  People often take user ratings and reviews into consideration when shopping
for products or services online. However, such user-generated data contains
self-selection bias that could affect people decisions and it is hard to
resolve this issue completely by algorithms. In this work, we propose to raise
the awareness of the self-selection bias by making three types of information
concerning user ratings and reviews transparent. We distill these three pieces
of information (reviewers experience, the extremity of emotion, and reported
aspects) from the definition of self-selection bias and exploration of related
literature. We further conduct an online survey to assess the perceptions of
the usefulness of such information and identify the exact facets people care
about in their decision process. Then, we propose a visual design to make such
details behind user reviews transparent and integrate the design into an
experimental website for evaluation. The results of a between-subjects study
demonstrate that our bias-aware design significantly increases the awareness of
bias and their satisfaction with decision-making. We further offer a series of
design implications for improving information transparency and awareness of
bias in user-generated content.
