PersaLog: Personalization of news article content

Eytan Adar, Carolyn Gearig, Ayshwarya Balasubramanian, Jessica Hullman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations


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. Persa-Log 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.

Original languageEnglish (US)
Title of host publicationCHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems
Subtitle of host publicationExplore, Innovate, Inspire
PublisherAssociation for Computing Machinery
Number of pages13
ISBN (Electronic)9781450346559
StatePublished - May 2 2017
Event2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2017 - Denver, United States
Duration: May 6 2017May 11 2017

Publication series

NameConference on Human Factors in Computing Systems - Proceedings


Other2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2017
Country/TerritoryUnited States


  • Guidelines
  • News personalization
  • Personalized content

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design


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