mlsanityData to 7 Oct 2026

TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification

arXiv 2010.12421 ↗First posted 23 Oct 2020cs.CL cs.SIcited by 414

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Abstract (arXiv)

The experimental landscape in natural language processing for social media is too fragmented. Each year, new shared tasks and datasets are proposed, ranging from classics like sentiment analysis to irony detection or emoji prediction. Therefore, it is unclear what the current state of the art is, as there is no standardized evaluation protocol, neither a strong set of baselines trained on such domain-specific data. In this paper, we propose a new evaluation framework (TweetEval) consisting of seven heterogeneous Twitter-specific classification tasks. We also provide a strong set of baselines as starting point, and compare different language modeling pre-training strategies. Our initial experiments show the effectiveness of starting off with existing pre-trained generic language models, and continue training them on Twitter corpora.

Key facts. Cited by 414 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Oct 2020.

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Citing papers per year: 2021: 33; 2022: 71; 2023: 90; 2024: 89; 2025: 65; 2026: 66.

414 papers.

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  5. 2023Sentiment Analysis in the Era of Large Language Models: A Reality Checkcited by 262
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38 more
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  12. 2023Overthinking the Truth: Understanding how Language Models Process False Demonstrationscited by 78
  13. 2023Cognitive Reframing of Negative Thoughts through Human-Language Model Interactioncited by 77
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  15. 2021On Transferability of Prompt Tuning for Natural Language Processingcited by 74
  16. 2024A Survey of Multimodal Large Language Model from A Data-centric Perspectivecited by 73
  17. 2022TweetNLP: Cutting-Edge Natural Language Processing for Social Mediacited by 71
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  38. 2024Analysing The Impact of Sequence Composition on Language Model Pre-Trainingcited by 22

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