Evidence map›Paper›PMID 34469328›Full record

ArticleJMIR formative research2021

Emotional Analysis of Twitter Posts During the First Phase of the COVID-19 Pandemic in Greece: Infoveillance Study.

Styliani Geronikolou, George Drosatos, George Chrousos

Abstract read
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Article in JMIR formative research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Styliani GeronikolouBiomedical Research Foundation of the Academy of Athens, Athens, Greece.ORCID https://orcid.org/0000-0003-1227-5274
George DrosatosInstitute for Language and Speech Processing, Athena Research Center, Xanthi, Greece.ORCID https://orcid.org/0000-0002-8130-5775
George ChrousosUniversity Research Institute of Maternal and Child Health and Precision Medicine, Athens, Greece.ORCID https://orcid.org/0000-0002-3098-5264

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe effectiveness of public health measures depends upon a community's compliance as well as on its positive or negative emotions.

objectiveThe purpose of this study was to perform an analysis of the expressed emotions in English tweets by Greek Twitter users during the first phase of the COVID-19 pandemic in Greece.

methodsThe period of this study was from January 25, 2020 to June 30, 2020. Data collection was performed by using appropriate search words with the filter-streaming application programming interface of Twitter. The emotional analysis of the tweets that satisfied the inclusion criteria was achieved using a deep learning approach that performs better by utilizing recurrent neural networks on sequences of characters. Emotional epidemiology tools such as the 6 basic emotions, that is, joy, sadness, disgust, fear, surprise, and anger based on the Paul Ekman classification were adopted.

resultsThe most frequent emotion that was detected in the tweets was "surprise" at the emerging contagion, while the imposed isolation resulted mostly in "anger" (odds ratio 2.108, 95% CI 0.986-4.506). Although the Greeks felt rather safe during the first phase of the COVID-19 pandemic, their positive and negative emotions reflected a masked "flight or fight" or "fear versus anger" response to the contagion.

conclusionsThe findings of our study show that emotional analysis emerges as a valid tool for epidemiology evaluations, design, and public health strategy and surveillance.

Indexed as

COVID-19emotional analysisemotional contagionepidemiologyGreeceinfodemicsmental healthpandemicTwitter

Identifiers

PMID34469328
PMCPMC8483272

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