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Probabilistic Multigraph Modeling for Improving the Quality of Crowdsourced Affective Data.

Jianbo Ye1, Jia Li2, Michelle G Newman3

  • 1College of Information Sciences and Technology, The Pennsylvania State University, University Park, PA 16802.

IEEE Transactions on Affective Computing
|October 3, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a probabilistic model to analyze participant reliability and human regularity in crowdsourced emotion studies. It distinguishes individual seriousness from population-wide agreement for robust affective data analysis.

Keywords:
Emotionscrowdsourcinghuman subjectsprobabilistic graphical modelvisual stimuli

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Area of Science:

  • Computational Social Science
  • Affective Computing
  • Human-Computer Interaction

Background:

  • Crowdsourced affective studies face unique challenges due to subjective human responses.
  • Existing methods often focus on participant reliability but neglect population-level human regularity.
  • Understanding both individual response seriousness and group consensus is crucial for affective data.

Purpose of the Study:

  • To propose a probabilistic approach for jointly modeling participant reliability and human regularity in crowdsourced affective studies.
  • To differentiate between individual participant seriousness and the overall consensus within a population.
  • To enable more robust analysis of large-scale affective datasets.

Main Methods:

  • Developed a probabilistic model built upon an agreement multigraph connecting tasks and workers.
  • The model quantifies individual reliability (seriousness of response) and population regularity (agreement with others).
  • Applied the method to analyze emotion and aesthetic assessments from visual stimuli experiments.

Main Results:

  • The proposed model effectively distinguishes between individual reliability and population regularity.
  • Demonstrated the method's capability for in-depth, robust analysis of complex affective data.
  • Provided a framework for understanding consensus in subjective assessments.

Conclusions:

  • The probabilistic approach offers a novel way to model human behavior in crowdsourced affective research.
  • This method enhances the analysis of subjective data, moving beyond simple reliability metrics.
  • It paves the way for more accurate and insightful studies of human emotions and aesthetics.