Crowdsourced Adaptive Surveys

TL;DR

This paper presents a novel crowdsourced adaptive survey methodology that leverages natural language processing and adaptive algorithms to dynamically generate and prioritize survey questions, enhancing the exploration of niche topics with minimal survey length increase.

Contribution

It introduces a new adaptive survey approach combining NLP and multi-armed bandit algorithms to improve question generation and topic exploration in survey research.

Findings

Successfully identified niche topics in diverse domains

Reduced survey length while maintaining question relevance

Demonstrated effectiveness in real-world applications

Abstract

Public opinion surveys are vital for informing democratic decision-making, but responding to rapidly evolving information environments and measuring beliefs within niche communities can be challenging for traditional survey methods. This paper introduces a crowdsourced adaptive survey methodology (CSAS) that unites advances in natural language processing and adaptive algorithms to generate question banks that evolve with user input. The CSAS method converts open-ended text provided by participants into survey items and applies a multi-armed bandit algorithm to determine which questions should be prioritized in the survey. The method's adaptive nature allows for the exploration of new survey questions, while imposing minimal costs in survey length. Applications in the domains of Latino information environments, national issue importance, and local politics showcase CSAS's ability to identify topics that might otherwise escape the notice of survey researchers. I conclude by highlighting CSAS's potential to bridge conceptual gaps between researchers and participants in survey research.