User-Centric Behavioral Tracking: Lessons from Three Case Studies with Do-It-Yourself Computational Pipelines

Alvin Zhou, Danaë Metaxa, Young Mie Kim, Kokil Jaidka

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

User-centric behavioral tracking, a cutting-edge computational social science tool, holds tremendous promise for advertising research. The article introduces the technique and presents three do-it-yourself (DIY) case studies in which researchers develop tracking applications and platforms, build infrastructures that host participants, maintain computational pipelines logging user behavior and content exposure, and manage logistical “ins and outs” such as onboarding, offboarding, and compensations all by themselves. We share our lessons, discuss challenges ahead for DIY user-centric behavioral tracking, and advocate for computational advertising scholars to become pioneers in this emerging body of work.

Original languageEnglish (US)
Pages (from-to)791-809
Number of pages19
JournalJournal of Advertising
Volume53
Issue number5
DOIs
StatePublished - 2024

Bibliographical note

Publisher Copyright:
© Copyright © 2024, American Academy of Advertising.

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