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widget: |
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- text: "climate change is a liberal hoax" |
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example_title: "example downplaying tweet" |
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- text: "We need to drastically reduce our carbon footprint to save the planet" |
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example_title: "example underscoring tweet" |
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--- |
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This model is a fine-tuned version of the ClimateBERT (distilroberta-base-climate-f) model (Webersinke et al., 2021) and created as part of a Bachelor Thesis at the University of St.Gallen (HSG). |
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The use-case of the FossilBERT model is the identification of climate-related tweets that try to "downplay the severity and certainty of climate-change related risks". |
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The initial climate-classification of the tweets is based on another fine-tuned version of ClimateBERT on the "ClimaText" dataset provided by Varini et al. (2020). |
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The fine-tuning procedure involved a training dataset of 2933 hand-labeled tweets (1458 "downplaying" / 1475 "underscoring") of five polarizing participants of the climate change discourse as well as tweets from the American Petroleum Institute and Greenpeace. |
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Full Credits of the underlying ClimateBERT model belong to: |
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Webersinke, N., Kraus, M., Bingler, J. A., & Leippold, M. (2021). Climatebert: A pretrained language model for climate-related text. arXiv preprint arXiv:2110.12010. https://doi.org/10.48550/arXiv.2110.12010 |
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ClimaText source: |
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Francesco S. Varini and Jordan Boyd-Graber and Massimiliano Ciaramita and Markus Leippold (2020). ClimaText: A Dataset for Climate Change Topic Detection, In: Tackling Climate Change with Machine Learning workshop at NeurIPS 2020, Online, 11 December 2020 - 11 December 2020. |
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