Personal health mention identification from tweets using convolutional neural network

Yue WANG, X. LI, D. Y. MO

Research output: Chapter in Book/Report/Conference proceedingChapters

1 Citation (Scopus)

Abstract

The past decade witnesses the unprecedent growth of social media users worldwide. People express health related outcomes, information, and views on social media platforms. This provides many opportunities to utilize the data source for health monitoring and surveillance, and digital epidemiology in real time. Personal health mention (PHM) is among one of the critical tasks for such purpose. It tries to identify whether a person's health condition is mentioned in a sentence. However, social media texts contain noises, many creative and novel phrases, sarcastic Emoji expressions, and misspellings. This poses challenges to detect PHM from social media text. This paper explores the PHM identification task for six diseases from twitter using convolutional neural network (CNN). Specifically, word embeddings are used to encode the twitter text. Then they are fed into CNN structure to train the classifier for PHM identification. We also explore how the performance of different methods are affected by data imbalance issue and training sample size. Copyright © 2020 IEEE.

Original languageEnglish
Title of host publicationProceedings of 2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020
Place of PublicationUSA
PublisherIEEE
Pages650-654
ISBN (Electronic)9781538672204
DOIs
Publication statusPublished - 2020

Citation

Wang, Y., Li, X., & Mo, D. Y. (2020). Personal health mention identification from tweets using convolutional neural network. In Proceedings of 2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020 (pp. 650-654). IEEE. https://doi.org/10.1109/IEEM45057.2020.9309807

Keywords

  • Deep learning
  • Health monitoring
  • Social media

Fingerprint

Dive into the research topics of 'Personal health mention identification from tweets using convolutional neural network'. Together they form a unique fingerprint.