Matching and sorting in online dating

Using data on user attributes and interactions from an online dating site, we estimate mate preferences, and use the Gale-Shapley algorithm to predict stable matches. The predicted matches are similar to the actual matches achieved by the dating site, and the actual matches are approximately efficient. Out-of-sample predictions of offline matches, i.e., marriages, exhibit assortative mating patterns similar to those observed in actual marriages. Thus, mate preferences, without resort to search frictions, can generate sorting in marriages. However, we underpredict some of the correlation patterns; search frictions may play a role in explaining the discrepancy.

Computational Journalism: from Answering Question to Questioning Answers and Raising Good Questions

Our media is saturated with claims of ``facts'' made from data. Database research has in the past focused on how to answer queries, but has not devoted much attention to discerning more subtle qualities of the resulting claims, e.g., is a claim ``cherry-picking''? This paper proposes a Query Response Surface (QRS) based framework that models claims based on structured data as parameterized queries. A key insight is that we can learn a lot about a claim by perturbing its parameters and seeing how its conclusion changes. This framework lets us formulate and tackle practical fact-checking tasks --- reverse-engineering vague claims, and countering questionable claims --- as computational problems. Within the QRS based framework, we take one step further, and propose a problem along with efficient algorithms for finding high-quality claims of a given form from data, i.e. raising good questions, in the first place. This is achieved to using a limited number of high-valued claims to represent high-valued regions of the QRS. Besides the general purpose high-quality claim finding problem, lead-finding can be tailored towards specific claim quality measures, also defined within the QRS framework. An example of uniqueness-based lead-finding is presented for ``one-of-the-few'' claims, landing in interpretable high-quality claims, and an adjustable mechanism for ranking objects, e.g. NBA players, based on what claims can be made for them. Finally, we study the use of visualization as a powerful way of conveying results of a large number of claims. An efficient two stage sampling algorithm is proposed for generating input of 2d scatter plot with heatmap, evalutaing a limited amount of data, while preserving the two essential visual features, namely outliers and clusters. For all the problems, we present real-world examples and experiments that demonstrate the power of our model, efficiency of our algorithms, and usefulness of their results.

Beyond LibGuides: The Past, Present, and Future of Online Research Guides

© 2015, Jon C. Giullian and Ernest A. Zitser.The proliferation of research guides created using the LibGuides platform has triggered extensive discussion touting their benefits for everything from assessment, engagement, and marketing, to outreach and pedagogy. However, there is at present a relative paucity of critical reflection about the product’s place in the broader informational landscape. This article is an attempt to redress this lacuna. Relying primarily on examples from the field of Slavic, East European, and Eurasian studies, the authors briefly describe the evolution of online research guides; identify reasons for the proliferation of Springshare’s product in academic libraries; question whether LibGuides improve learning or reinforce information inequality in higher education; and propose a way to move beyond LibGuides.

An online daily diary study of alcohol use using Amazon's mechanical turk

Introduction and Aims: In recent years, unprecedented levels of Internet access and the widespread growth of emergent communication technologies have resulted in significantly greater population access for substance use researchers. Despite the research potential of such technologies, the use of the Internet to recruit individuals for participation in event-level research has been limited. The purpose of this paper is to provide a brief account of the methods and results from an online daily diary study of alcohol use. Design and Methods: Participants were recruited using Amazon's Mechanical Turk. Eligible participants completed a brief screener assessing demographics and health behaviours, with a subset of individuals subsequently recruited to participate in a 2 week daily diary study of alcohol use. Results: Multilevel models of the daily alcohol data derived from the Mechanical Turk sample (n=369) replicated several findings commonly reported in daily diary studies of alcohol use. Discussion and Conclusions: Results demonstrate that online participant recruitment and survey administration can be a fruitful method for conducting daily diary alcohol research. © 2014 Australasian Professional Society on Alcohol and other Drugs.