Using BERT and Semantic Patterns to Analyze Disease Outbreak Context over Social Network Data

Neelesh Rastogi, Fazel Keshtkar

Abstract

Predicting disease outbreaks has been a focus for various institutions and researchers for many years. However, any models that seemed to get close to resolve this issue have failed to predict potential outbreaks with accuracy over time. For leveraging the social media data effectively, it is crucial to filter out noisy information from the large volume of data flux so that we could better estimate potential disease outbreaks with growing social data. Not satisfied with essential keyword-based filtration, many researchers turn to machine learning for a solution. In this paper, we apply deep learning techniques to address the Tweets classification problem concerning disease outbreak predictions. To achieve this, we curated a labeled corpus of Tweets that reflect different types of disease-related reports, showcasing diverse community sentiment and varied potential usages in emergency responses. Further, we used BERT, a word embedding and deep learning method to apply transfer learning against our curated dataset. Applying BERT showed that it performs better in comparable results to Long short-term memory (LSTM) and outperforming the baseline model on average in terms of accuracy and F-score.

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Paper Citation


in Harvard Style

Rastogi N. and Keshtkar F. (2020). Using BERT and Semantic Patterns to Analyze Disease Outbreak Context over Social Network Data.In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: Cognitive Health IT, ISBN 978-989-758-398-8, pages 854-863. DOI: 10.5220/0009375908540863


in Bibtex Style

@conference{cognitive health it20,
author={Neelesh Rastogi and Fazel Keshtkar},
title={Using BERT and Semantic Patterns to Analyze Disease Outbreak Context over Social Network Data},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: Cognitive Health IT,},
year={2020},
pages={854-863},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009375908540863},
isbn={978-989-758-398-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: Cognitive Health IT,
TI - Using BERT and Semantic Patterns to Analyze Disease Outbreak Context over Social Network Data
SN - 978-989-758-398-8
AU - Rastogi N.
AU - Keshtkar F.
PY - 2020
SP - 854
EP - 863
DO - 10.5220/0009375908540863