Designers are increasingly leveraging online crowds; yet, online contributors may lack the expertise, context, and sensitivity to provide effective critique. Rubrics help feedback providers but require domain experts to write them and may not generalize across design domains. This paper introduces and tests a novel semi-automated method to support feedback providers by analyzing feedback language. In our first study, 52 students from two design courses created design solutions and received feedback from 176 online providers. Instructors, students, and crowd contributors rated the helpfulness of each feedback response. From this data, an algorithm extracted a set of natural language features (e.g., specificity, sentiment etc.) that correlated with the ratings. The features accurately predicted the ratings and remained stable across different raters and design solutions. Based on these features, we produced a critique style guide with feedback examples - automatically selected for each feature - to help providers revise their feedback through self-assessment. In a second study, we tested the validity of the guide through a between-subjects experiment (n=50). Providers wrote feedback on design solutions with or without the guide. Providers generated feedback with higher perceived helpfulness when using our style-based guidance.

Feedback is information that can improve task performance. Online communities, educational forums, and crowd-based feedback platforms all support feedback exchange among a more diverse set of sources than ever before, with greater control over how to moderate this exchange. In this work, we study how the power relationship between the source and receiver and the tone of language influence the recep-tivity, effort, and work performance resulting from online feedback exchange. We conducted an online experiment manipulating affective language and source of feedback on a writing task. We found that critiques with positive affec-tive language increased positive emotions and reduced participants' annoyance and frustration, which led to an increase in work quality, compared to critiques without positive language. Feedback without positive affective language led to more edits, but not better work outcomes. Participants reacted more positively to feedback from an anonymous source than from a peer or an authority. Our findings provide design implications for platforms to support more fruitful feedback exchange.