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

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 July 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.

[1]  Viviana Cotik,et al.  A study of Hate Speech in Social Media during the COVID-19 outbreak , 2020 .

[2]  Md. Mokhlesur Rahman,et al.  COVID-19 Public Sentiment Insights and MachineLearning for Tweets Classification , 2020, medRxiv.

[3]  Qiang Chen,et al.  Unpacking the black box: How to promote citizen engagement through government social media during the COVID-19 crisis , 2020, Computers in Human Behavior.

[4]  Cornelia Caragea,et al.  Multi-Task Stance Detection with Sentiment and Stance Lexicons , 2019, EMNLP.

[5]  Guido Zarrella,et al.  MITRE at SemEval-2016 Task 6: Transfer Learning for Stance Detection , 2016, *SEMEVAL.

[6]  P. Burstein The Impact of Public Opinion on Public Policy: A Review and an Agenda , 2003 .

[7]  Walter Daelemans,et al.  Pattern for Python , 2012, J. Mach. Learn. Res..

[8]  Takao Terano,et al.  Detecting rumor patterns in streaming social media , 2015, 2015 IEEE International Conference on Big Data (Big Data).

[9]  Yang Li,et al.  Interpreting the Public Sentiment Variations on Twitter , 2014, IEEE Transactions on Knowledge and Data Engineering.

[10]  Huilan Xu,et al.  Chinese Public's Attention to the COVID-19 Epidemic on Social Media: Observational Descriptive Study , 2020, Journal of Medical Internet Research.

[11]  Emily K. Vraga,et al.  A first look at COVID-19 information and misinformation sharing on Twitter , 2020, ArXiv.

[12]  Matteo Cinelli,et al.  The COVID-19 social media infodemic , 2020, Scientific reports.

[13]  G. Eysenbach,et al.  Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak , 2010, PloS one.

[14]  Huilan Xu,et al.  Chinese Public's Attention to the COVID-19 Epidemic on Social Media: Observational Descriptive Study (Preprint) , 2020 .

[15]  Preslav Nakov,et al.  Unsupervised User Stance Detection on Twitter , 2019, ICWSM.

[16]  Hung-Yu Kao,et al.  IKM at SemEval-2017 Task 8: Convolutional Neural Networks for stance detection and rumor verification , 2017, *SEMEVAL.

[17]  Jabra Zarka,et al.  Coronavirus Goes Viral: Quantifying the COVID-19 Misinformation Epidemic on Twitter , 2020, Cureus.

[18]  Tomas Mikolov,et al.  Bag of Tricks for Efficient Text Classification , 2016, EACL.

[19]  Alaa Abd-Alrazaq,et al.  Top Concerns of Tweeters During the COVID-19 Pandemic: Infoveillance Study , 2020, Journal of medical Internet research.

[20]  Tomas Mikolov,et al.  Enriching Word Vectors with Subword Information , 2016, TACL.