PersaLog: Personalization of News Article Content
Eytan Adar, Carolyn Gearig, Ayshwarya Balasubramanian, Jessica Hullman

Content personalization-automatically modifying text and multimedia features within articles based on the reader's individual features-is evolving as a new form of journalism. Informed by constraints articulated through a survey of journalists, we have implemented PersaLog, a novel system for creating personalized content (e.g., text and interactive visualizations). Because crafting, and validating, personalized content can be challenging to scale across articles (unlike feed personalization), we offer a simple Domain Specific Language (DSL), and editing environment, to support this task. PersaLog is particularly designed to support the personalization of existing text and visualizations. Our work provides guidelines for personalization as well as a system that allows for both subtle and dramatic personalization-driven content changes. We validate PersaLog using case and lab studies.

Available as: PDF (4.4Mb), to appear, CHI'17, Demo site: