Syndromic surveillance with the use of Internet data has been used to track and forecast epidemics
for the last two decades, using different sources from social media to search engine records. More recently,
studies have addressed how the World Wide Web could be used as a valuable source for analysing the reactions of
the public to outbreaks and revealing emotions and sentiment impact from certain events, notably that of
pandemics.
Objective: The objective of this research is to evaluate the capability of Twitter messages (tweets) in estimating the
sentiment impact of COVID-19 cases in Greece in real time as related to cases.
Methods: 153,528 tweets were gathered from 18,730 Twitter users totalling 2,840,024 words for exactly one year
and were examined towards two sentimental lexicons: one in English language translated into Greek (using the
Vader library) and one in Greek. We then used the specific sentimental ranking included in these lexicons to track
i) the positive and negative impact of COVID-19 and ii) six types of sentiments: Surprise, Disgust, Anger, Happiness,
Fear and Sadness and iii) the correlations between real cases of COVID-19 and sentiments and correlations between sentiments and the volume of data.
Results: Surprise (25.32%) mainly and secondly Disgust (19.88%) were found to be the prevailing sentiments of
COVID-19. The correlation coefficient (R2
) for the Vader lexicon is − 0.07454 related to cases and − 0.,70668 to
the tweets, while the other lexicon had 0.167387 and − 0.93095 respectively, all measured at significance level of
p < 0.01. Evidence shows that the sentiment does not correlate with the spread of COVID-19, possibly since the
interest in COVID-19 declined after a certain time
