Regional sentiments in Covid tweets in the Netherlands before and during peak infections

Bas van Leeuwen; Vyacheslav Tykhonov

Regional sentiments in Covid tweets in the Netherlands before and during peak infections

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Abstract

We use numbers of Covid-related tweets over Dutch regions in month 1-9 of 2020. Peaks in tweets precede the peaks of infections by about one month (the exception is June when the corona emergency law (“Spoedwet”) was introduced, which drew a lot of online comments. The reason for this time lag is that more positive sentiments, which resulted in fewer tweets, occurred during peak infections. Just before, more negative sentiments dominated causing more tweets. This positivity in tweets during peak infections has, no doubt, various reasons. Yet, one reason may be in how people value society: the higher the number of infections, the more positive the sentiments related to crucial occupations became, which resulted in fewer tweets. This relation does not hold for non-crucial occupations.

Original language	English
Place of Publication	Munich
Publisher	Munich Personal RePEc Archive
Number of pages	11
Volume	MPRA Paper No. 110879
Publication status	Published - 2021

Publication series

Name	MPRA Paper

Keywords

Sentiment analysis
covid-19
Netherlands

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title = "Regional sentiments in Covid tweets in the Netherlands before and during peak infections",

abstract = "We use numbers of Covid-related tweets over Dutch regions in month 1-9 of 2020. Peaks in tweets precede the peaks of infections by about one month (the exception is June when the corona emergency law (“Spoedwet”) was introduced, which drew a lot of online comments. The reason for this time lag is that more positive sentiments, which resulted in fewer tweets, occurred during peak infections. Just before, more negative sentiments dominated causing more tweets. This positivity in tweets during peak infections has, no doubt, various reasons. Yet, one reason may be in how people value society: the higher the number of infections, the more positive the sentiments related to crucial occupations became, which resulted in fewer tweets. This relation does not hold for non-crucial occupations.",

keywords = "Sentiment analysis, covid-19, Netherlands",

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year = "2021",

language = "English",

volume = "MPRA Paper No. 110879",

series = "MPRA Paper ",

publisher = "Munich Personal RePEc Archive",

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T1 - Regional sentiments in Covid tweets in the Netherlands before and during peak infections

AU - van Leeuwen, Bas

AU - Tykhonov, Vyacheslav

PY - 2021

Y1 - 2021

N2 - We use numbers of Covid-related tweets over Dutch regions in month 1-9 of 2020. Peaks in tweets precede the peaks of infections by about one month (the exception is June when the corona emergency law (“Spoedwet”) was introduced, which drew a lot of online comments. The reason for this time lag is that more positive sentiments, which resulted in fewer tweets, occurred during peak infections. Just before, more negative sentiments dominated causing more tweets. This positivity in tweets during peak infections has, no doubt, various reasons. Yet, one reason may be in how people value society: the higher the number of infections, the more positive the sentiments related to crucial occupations became, which resulted in fewer tweets. This relation does not hold for non-crucial occupations.

AB - We use numbers of Covid-related tweets over Dutch regions in month 1-9 of 2020. Peaks in tweets precede the peaks of infections by about one month (the exception is June when the corona emergency law (“Spoedwet”) was introduced, which drew a lot of online comments. The reason for this time lag is that more positive sentiments, which resulted in fewer tweets, occurred during peak infections. Just before, more negative sentiments dominated causing more tweets. This positivity in tweets during peak infections has, no doubt, various reasons. Yet, one reason may be in how people value society: the higher the number of infections, the more positive the sentiments related to crucial occupations became, which resulted in fewer tweets. This relation does not hold for non-crucial occupations.

KW - Sentiment analysis

KW - covid-19

KW - Netherlands

M3 - Working paper/Discussion paper

VL - MPRA Paper No. 110879

T3 - MPRA Paper

BT - Regional sentiments in Covid tweets in the Netherlands before and during peak infections

PB - Munich Personal RePEc Archive

CY - Munich

ER -

Regional sentiments in Covid tweets in the Netherlands before and during peak infections

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