Learning to Drive on the Wrong Side of the Road: How American Computing Came to Rely on Conferences for Primary Publication

In contrast to other fields where conferences are typically for less polished or in-progress research, computing has long relied on referred conference papers as a venue for the final publication of completed research. While frequently a topic of informal discussion, debates about its efficacy, or library science research, the development of this phenomena has not been historically analyzed. This paper presents the first systematic investigation of the development of modern computing publications. It relies on semi-structured interviews with eight computing professors from diverse backgrounds to understand how researchers experienced changes in publication culture over time. Ultimately, the article concludes that the early presence of non-academic practitioners in research and a degree of path dependenceor a tendency to continue on the established path rather than the most economically optimal one allowed conferences to gain and hold prominence as the field exploded in popularity during the 1980s.

A First Look at Scams on YouTube

YouTube has become the second most popular website according to Alexa, and it represents an enticing platform for scammers to attract victims. Because of the computational difficulty of classifying multimedia, identifying scams on YouTube is more difficult than text-based media. As a consequence, the research community to-date has provided little insight into the prevalence, lifetime, and operational patterns of scammers on YouTube. In this short paper, we present a preliminary exploration of scam videos on YouTube. We begin by identifying 74 search queries likely to lead to scam videos based on the authors experience seeing scams during routine browsing. We then manually review and characterize the results to identify 668 scams in 3,700 videos. In a detailed analysis of our classifications and metadata, we find that these scam videos have a median lifetime of nearly nine months, and many rely on external websites for monetization. We also explore the potential of detecting scams from metadata alone, finding that metadata does not have enough predictive power to distinguish scams from legitimate videos. Our work demonstrates that scams are a real problem for YouTube users, motivating future work on this topic.