Creddit Where it's Due: Social News Sites as a Model for Scientific Article Recommendations

Scientists are always working to determine which articles are interesting to them, timely, and relevant to their research. If working in an unfamiliar research area, searching for papers becomes even more difficult. By allowing users to vote on the prominence of links, social news sites like Slashdot, Digg, and reddit.com have addressed the issue of surfacing new and interesting content from across the internet. Moreover, they provide opportunities to provide context and comment on the content. This study adapts the properties of these applications, particularly the social news site known as reddit, to explore how scientists' values inform the design of comparable systems for recommending scholarly articles. Through iterative design and user interviews, the prototype social news system—Creddit—intends to help scientists and professionals find interesting articles and informal critiques on papers. The work provides a concrete research context for unpacking the interplay between scientists' needs and system design.