@inproceedings{sampath-kumar-etal-2022-automatic,
title = "Automatic Identification of 5{C} Vaccine Behaviour on Social Media",
author = "Sampath Kumar, Ajay Hemanth and
Shausan, Aminath and
Demartini, Gianluca and
Rahimi, Afshin",
booktitle = "Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022)",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wnut-1.15",
pages = "136--146",
abstract = "Monitoring vaccine behaviour through social media can guide health policy. We present a new dataset of 9471 tweets posted in Australia from 2020 to 2022, annotated with sentiment toward vaccines and also 5C, the five types of behaviour toward vaccines, a scheme commonly used in health psychology literature. We benchmark our dataset using BERT and Gradient Boosting Machine and show that jointly training both sentiment and 5C tasks (F1=48) outperforms individual training (F1=39) in this highly imbalanced data. Our sentiment analysis indicates close correlation between the sentiments and prominent events during the pandemic. We hope that our dataset and benchmark models will inform further work in online monitoring of vaccine behaviour. The dataset and benchmark methods are accessible online.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="sampath-kumar-etal-2022-automatic">
<titleInfo>
<title>Automatic Identification of 5C Vaccine Behaviour on Social Media</title>
</titleInfo>
<name type="personal">
<namePart type="given">Ajay</namePart>
<namePart type="given">Hemanth</namePart>
<namePart type="family">Sampath Kumar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Aminath</namePart>
<namePart type="family">Shausan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Gianluca</namePart>
<namePart type="family">Demartini</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Afshin</namePart>
<namePart type="family">Rahimi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2022-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022)</title>
</titleInfo>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Gyeongju, Republic of Korea</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Monitoring vaccine behaviour through social media can guide health policy. We present a new dataset of 9471 tweets posted in Australia from 2020 to 2022, annotated with sentiment toward vaccines and also 5C, the five types of behaviour toward vaccines, a scheme commonly used in health psychology literature. We benchmark our dataset using BERT and Gradient Boosting Machine and show that jointly training both sentiment and 5C tasks (F1=48) outperforms individual training (F1=39) in this highly imbalanced data. Our sentiment analysis indicates close correlation between the sentiments and prominent events during the pandemic. We hope that our dataset and benchmark models will inform further work in online monitoring of vaccine behaviour. The dataset and benchmark methods are accessible online.</abstract>
<identifier type="citekey">sampath-kumar-etal-2022-automatic</identifier>
<location>
<url>https://aclanthology.org/2022.wnut-1.15</url>
</location>
<part>
<date>2022-10</date>
<extent unit="page">
<start>136</start>
<end>146</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Automatic Identification of 5C Vaccine Behaviour on Social Media
%A Sampath Kumar, Ajay Hemanth
%A Shausan, Aminath
%A Demartini, Gianluca
%A Rahimi, Afshin
%S Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022)
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F sampath-kumar-etal-2022-automatic
%X Monitoring vaccine behaviour through social media can guide health policy. We present a new dataset of 9471 tweets posted in Australia from 2020 to 2022, annotated with sentiment toward vaccines and also 5C, the five types of behaviour toward vaccines, a scheme commonly used in health psychology literature. We benchmark our dataset using BERT and Gradient Boosting Machine and show that jointly training both sentiment and 5C tasks (F1=48) outperforms individual training (F1=39) in this highly imbalanced data. Our sentiment analysis indicates close correlation between the sentiments and prominent events during the pandemic. We hope that our dataset and benchmark models will inform further work in online monitoring of vaccine behaviour. The dataset and benchmark methods are accessible online.
%U https://aclanthology.org/2022.wnut-1.15
%P 136-146
Markdown (Informal)
[Automatic Identification of 5C Vaccine Behaviour on Social Media](https://aclanthology.org/2022.wnut-1.15) (Sampath Kumar et al., WNUT 2022)
ACL
- Ajay Hemanth Sampath Kumar, Aminath Shausan, Gianluca Demartini, and Afshin Rahimi. 2022. Automatic Identification of 5C Vaccine Behaviour on Social Media. In Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022), pages 136–146, Gyeongju, Republic of Korea. Association for Computational Linguistics.