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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Understanding Weekly COVID-19 Concerns through Dynamic Content-Specific LDA Topic Modeling.

Mohammadzaman Zamani1, H Andrew Schwartz1, Johannes Eichstaedt2

  • 1Stony Brook University.

Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing
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Summary
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This study introduces a dynamic topic modeling technique to track evolving public concerns about COVID-19 on social media. The method offers more coherent topics and better prediction of societal impacts like unemployment.

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Area of Science:

  • Computational Social Science
  • Public Health Informatics
  • Natural Language Processing

Background:

  • The COVID-19 pandemic caused rapid societal shifts, altering public perceptions and concerns.
  • Social media discourse reflects these evolving public sentiments and reactions to policy changes.

Purpose of the Study:

  • To develop a dynamic, content-specific Latent Dirichlet Allocation (LDA) topic modeling technique.
  • To identify and track distinct domains of COVID-19 related discourse on social media.
  • To assess the utility of these topics in understanding societal shifts and predicting outcomes.

Main Methods:

  • Proposed a dynamic content-specific LDA topic modeling approach.
  • Applied the technique to analyze social media discourse related to the COVID-19 pandemic.
  • Compared model-derived topics with standard LDA for coherence and predictive power.

Main Results:

  • The dynamic content-specific LDA model generated more coherent topics than standard LDA.
  • Derived topics provided valuable features for predicting COVID-19 related outcomes.
  • Successfully tracked shifts in public concerns and views through social media discourse analysis.

Conclusions:

  • Dynamic content-specific LDA is effective for analyzing evolving public discourse during health crises.
  • This approach enhances understanding of societal impacts and aids in predicting related outcomes.
  • Social media analysis using this method offers insights into public perception shifts during pandemics.