Katharina Reinecke


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Exploring the Effect of Author and Reader Identity in Online Story Writing: the STORIESINTHEWILD Corpus.
Tal August | Maarten Sap | Elizabeth Clark | Katharina Reinecke | Noah A. Smith
Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events

Current story writing or story editing systems rely on human judgments of story quality for evaluating performance, often ignoring the subjectivity in ratings. We analyze the effect of author and reader characteristics and story writing setup on the quality of stories in a short storytelling task. To study this effect, we create and release STORIESINTHEWILD, containing 1,630 stories collected on a volunteer-based crowdsourcing platform. Each story is rated by three different readers, and comes paired with the author’s and reader’s age, gender, and personality. Our findings show significant effects of authors’ and readers’ identities, as well as writing setup, on story writing and ratings. Notably, compared to younger readers, readers age 45 and older consider stories significantly less creative and less entertaining. Readers also prefer stories written all at once, rather than in chunks, finding them more coherent and creative. We also observe linguistic differences associated with authors’ demographics (e.g., older authors wrote more vivid and emotional stories). Our findings suggest that reader and writer demographics, as well as writing setup, should be accounted for in story writing evaluations.

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Writing Strategies for Science Communication: Data and Computational Analysis
Tal August | Lauren Kim | Katharina Reinecke | Noah A. Smith
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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. 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.