Showing papers by "Michael S. Bernstein published in 2009"

Collabio: a game for annotating people within social networks

Abstract: We present Collabio, a social tagging game within an online social network that encourages friends to tag one another. Collabio's approach of incentivizing members of the social network to generate information about each other produces personalizing information about its users. We report usage log analysis, survey data, and a rating exercise demonstrating that Collabio tags are accurate and augment information that could have been scraped online.

Note to self: examining personal information keeping in a lightweight note-taking tool

Abstract: This paper describes a longitudinal field experiment in personal note-taking that examines how people capture and use information in short textual notes. Study participants used our tool, a simple browser-based textual note-taking utility, to capture personal information over the course of ten days. We examined the information they kept in notes using the tool, how this information was expressed, and aspects of note creation, editing, deletion, and search. We found that notes were recorded extremely quickly and tersely, combined information of multiple types, and were rarely revised or deleted. The results of the study demonstrate the need for a tool such as ours to support the rapid capture and retrieval of short notes-to-self, and afford insights into how users' actual note-keeping tendencies could be used to better support their needs in future PIM tools.

Understanding and Supporting Directed Content Sharing on the Web

Abstract: To find interesting, personally relevant web content, we often rely on friends and colleagues to pass links along as they encounter them. In this paper, we study and augment link-sharing via e-mail, the most popular means of sharing web content today. Armed with survey data indicating that active sharers of novel web content are often those that actively seek it out, we present FeedMe, a plug-in for Google Reader that makes directed sharing of content a more salient part of the user experience. Our survey research indicates that sharing is moderated by concern about relevancy to the recipient, a desire to send only novel content to the recipient, and the effort required to share. FeedMe allays these concerns by recommending friends who may be interested in seeing the content, providing information on what the recipient has seen and how many emails they have received recently, and giving recipients the opportunity to provide lightweight feedback when they appreciate shared content. FeedMe introduces a novel design space for mixed-initiative social recommenders: friends who know the user voluntarily vet the material on the user’s behalf. We present a two week field experiment (N=60) demonstrating that FeedMe’s recommendations and social awareness features made it easier and more enjoyable to share content that recipients appreciated and would not have found otherwise. Author