@proceedings{33886,
  author = {Jason Chuang and John Wilkerson and Rebecca Weiss and Dustin Tingley and Brandon Stewart and Margaret Roberts and Forough Poursabzi-Sangdeh and Justin Grimmer and Leah Findlater and Jordan Boyd-Graber and Jeff Heer},
  title = {Computer-Assisted Content Analysis: Topic Models for Exploring Multiple Subjective Interpretations},
  abstract = {<p><span>Content analysis, a labor-intensive but widely-applied research method, is increasingly being supplemented by computational techniques such as statistical topic modeling. However, while the discourse on content analysis centers heavily on reproducibility, computer scientists often focus more on increasing the scale of analysis and less on establishing the reliability of analysis results. The gap between user needs and available tools leads to justified skepticism, and limits the adoption and effective use of computational approaches. We argue that enabling </span><span>human-in-the-loop </span><span>machine learning requires establishing users{\textquoteright} trust in computer-assisted analysis. To this aim, we introduce our ongoing work on analysis tools for interac- tively exploring the space of available topic models. To aid tool development, we propose two studies to examine how a computer-aided workflow affects the uncovered codes, and how machine-generated codes impact analysis outcome. We present our prototypes and findings currently under submission.\&nbsp;</span></p>
},
  year = {2014},
  journal = {Advances in Neural Information Processing Systems Workshop on Human-Propelled Machine Learning},
  address = {Montreal, Canada},
  language = {eng},
}