as auxiliary tasks.