Pith. sign in

REVIEW 1 cited by

Will-They-Won't-They: A Very Large Dataset for Stance Detection on Twitter

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2005.00388 v1 pith:T5YV2BNF submitted 2020-05-01 cs.CL

Costanza Conforti , Jakob Berndt , Mohammad Taher Pilehvar , Chryssi Giannitsarou , Flavio Toxvaerd , Nigel Collier This is my paper
classification cs.CL
keywords datasetdetectionstancet-theywill-they-wonannotationsavailablebenchmark
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We present a new challenging stance detection dataset, called Will-They-Won't-They (WT-WT), which contains 51,284 tweets in English, making it by far the largest available dataset of the type. All the annotations are carried out by experts; therefore, the dataset constitutes a high-quality and reliable benchmark for future research in stance detection. Our experiments with a wide range of recent state-of-the-art stance detection systems show that the dataset poses a strong challenge to existing models in this domain.

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

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

  1. MT2-CSD: A New Dataset and Multi-Semantic Knowledge Fusion Method for Conversational Stance Detection

    cs.CL 2025-06 conditional novelty 6.0 of 10

    The paper presents a large new English conversational stance detection dataset and a model that fuses LLM-generated relation and act knowledge, reporting state-of-the-art F1.

Pith tools