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Creators/Authors contains: "Rojecki, Andrew"

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  1. ; ; ; ; ; ; ; ; (, IEEE)
    We describe the design process and the challenges we met during a rapid multi-disciplinary pandemic project related to stay-at-home orders and social media moral frames. Unlike our typical design experience, we had to handle a steeper learning curve, emerging and continually changing datasets, as well as under-specified design requirements, persistent low visual literacy, and an extremely fast turnaround for new data ingestion, prototyping, testing and deployment. We describe the lessons learned through this experience. 
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  2. ; ; ; ; ; ; ; ; (, Proceedings of 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA))
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  3. ; ; ; (, Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events)
    null (Ed.)
    We describe work in progress on detecting and understanding the moral biases of news sources by combining framing theory with natural language processing. First we draw connections between issue-specific frames and moral frames that apply to all issues. Then we analyze the connection between moral frame presence and news source political leaning. We develop and test a simple classification model for detecting the presence of a moral frame, highlighting the need for more sophisticated models. We also discuss some of the annotation and frame detection challenges that can inform future research in this area. 
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