mlsanityData to 7 Oct 2026

SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research

arXiv 2310.14757 ↗First posted 23 Oct 2023cs.CLcited by 9

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Abstract (arXiv)

Despite its relevance, the maturity of NLP for social media pales in comparison with general-purpose models, metrics and benchmarks. This fragmented landscape makes it hard for the community to know, for instance, given a task, which is the best performing model and how it compares with others. To alleviate this issue, we introduce a unified benchmark for NLP evaluation in social media, SuperTweetEval, which includes a heterogeneous set of tasks and datasets combined, adapted and constructed from scratch. We benchmarked the performance of a wide range of models on SuperTweetEval and our results suggest that, despite the recent advances in language modelling, social media remains challenging.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 44 of its 89 references that are in the map. First posted on arXiv 23 Oct 2023.

Builds on

44 of 89 references.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 4; 2026: 1.

9 papers.