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TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification

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arxiv 2010.12421 v2 pith:F4OKLCCE submitted 2020-10-23 cs.CL cs.SI

Francesco Barbieri , Jose Camacho-Collados , Leonardo Neves , Luis Espinosa-Anke This is my paper · ORCID
classification cs.CLcs.SI
keywords evaluationlanguagebaselinesclassificationstartingstrongtaskstweeteval
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Controlling the Risk of Corrupted Contexts for Language Models via Early-Exiting

    cs.AI 2025-10 conditional novelty 6.0 of 10

    An early-exit rule with a zero-shot fallback, calibrated by Learn-then-Test risk control, keeps the average loss from corrupted in-context demonstrations under a preset bound.

  2. Brain2Model Transfer: Training sensory and decision models with human neural activity as a teacher

    cs.NE 2025-06 conditional novelty 6.0 of 10

    A new transfer-learning framework, Brain2Model, uses human neural recordings to shape the latent representations of artificial networks, improving test accuracy in two tasks.

  3. Social Contagion in COVID-19 Discussions within the Belgian Reddit Community: A Statistical and Modeling Study

    cs.SI 2025-05 conditional novelty 6.0 of 10

    In r/Belgium, COVID-19 topics were seeded by external events, not by prior posts, but comment sentiment was contagious, and a two-layer bounded confidence model best captured that asymmetry.

  4. Confidence Optimization for Probabilistic Encoding

    cs.LG 2025-07 conditional novelty 4.0 of 10

    A confidence-aware loss plus a negative L2 variance term gives a small boost to probabilistic encoding classifiers on TweetEval, but gains over the SPC baseline are modest and inconsistent on RoBERTa.

  5. From BERT to Qwen: Hate Detection across architectures

    cs.CL 2025-07 conditional novelty 4.0 of 10

    Fine-tuned Qwen-0.5B slightly edges out DistilBERT and RoBERTa on a balanced hate speech corpus, and few-shot prompting substantially improves Gemma-3-1B over zero-shot.

  6. Involvement drives complexity of language in online debates

    cs.CL 2025-06 conditional novelty 4.0 of 10

    Influential Twitter users who are more partisan, negative, or offensive tend to use more lexically complex language, but the causal claim that involvement drives complexity is not supported.

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