An Evaluation of Search Strategies for User-Generated Video Content

As the amount of user-generated content (UGC) on websites such as YouTube have experienced explosive growth, the demand for searching for relevant content has expanded at a similar pace. Unfortunately the minimally-required production effort and decentralization of content make these searches problematic. In addition, most UGC search efforts rely on notoriously noisy usersupplied tags and comments. In this paper, we examine UGC search strategies on YouTube using video requests from several knowledge markets such as Yahoo! Answers. We compare crowdsourcing and student search efforts to YouTube’s own search interface and apply these strategies to different types of information needs, ranging from easy to difficult. We evaluate our findings using two different assessment methods and discuss how the relative time and financial costs of these three search strategies affect our results.