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Authors: Neelesh Rastogi and Fazel Keshtkar

Affiliation: St. John’s University, 8000 Utopia Pkwy, Queens, New York 11439, U.S.A.

Keyword(s): Social Network Streams, Classification, Neural Networks, BERT, Word Embedding, Topic Modelling, Emergency Response, Sentiment Analysis, Disease Ontology.

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. (More)

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Paper citation in several formats:
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 - Cognitive Health IT; ISBN 978-989-758-398-8; ISSN 2184-4305, SciTePress, pages 854-863. DOI: 10.5220/0009375908540863

@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 - Cognitive Health IT},
year={2020},
pages={854-863},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009375908540863},
isbn={978-989-758-398-8},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Cognitive Health IT
TI - Using BERT and Semantic Patterns to Analyze Disease Outbreak Context over Social Network Data
SN - 978-989-758-398-8
IS - 2184-4305
AU - Rastogi, N.
AU - Keshtkar, F.
PY - 2020
SP - 854
EP - 863
DO - 10.5220/0009375908540863
PB - SciTePress