Twitter Data Augmentation for Monitoring Public Opinion on COVID-19 Intervention Measures.

Lin Miao; Mark Last; Marina Litvak

doi:10.18653/v1/2020.nlpcovid19-2.19

Twitter Data Augmentation for Monitoring Public Opinion on COVID-19 Intervention Measures.

Lin Miao, Mark Last, Marina Litvak

Department of Software and Information Systems Engineering

Ben-Gurion University of the Negev

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The COVID-19 outbreak is an ongoing worldwide pandemic that was announced as a global health crisis in March 2020.
Due to the enormous challenges and high stakes of this pandemic, governments have implemented a wide range of policies aimed at containing the spread of the virus and its negative effect on multiple aspects of our life. Public responses to various intervention measures imposed over time can be explored by analyzing the social media. Due to the shortage of available labeled data for this new and evolving domain, we apply data distillation methodology to labeled datasets from related tasks and a very small manually labeled dataset. Our experimental results show that data distillation outperforms other data augmentation methods on our task.

Original language	American English
Title of host publication	Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020
Number of pages	7
DOIs	https://doi.org/10.18653/v1/2020.nlpcovid19-2.19
State	Published - Dec 2020

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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10.18653/v1/2020.nlpcovid19-2.19

Cite this

@inproceedings{649ce7741f5a4663917093465f3ebf0e,

title = "Twitter Data Augmentation for Monitoring Public Opinion on COVID-19 Intervention Measures.",

abstract = "The COVID-19 outbreak is an ongoing worldwide pandemic that was announced as a global health crisis in March 2020. Due to the enormous challenges and high stakes of this pandemic, governments have implemented a wide range of policies aimed at containing the spread of the virus and its negative effect on multiple aspects of our life. Public responses to various intervention measures imposed over time can be explored by analyzing the social media. Due to the shortage of available labeled data for this new and evolving domain, we apply data distillation methodology to labeled datasets from related tasks and a very small manually labeled dataset. Our experimental results show that data distillation outperforms other data augmentation methods on our task.",

author = "Lin Miao and Mark Last and Marina Litvak",

note = "DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.",

year = "2020",

month = dec,

doi = "10.18653/v1/2020.nlpcovid19-2.19",

language = "American English",

booktitle = "Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020",

}

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T1 - Twitter Data Augmentation for Monitoring Public Opinion on COVID-19 Intervention Measures.

AU - Miao, Lin

AU - Last, Mark

AU - Litvak, Marina

N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

PY - 2020/12

Y1 - 2020/12

N2 - The COVID-19 outbreak is an ongoing worldwide pandemic that was announced as a global health crisis in March 2020. Due to the enormous challenges and high stakes of this pandemic, governments have implemented a wide range of policies aimed at containing the spread of the virus and its negative effect on multiple aspects of our life. Public responses to various intervention measures imposed over time can be explored by analyzing the social media. Due to the shortage of available labeled data for this new and evolving domain, we apply data distillation methodology to labeled datasets from related tasks and a very small manually labeled dataset. Our experimental results show that data distillation outperforms other data augmentation methods on our task.

AB - The COVID-19 outbreak is an ongoing worldwide pandemic that was announced as a global health crisis in March 2020. Due to the enormous challenges and high stakes of this pandemic, governments have implemented a wide range of policies aimed at containing the spread of the virus and its negative effect on multiple aspects of our life. Public responses to various intervention measures imposed over time can be explored by analyzing the social media. Due to the shortage of available labeled data for this new and evolving domain, we apply data distillation methodology to labeled datasets from related tasks and a very small manually labeled dataset. Our experimental results show that data distillation outperforms other data augmentation methods on our task.

U2 - 10.18653/v1/2020.nlpcovid19-2.19

DO - 10.18653/v1/2020.nlpcovid19-2.19

M3 - Conference contribution

BT - Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020

ER -

Twitter Data Augmentation for Monitoring Public Opinion on COVID-19 Intervention Measures.

Abstract

UN SDGs

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