DS@GT at CheckThat! 2025: Ensemble Methods for Detection of Scientific Discourse on Social Media
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2025
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| _version_ | 1866918086510116864 |
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| author | Parikh, Ayush Truong, Hoang Thanh Thanh Schofield, Jeanette Heil, Maximilian |
| author_facet | Parikh, Ayush Truong, Hoang Thanh Thanh Schofield, Jeanette Heil, Maximilian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_06205 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DS@GT at CheckThat! 2025: Ensemble Methods for Detection of Scientific Discourse on Social Media Parikh, Ayush Truong, Hoang Thanh Thanh Schofield, Jeanette Heil, Maximilian Computation and Language 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. |
| title | DS@GT at CheckThat! 2025: Ensemble Methods for Detection of Scientific Discourse on Social Media |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2507.06205 |