Aeonium: Visual analytics to support collaborative qualitative coding
Aeonium: Visual analytics to support collaborative qualitative coding
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Aeium：支持协作定性编码的可视化分析
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Cecilia R. Aragon
中科院分区：
文献类型：
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作者：
Margaret Drouhard;N. Chen;J. Suh;Rafal Kocielnik;Vanessa Peña Araya;Keting Cen;Xiangyi Zheng;Cecilia R. Aragon
定性编码提供了深入了解社交媒体的潜力，但该技术可能不一致且难以扩展。使用定性编码的研究人员通过代表分析类别的“代码”将结构强加于非结构化数据。我们的可视化分析界面 Aeium 通过多个研究人员分配的代码的可视化概述以及重要关键字和代码的分布来支持人类对协作编码的洞察。底层机器学习模型突出了模糊性和不一致之处。我们的目标不是将定性编码简化为机器可解决的问题，而是增强人类通过协作编码和重新解释数据获得的理解。我们对 39 名参与者进行了一项实验研究，他们使用我们的界面编写推文。除了加深对主题的理解之外，参与者还报告说，Aeium 的协作编码功能帮助他们反思自己的解释。参与者的反馈表明，可视化分析可以帮助促进丰富的定性分析，并为未来的探索提出设计建议。
Qualitative coding offers the potential to obtain deep insights into social media, but the technique can be inconsistent and hard to scale. Researchers using qualitative coding impose structure on unstructured data through “codes” that represent categories for analysis. Our visual analytics interface, Aeonium, supports human insight in collaborative coding through visual overviews of codes assigned by multiple researchers and distributions of important keywords and codes. The underlying machine learning model highlights ambiguity and inconsistency. Our goal was not to reduce qualitative coding to a machine-solvable problem, but rather to bolster human understanding gained from coding and reinterpreting the data collaboratively. We conducted an experimental study with 39 participants who coded tweets using our interface. In addition to increased understanding of the topic, participants reported that Aeonium's collaborative coding functionality helped them reflect on their own interpretations. Feedback from participants demonstrates that visual analytics can help facilitate rich qualitative analysis and suggests design implications for future exploration.