Millions of people search the Web each day. As a consequence, the ranking algorithms employed by Web search engines have a profound influence on which pages users visit. Characterizing this influence, and informing users when different engines favor certain sites or points of view, enables more transparent access to the Web's information. We present PAWS, a platform for analyzing differences among Web search engines. PAWS measures content emphasis: the degree to which differences across search engines' rankings correlate with features of the ranked content, including point of view (e.g., positive or negative orientation toward their company's products) and advertisements. We propose an approach for identifying the orientations in search results at scale, through a novel technique that minimizes the expected number of human judgments required. We apply PAWS to news search on Google and Bing, and find no evidence that the engines emphasize results that express positive orientation toward the engine company's products. We do find that the engines emphasize particular news sites, and that they also favor pages containing their company's advertisements, as opposed to competitor advertisements.