Writing Strategies for Science Communication: Data and Computational Analysis
Tal August, Lauren Kim, Katharina Reinecke, Noah A. Smith
摘要
Communicating complex scientific ideas without misleading or overwhelming the public is challenging. While science communication guides exist, they rarely offer empirical evidence for how their strategies are used in practice. Writing strategies that can be automatically recognized could greatly support science communication efforts by enabling tools to detect and suggest strategies for writers. We compile a set of writing strategies drawn from a wide range of prescriptive sources and develop an annotation scheme allowing humans to recognize them. We collect a corpus of 128K science writing documents in English and annotate a subset of this corpus. 1 We use the annotations to train transformer-based classifiers and measure the strategies' use in the larger corpus. We find that the use of strategies, such as storytelling and emphasizing the most important findings, varies significantly across publications with different reader audiences.
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引用它的顶会 Paper8
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它引用的顶会 Paper2
- Don't Stop Pretraining: Adapt Language Models to Domains and TasksSuchin Gururangan, Ana Marasovic, Swabha Swayamdipta, Kyle Lo 等ACL 2020 · 被引用 93 次
- Social Bias Frames: Reasoning about Social and Power Implications of LanguageMaarten Sap, Saadia Gabriel, Lianhui Qin, Dan Jurafsky 等ACL 2020 · 被引用 16 次
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