PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
February 12, 20260 citationsOpen Access

DICE: Advancing Social Media Research through Digital In-Context Experiments

HRHauke RoggenkampJBJohannes BoegershausenCHChristian Hildebrand

Key Points

  • The study aims to address the limitations of existing social media research methods by introducing DICE, which enhances both internal and ecological validity.
  • Introduced Digital In-Context Experiments (DICE) for social media research.
  • Manipulated entire feed compositions to evaluate user engagement.
  • Gathered data on post-level dwell times and traditional survey responses.
  • DICE provides improved insights into user attention in realistic social media environments.
  • Findings suggest that users engage differently than previously measured in isolated experiments.

Abstract

Social media research faces a fundamental tension between internal and ecological validity. Vignette experiments often isolate single posts, which fail to capture how users browse feeds where content competes for attention. Field studies offer realism but are subject to algorithmic interference, which threatens internal validity. Against this backdrop, we introduce Digital In-Context Experiments (DICE), an experimental paradigm that bridges this gap and offers experimental control in scrollable feeds that mimic digital environments. Researchers can manipulate entire feed compositions (rather than individual posts in isolation). DICE provides researchers with post-level dwell times as behavioral proxies for (in)attention and combines these unobtrusive measures with traditional survey responses. In this session, we will discuss the type of research questions DICE is more (less) suited for and how it relates to similar methodological advances in marketing research.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Roggenkamp et al. (2026) studied this question.

synapsesocial.com/papers/698d6df45be6419ac0d53400https://doi.org/10.5281/zenodo.18589494
Ask AI
Helpful
Bookmark
Share
View Full Paper