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Related Experiment Video

Updated: Dec 11, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

885

Integrating machine learning and open data into social Chatbot for filtering information rumor.

I-Ching Hsu1, Chun-Cheng Chang1

  • 1Department of Computer Science and Information Engineering, National Formosa University, 64, Wenhua Rd., Huwei Township, Yunlin County 632 Taiwan.

Journal of Ambient Intelligence and Humanized Computing
|August 25, 2020
PubMed
Summary

This study introduces a machine learning and open data cloud computing (MLODCCC) architecture to verify social media information. A Food Safety Information Platform (FSIP) using this model achieved 0.769 accuracy in identifying credible food safety news.

Keywords:
ChatbotCloud computingMachine learningOpen data

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Last Updated: Dec 11, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

885

Area of Science:

  • Computer Science
  • Information Science
  • Public Health

Background:

  • Social networks are primary information dissemination channels, increasingly used for spreading harmful rumors.
  • The proliferation of misinformation on social media poses significant societal challenges and damages.
  • Existing methods for verifying information on social platforms are insufficient to handle the volume and speed of data.

Purpose of the Study:

  • To develop a robust architecture for verifying information authenticity on social media platforms.
  • To create a functional platform for identifying credible food safety information using the proposed architecture.
  • To evaluate the performance of different machine learning algorithms within a cloud computing environment for information verification.

Main Methods:

  • Proposed a general architecture integrating machine learning, open data, a chatbot, and cloud computing (MLODCCC).
  • Developed a Food Safety Information Platform (FSIP) utilizing the MLODCCC architecture with a Facebook chatbot interface.
  • Compared the accuracy of decision tree, logistic regression, and support vector machine algorithms for binary classification in cloud environments.

Main Results:

  • The MLODCCC architecture comprises six integrated modules: cloud computing, machine learning, data preparation, open data, chatbot, and intelligent social application.
  • The developed Food Safety Information Platform (FSIP) demonstrated effective information verification capabilities.
  • Achieved a binary classification accuracy of 0.769, indicating the proposed approach's effectiveness in classifying information authenticity.

Conclusions:

  • The MLODCCC architecture provides a viable framework for enhancing information authenticity verification on social media.
  • The FSIP serves as a practical application, successfully identifying credible food safety information via a user-friendly interface.
  • The study validates the efficacy of machine learning algorithms within cloud computing for combating misinformation, particularly in critical areas like food safety.