Dimitrios Athanasakis

Panagiotis Papadopoulos, Peter Snyder, Dimitrios Athanasakis et al.

Funding the production of quality online content is a pressing problem for content producers. The most common funding method, online advertising, is rife with well-known performance and privacy harms, and an intractable subject-agent conflict: many users do not want to see advertisements, depriving the site of needed funding. Because of these negative aspects of advertisement-based funding, paywalls are an increasingly popular alternative for websites. This shift to a "pay-for-access" web is one that has potentially huge implications for the web and society. Instead of a system where information (nominally) flows freely, paywalls create a web where high quality information is available to fewer and fewer people, leaving the rest of the web users with less information, that might be also less accurate and of lower quality. Despite the potential significance of a move from an "advertising-but-open" web to a "paywalled" web, we find this issue understudied. This work addresses this gap in our understanding by measuring how widely paywalls have been adopted, what kinds of sites use paywalls, and the distribution of policies enforced by paywalls. A partial list of our findings include that (i) paywall use is accelerating (2x more paywalls every 6 months), (ii) paywall adoption differs by country (e.g. 18.75% in US, 12.69% in Australia), (iii) paywalls change how users interact with sites (e.g. higher bounce rates, less incoming links), (iv) the median cost of an annual paywall access is $108 per site, and (v) paywalls are in general trivial to circumvent. Finally, we present the design of a novel, automated system for detecting whether a site uses a paywall, through the combination of runtime browser instrumentation and repeated programmatic interactions with the site. We intend this classifier to augment future, longitudinal measurements of paywall use and behavior.

Dimitrios Athanasakis, John Shawe-Taylor, Delmiro Fernandez-Reyes

Recent non-linear feature selection approaches employing greedy optimisation of Centred Kernel Target Alignment(KTA) exhibit strong results in terms of generalisation accuracy and sparsity. However, they are computationally prohibitive for large datasets. We propose randSel, a randomised feature selection algorithm, with attractive scaling properties. Our theoretical analysis of randSel provides strong probabilistic guarantees for correct identification of relevant features. RandSel's characteristics make it an ideal candidate for identifying informative learned representations. We've conducted experimentation to establish the performance of this approach, and present encouraging results, including a 3rd position result in the recent ICML black box learning challenge as well as competitive results for signal peptide prediction, an important problem in bioinformatics.