Crowdsourced Adaptive Surveys

• URL: http://arxiv.org/abs/2401.12986v2
• Date: Fri, 06 Dec 2024 19:42:55 GMT
• Title: Crowdsourced Adaptive Surveys
• Authors: Yamil Velez,
• Abstract summary: This paper introduces a crowdsourced adaptive survey methodology (CSAS) The 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. I conclude by highlighting CSAS's potential to bridge conceptual gaps between researchers and participants in survey research.
• Score: 0.0
• License:
• 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.

Related papers

• Specializing Large Language Models to Simulate Survey Response Distributions for Global Populations [49.908708778200115]
  We are the first to specialize large language models (LLMs) for simulating survey response distributions. As a testbed, we use country-level results from two global cultural surveys. We devise a fine-tuning method based on first-token probabilities to minimize divergence between predicted and actual response distributions.
  arXiv  Detail & Related papers  (2025-02-10T21:59:27Z)
• Transforming Social Science Research with Transfer Learning: Social Science Survey Data Integration with AI [0.4944564023471818]
  Large-N nationally representative surveys, which have profoundly shaped American politics scholarship, represent related but distinct domains. Our study introduces a novel application of transfer learning (TL) to address these gaps. Models pre-trained on the Cooperative Election Study dataset are fine-tuned for use in the American National Election Studies dataset.
  arXiv  Detail & Related papers  (2025-01-11T16:01:44Z)
• Exploring LLMs for Automated Pre-Testing of Cross-Cultural Surveys [41.34785468969536]
  We propose using large language models (LLMs) to automate the questionnaire pretesting process in cross-cultural settings. Our study used LLMs to adapt a U.S.-focused climate opinion survey for a South African audience.
  arXiv  Detail & Related papers  (2025-01-10T14:17:48Z)
• A Survey of Stance Detection on Social Media: New Directions and Perspectives [50.27382951812502]
  stance detection has emerged as a crucial subfield within affective computing. Recent years have seen a surge of research interest in developing effective stance detection methods. This paper provides a comprehensive survey of stance detection techniques on social media.
  arXiv  Detail & Related papers  (2024-09-24T03:06:25Z)
• CLARINET: Augmenting Language Models to Ask Clarification Questions for Retrieval [52.134133938779776]
  We present CLARINET, a system that asks informative clarification questions by choosing questions whose answers would maximize certainty in the correct candidate. Our approach works by augmenting a large language model (LLM) to condition on a retrieval distribution, finetuning end-to-end to generate the question that would have maximized the rank of the true candidate at each turn.
  arXiv  Detail & Related papers  (2024-04-28T18:21:31Z)
• Qsnail: A Questionnaire Dataset for Sequential Question Generation [76.616068047362]
  We present the first dataset specifically constructed for the questionnaire generation task, which comprises 13,168 human-written questionnaires. We conduct experiments on Qsnail, and the results reveal that retrieval models and traditional generative models do not fully align with the given research topic and intents. Despite enhancements through the chain-of-thought prompt and finetuning, questionnaires generated by language models still fall short of human-written questionnaires.
  arXiv  Detail & Related papers  (2024-02-22T04:14:10Z)
• A Survey on Interpretable Cross-modal Reasoning [64.37362731950843]
  Cross-modal reasoning (CMR) has emerged as a pivotal area with applications spanning from multimedia analysis to healthcare diagnostics. This survey delves into the realm of interpretable cross-modal reasoning (I-CMR) This survey presents a comprehensive overview of the typical methods with a three-level taxonomy for I-CMR.
  arXiv  Detail & Related papers  (2023-09-05T05:06:48Z)
• Predicting Survey Response with Quotation-based Modeling: A Case Study on Favorability towards the United States [0.0]
  We propose a pioneering approach for predicting survey responses by examining quotations using machine learning. We leverage a vast corpus of quotations from individuals across different nationalities to extract their level of favorability. We employ a combination of natural language processing techniques and machine learning algorithms to construct a predictive model for survey responses.
  arXiv  Detail & Related papers  (2023-05-23T14:11:01Z)
• Releasing survey microdata with exact cluster locations and additional privacy safeguards [77.34726150561087]
  We propose an alternative microdata dissemination strategy that leverages the utility of the original microdata with additional privacy safeguards. Our strategy reduces the respondents' re-identification risk for any number of disclosed attributes by 60-80% even under re-identification attempts.
  arXiv  Detail & Related papers  (2022-05-24T19:37:11Z)
• Questionnaire analysis to define the most suitable survey for port-noise investigation [0.0]
  The paper analyses a sample of questions suitable for the specific research, chosen as part of the wide database of questionnaires internationally proposed for subjective investigations. The questionnaire will be optimized to be distributed in the TRIPLO project (TRansports and Innovative sustainable connections between Ports and LOgistic platforms)
  arXiv  Detail & Related papers  (2020-07-14T08:52:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.