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This study introduces neural models for text similarity, achieving 90% accuracy in identifying disease-related messages on social media. These models help group and analyze similar information for better crisis response and public health surveillance.

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Area of Science:

  • Computational linguistics
  • Public health informatics
  • Machine learning

Background:

  • Social networks are vital for real-time information dissemination during crises.
  • Analyzing vast amounts of social media data is crucial for public health and disaster management.
  • Existing methods for text similarity may not be optimized for rapid analysis of crisis-related information.

Purpose of the Study:

  • To investigate neural network models for assessing text similarity in social media messages.
  • To develop methods for identifying disease-related content and grouping similar messages.
  • To evaluate the performance of different learning algorithms in calculating message similarity.

Main Methods:

  • Utilized neural network architectures for natural language processing tasks.
  • Implemented algorithms to determine message relatedness to specific topics, such as diseases.
  • Applied text similarity metrics and machine learning for classification and grouping.
  • Trained and evaluated models on a dataset of social media posts, specifically tweets.

Main Results:

  • Achieved 90% accuracy in classifying which of two tweets is more similar to a sample tweet.
  • Demonstrated the effectiveness of neural models in identifying disease-related social media content.
  • Successfully grouped similar messages, facilitating data analysis and storage.

Conclusions:

  • Neural models offer a highly accurate approach to text similarity analysis for social media.
  • This technology can significantly enhance real-time monitoring of public health and crisis situations.
  • The developed methods provide a scalable solution for managing and analyzing large volumes of online information.