---
title: "TweetEval: Unified Benchmark and Comparative — Barbieri 2020"
description: "Research on NLP for Twitter is scattered across many separate shared tasks and datasets."
authors: ["Francesco Barbieri", "Jose Camacho-Collados", "Leonardo Neves", "Luis Espinosa-Anke"]
year: 2020
venue: "arXiv:2010.12421"
source: "https://arxiv.org/abs/2010.12421"
doi: "10.48550/arxiv.2010.12421"
arxiv: "2010.12421"
licence: "arXiv — non-exclusive licence to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/barbieri-2020-tweeteval-unified-benchmark-comparative-evaluation/"
---
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# TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification

**Authors:** [Francesco Barbieri](https://md.research.elamur.ai/free/en/authors/francesco-barbieri/), [Jose Camacho-Collados](https://md.research.elamur.ai/free/en/authors/jose-camacho-collados/), [Leonardo Neves](https://md.research.elamur.ai/free/en/authors/leonardo-neves/), [Luis Espinosa-Anke](https://md.research.elamur.ai/free/en/authors/luis-espinosa-anke/) · **Year:** 2020 · **Venue:** arXiv:2010.12421 · **Licence:** [arXiv — non-exclusive licence to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2010.12421) · [PDF](https://arxiv.org/pdf/2010.12421) · [DOI 10.48550/arxiv.2010.12421](https://doi.org/10.48550/arxiv.2010.12421) · [arXiv 2010.12421](https://arxiv.org/abs/2010.12421) · [OpenAlex](https://openalex.org/W3094002940)

**Citations:** 674 citations (openalex-snapshot · 2026-10-14) · [278](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Research on NLP for Twitter is scattered across many separate shared tasks and datasets. Each one has its own splits and metrics, so it is hard to tell which methods are actually the state of the art for tweets. TweetEval addresses this with a single English benchmark in the style of GLUE. It gathers seven tweet classification tasks, all taken from earlier SemEval competitions: sentiment, emotion, offensive language, hate speech, stance, emoji and irony. The authors applied the same preprocessing and train/validation/test splitting rules to every task. They kept each task's original metric and combined them into one averaged score (TE). They report classic baselines (SVM, FastText, BLSTM) and compare three ways of pre-training RoBERTa: the standard pre-trained model, the same model trained further on 60M English tweets, and a model trained on tweets alone from scratch. The best results came from taking a general-purpose pre-trained model and continuing its training on tweets, while the tweet-only model did worst of the three. TweetEval gives later social-media language models a common, reproducible test.

## Key points
- The seven tasks come from SemEval 2016–2019 datasets: Affects in Tweets (cut down to 4 emotions), Emoji Prediction (20 classes), Irony Detection, HatEval, OffensEval, sentiment Subtask A and Stance in Tweets (5 targets). Training sets range from a few hundred to about 45k tweets.
- Most tasks are scored with macro-F1. The exceptions are stance (average F1 of the favor and against classes), irony (F1 of the ironic class) and sentiment (macro-averaged recall). TE is the average of all the task scores.
- Average validation scores: RoB-RT 69.4, RoB-Bs 67.7, RoB-Tw 65.4. On the test set the simple baselines scored lower: SVM 53.5, FastText 58.1, BLSTM 56.5.
- The Twitter corpus has about 584M tokens. Each RoBERTa variant took roughly 8–9 days on 8 V100 GPUs, costing about USD 4,000. Irony was the only task where the tweet-only model generalized better.
- Emoji prediction scores are low partly because Twitter's sharing policy limited the released training data to 50k tweets. The data and code are at github.com/cardiffnlp/tweeteval.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Other](https://md.research.elamur.ai/free/en/shelves/other/)

## Cited by

Cited by

278 papers of our library cite it (of the 140673 whose bibliographies we read); 13 of them have a page here, 265 not yet.

- [CSECU-DSG @ Causal News Corpus 2022: Fusion of RoBERTa Transformers Variants for Causal Event Classification](https://md.research.elamur.ai/free/en/papers/aziz-2022-csecu-dsg-causal-news-corpus/)
- [XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond](https://md.research.elamur.ai/free/en/papers/barbieri-2022-xlm-t-multilingual-language-models/)
- [HateBERT: Retraining BERT for Abusive Language Detection in English](https://md.research.elamur.ai/free/en/papers/caselli-2021-hatebert-retraining-bert-abusive-language/)
- [Chain-of-Thought Embeddings for Stance Detection on Social Media](https://md.research.elamur.ai/free/en/papers/gatto-2023-chain-thought-embeddings-stance-detection/)
- [Cross-Domain Label-Adaptive Stance Detection](https://md.research.elamur.ai/free/en/papers/hardalov-2021-cross-domain-label-adaptive-stance/)
- [ChatGPT: Jack of all trades, master of none](https://md.research.elamur.ai/free/en/papers/kocon-2023-chatgpt-jack-all-trades-master/)
- [TimeLMs: Diachronic Language Models from Twitter](https://md.research.elamur.ai/free/en/papers/loureiro-2022-timelms-diachronic-language-models-twitter/)
- [A Holistic Approach to Undesired Content Detection in the Real World](https://md.research.elamur.ai/free/en/papers/markov-2022-holistic-approach-undesired-content-detection/)
- [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://md.research.elamur.ai/free/en/papers/min-2022-rethinking-role-demonstrations-what-makes/)
- [Sentiment Analysis and Language Models for Kwanyama](https://md.research.elamur.ai/free/en/papers/nakashole-2026-sentiment-analysis-language-models-kwanyama/)
- [Chandler: An Explainable Sarcastic Response Generator](https://md.research.elamur.ai/free/en/papers/oprea-2021-chandler-explainable-sarcastic-response-generator/)
- [The Spectrum of Sentiment: Optimistic, Pessimistic, and Neutral Voices in Online Depression Discourse](https://md.research.elamur.ai/free/en/papers/tabusca-2026-spectrum-sentiment-optimistic-pessimistic-neutral/)
- [Sentiment Analysis in the Era of Large Language Models: A Reality Check](https://md.research.elamur.ai/free/en/papers/zhang-2023-sentiment-analysis-era-large-language/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{barbieri2020tweeteval,
  title = {{TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification}},
  author = {Barbieri, Francesco and Camacho-Collados, Jose and Neves, Leonardo and Espinosa-Anke, Luis},
  year = {2020},
  doi = {10.48550/arxiv.2010.12421},
  eprint = {2010.12421},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2010.12421}
}
```

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