Peter Jin Hong

A comparison of visual and textual page previews in judging the helpfulness of web pages

We investigated the efficacy of visual and textual web page previews in predicting the helpfulness of web pages related to a specific topic. We ran two studies in the usability lab and collected data through an online survey. Participants (total of 245) were asked to rate the expected helpfulness of a web page based on a preview (four different thumbnail variations: a textual web page summary, a thumbnail/title/URL combination, a title/URL combination). In the lab studies, the same participants also rated the helpfulness of the actual web pages themselves. In the online study, the web page ratings were collected from a separate group of participants. Our results show that thumbnails add information about the relevance of web pages that is not available in the textual summaries of web pages (title, snippet & URL). However, showing only thumbnails, with no textual information, results in poorer performance than showing only textual summaries. The prediction inaccuracy caused by textual vs. visual previews was different: textual previews tended to make users overestimate the helpfulness of web pages, whereas thumbnails made users underestimate the helpfulness of web pages in most cases. In our study, the best performance was obtained by combining sufficiently large thumbnails (at least 200x200 pixels) with page titles and URLs - and it was better to make users focus primarily on the thumbnail by placing the title and URL below the thumbnail. Our studies highlighted four key aspects that affect the performance of previews: the visual/textual mode of the previews, the zoom level and size of the thumbnail, as well as the positioning of key information elements.

Research trails: getting back where you left off

In this paper, we present a prototype system that helps users in early-stage web research to create and reestablish context across fragmented work process, without requiring them to explicitly collect and organize the material they visit. The system clusters a users web history and shows it as research trails. We present two user interaction models with the research trails. The first interaction model is implemented as a standalone application, which presents a hierarchical view of research trails. The second interaction model is integrated with the web browser. It shows the users research trails as selectable and manipulable visual streams when they open a new tab. Thereby, the NewTab page serves as a springboard in the browser for a user resuming an ongoing task.