Information Flows in a Gallery-Work-Entertainment Space: The Effect of a Digital Bulletin Board on Social Encounters

Human Organization, July 2009, Volume 68, Issue 2, p.206-217, 2009
Information Flows in a Gallery-Work-Entertainment Space: The Effect of a Digital Bulletin Board on Social Encounters
Elizabeth Churchill, Les Nelson, Elizabeth Churchill, Les Nelson

Digital media displays are increasingly common in public spaces. Typically, these are minimally interactive and predominantly function as signage or advertisements. However, in our work we have been exploring how digital media public displays can be designed to facilitate community content sharing in civic buildings, in organizations, and at social gatherings like conferences.

While most of our installations have been within fairly formal, professional settings, in this paper we address the impact of a digital community display on interactions between the inhabitants of a neighborhood art gallery and café.

We describe the location, the display itself, and the underlying content distribution and publication infrastructure. Findings from qualitative and quantitative analyses before and after the installation demonstrate that patrons easily adopted use of the display, which was used frequently to find out more about café/gallery events and for playful exchanges.

However, despite the enthusiasm of patrons and café staff, the café owners were wary of maintaining or extending the technology. We speculate on this reticence in terms of potential for services and technologies in public space technology design.

Another publication from the same category: Machine Learning and Data Science

WWW '17 Perth Australia April 2017

Drawing Sound Conclusions from Noisy Judgments

David Goldberg, Andrew Trotman, Xiao Wang, Wei Min, Zongru Wan

The quality of a search engine is typically evaluated using hand-labeled data sets, where the labels indicate the relevance of documents to queries. Often the number of labels needed is too large to be created by the best annotators, and so less accurate labels (e.g. from crowdsourcing) must be used. This introduces errors in the labels, and thus errors in standard precision metrics (such as P@k and DCG); the lower the quality of the judge, the more errorful the labels, consequently the more inaccurate the metric. We introduce equations and algorithms that can adjust the metrics to the values they would have had if there were no annotation errors.

This is especially important when two search engines are compared by comparing their metrics. We give examples where one engine appeared to be statistically significantly better than the other, but the effect disappeared after the metrics were corrected for annotation error. In other words the evidence supporting a statistical difference was illusory, and caused by a failure to account for annotation error.