Public Sentiment on Governmental COVID-19 Measures in Dutch Social Media

Shihan Wang; Marijn Schraagen; Erik Tjong Kim Sang; Mehdi Dastani

doi:10.18653/v1/2020.nlpcovid19-2.17

Public Sentiment on Governmental COVID-19 Measures in Dutch Social Media

Shihan Wang, Marijn Schraagen, Erik Tjong Kim Sang, Mehdi Dastani

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

Abstract

Public sentiment (the opinion, attitude or feeling that the public expresses) is a factor of interest for government, as it directly influences the implementation of policies. Given the unprecedented nature of the COVID-19 crisis, having an up-to-date representation of public sentiment on governmental measures and announcements is crucial. In this paper, we analyse Dutch public sentiment on governmental COVID-19 measures from text data collected across three online media sources (Twitter, Reddit and Nu.nl) from February to September 2020. We apply sentiment analysis methods to analyse polarity over time, as well as to identify stance towards two specific pandemic policies regarding social distancing and wearing face masks. The presented preliminary results provide valuable insights into the narratives shown in vast social media text data, which help understand the influence of COVID-19 measures on the general public.

Original language	English
Title of host publication	Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at the 2020 Conference on Empirical Methods in Natural Language Processing
Editors	Karin Verspoor, Kevin Bretonnel Cohen, Michael Conway, Berry de Bruijn, Mark Dredze, Rada Mihalcea, Byron Wallace
Publisher	Association for Computational Linguistics
Number of pages	5
DOIs	https://doi.org/10.18653/v1/2020.nlpcovid19-2.17
Publication status	Published - 16 Nov 2020

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2020.nlpcovid19-2.17Final published version, 319 KBLicence: Taverne

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Wang, S., Schraagen, M., Tjong Kim Sang, E., & Dastani, M. (2020). Public Sentiment on Governmental COVID-19 Measures in Dutch Social Media. In K. Verspoor, K. Bretonnel Cohen, M. Conway, B. de Bruijn, M. Dredze, R. Mihalcea, & B. Wallace (Eds.), Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at the 2020 Conference on Empirical Methods in Natural Language Processing Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.nlpcovid19-2.17

Wang, Shihan ; Schraagen, Marijn ; Tjong Kim Sang, Erik et al. / Public Sentiment on Governmental COVID-19 Measures in Dutch Social Media. Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at the 2020 Conference on Empirical Methods in Natural Language Processing. editor / Karin Verspoor ; Kevin Bretonnel Cohen ; Michael Conway ; Berry de Bruijn ; Mark Dredze ; Rada Mihalcea ; Byron Wallace. Association for Computational Linguistics, 2020.

@inproceedings{8d40fb9528b14247bb17773137f7d005,

title = "Public Sentiment on Governmental COVID-19 Measures in Dutch Social Media",

abstract = "Public sentiment (the opinion, attitude or feeling that the public expresses) is a factor of interest for government, as it directly influences the implementation of policies. Given the unprecedented nature of the COVID-19 crisis, having an up-to-date representation of public sentiment on governmental measures and announcements is crucial. In this paper, we analyse Dutch public sentiment on governmental COVID-19 measures from text data collected across three online media sources (Twitter, Reddit and Nu.nl) from February to September 2020. We apply sentiment analysis methods to analyse polarity over time, as well as to identify stance towards two specific pandemic policies regarding social distancing and wearing face masks. The presented preliminary results provide valuable insights into the narratives shown in vast social media text data, which help understand the influence of COVID-19 measures on the general public.",

author = "Shihan Wang and Marijn Schraagen and {Tjong Kim Sang}, Erik and Mehdi Dastani",

year = "2020",

month = nov,

day = "16",

doi = "10.18653/v1/2020.nlpcovid19-2.17",

language = "English",

editor = "Verspoor, {Karin } and {Bretonnel Cohen}, {Kevin } and Conway, {Michael } and {de Bruijn}, {Berry } and Dredze, {Mark } and Mihalcea, {Rada } and Wallace, {Byron }",

