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arXiv · 2507.06205

DS@GT at CheckThat! 2025: Ensemble Methods for Detection of Scientific Discourse on Social Media

Ayush Parikh·Hoang Thanh Thanh Truong·Jeanette Schofield·Maximilian Heil
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Abstract

In this paper, we, as the DS@GT team for CLEF 2025 CheckThat! Task 4a Scientific Web Discourse Detection, present the methods we explored for this task. For this multiclass classification task, we determined if a tweet contained a scientific claim, a reference to a scientific study or publication, and/or mentions of scientific entities, such as a university or a scientist. We present 3 modeling approaches for this task: transformer finetuning, few-shot prompting of LLMs, and a combined ensemble model whose design was informed by earlier experiments. Our team placed 7th in the competition, achieving a macro-averaged F1 score of 0.8611, an improvement over the DeBERTaV3 baseline of 0.8375. Our code is available on Github at https://github.com/dsgt-arc/checkthat-2025-swd/tree/main/subtask-4a.

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Ayush Parikh, Hoang Thanh Thanh Truong, Jeanette Schofield, Maximilian Heil. 2025-07-08. DS@GT at CheckThat! 2025: Ensemble Methods for Detection of Scientific Discourse on Social Media. https://arxiv.org/abs/2507.06205

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