booktitle = "Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at the 2020 Conference on Empirical Methods in Natural Language Processing",

publisher = "Association for Computational Linguistics",

address = "United States",

}

Wang, S , Schraagen, M, Tjong Kim Sang, E & Dastani, M 2020, Public Sentiment on Governmental COVID-19 Measures in Dutch Social Media. in K Verspoor, K Bretonnel Cohen, M Conway, B de Bruijn, M Dredze, R Mihalcea & B Wallace (eds), Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at the 2020 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.nlpcovid19-2.17

Public Sentiment on Governmental COVID-19 Measures in Dutch Social Media. / Wang, Shihan ; Schraagen, Marijn; Tjong Kim Sang, Erik et al.
Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at the 2020 Conference on Empirical Methods in Natural Language Processing. ed. / Karin Verspoor; Kevin Bretonnel Cohen; Michael Conway; Berry de Bruijn; Mark Dredze; Rada Mihalcea; Byron Wallace. Association for Computational Linguistics, 2020.

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

TY - GEN

T1 - Public Sentiment on Governmental COVID-19 Measures in Dutch Social Media

AU - Wang, Shihan

AU - Schraagen, Marijn

AU - Tjong Kim Sang, Erik

AU - Dastani, Mehdi

PY - 2020/11/16

Y1 - 2020/11/16

N2 - Public sentiment (the opinion, attitude or feeling that the public expresses) is a factor of interest for government, as it directly influences the implementation of policies. Given the unprecedented nature of the COVID-19 crisis, having an up-to-date representation of public sentiment on governmental measures and announcements is crucial. In this paper, we analyse Dutch public sentiment on governmental COVID-19 measures from text data collected across three online media sources (Twitter, Reddit and Nu.nl) from February to September 2020. We apply sentiment analysis methods to analyse polarity over time, as well as to identify stance towards two specific pandemic policies regarding social distancing and wearing face masks. The presented preliminary results provide valuable insights into the narratives shown in vast social media text data, which help understand the influence of COVID-19 measures on the general public.

AB - Public sentiment (the opinion, attitude or feeling that the public expresses) is a factor of interest for government, as it directly influences the implementation of policies. Given the unprecedented nature of the COVID-19 crisis, having an up-to-date representation of public sentiment on governmental measures and announcements is crucial. In this paper, we analyse Dutch public sentiment on governmental COVID-19 measures from text data collected across three online media sources (Twitter, Reddit and Nu.nl) from February to September 2020. We apply sentiment analysis methods to analyse polarity over time, as well as to identify stance towards two specific pandemic policies regarding social distancing and wearing face masks. The presented preliminary results provide valuable insights into the narratives shown in vast social media text data, which help understand the influence of COVID-19 measures on the general public.

U2 - 10.18653/v1/2020.nlpcovid19-2.17

DO - 10.18653/v1/2020.nlpcovid19-2.17

M3 - Conference contribution

BT - Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at the 2020 Conference on Empirical Methods in Natural Language Processing

A2 - Verspoor, Karin

A2 - Bretonnel Cohen, Kevin

A2 - Conway, Michael

A2 - de Bruijn, Berry

A2 - Dredze, Mark

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A2 - Wallace, Byron

PB - Association for Computational Linguistics

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Wang S , Schraagen M, Tjong Kim Sang E, Dastani M. Public Sentiment on Governmental COVID-19 Measures in Dutch Social Media. In Verspoor K, Bretonnel Cohen K, Conway M, de Bruijn B, Dredze M, Mihalcea R, Wallace B, editors, Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at the 2020 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics. 2020 doi: 10.18653/v1/2020.nlpcovid19-2.17

Public Sentiment on Governmental COVID-19 Measures in Dutch Social Media

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