Lay perspectives on social distancing and other official recommendations and regulations in the time of COVID-19: a qualitative study of social media posts

Ölçer, Sabahat; Yılmaz-Aslan, Yüce; Brzoska, Patrick

doi:10.21203/rs.3.rs-23164/v2

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…Information has flowed through social networks and unfiltered private networks such as WhatsApp, Facebook, Twitter, YouTube and TikTok (Canovaca de la Fuente, 2020 ;Patel, et al, 2020). The international community warned about the potential growth of 'infoxication' (Ölcer, et al, 2020;Tasnim, et al, 2020); during the pandemic it was called 'infodemia'.…”

Section: Twitter In the Face Of The Pandemicmentioning

confidence: 99%

The COVID-19 infodemic in social media: Political exaggeration and communicative autonomy

Ferré-Pavia,

Abrego,

Ricardez

2023

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This study aims to assess the difficulty of maintaining the interpretative autonomy of communication professionals and citizens, in the face of information about the global pandemic. At the same time, this research analyzes critically the World Health Organization’s accusation of an ’infodemic’; was it confirmed or should it be regarded as political exaggeration? An analysis was made of 15,000 tweets around the world, with more than 1,000 RTs for each one, that circulated from 6 February to 18 March 2020. The results demonstrate that it is not so much possible to speak of infodemic but of a remarkable difficulty in interpreting information, together with a preponderant weight of opinion and emotionality. Academia is responsible for disseminating concepts; corporations, for filtering ethically their content; the political class, for not hiding behind the infodemic to lower the challenge of managing the pandemic.

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Section: Twitter In the Face Of The Pandemicmentioning

confidence: 99%

The COVID-19 infodemic in social media: Political exaggeration and communicative autonomy

Ferré-Pavia,

Abrego,

Ricardez

2023

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Using Natural Language Processing to Explore Mental Health Insights From UK Tweets During the COVID-19 Pandemic: Infodemiology Study

Marshall¹,

Lanyi²,

Green³

et al. 2022

JMIR Infodemiology

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Background There is need to consider the value of soft intelligence, leveraged using accessible natural language processing (NLP) tools, as a source of analyzed evidence to support public health research outputs and decision-making. Objective The aim of this study was to explore the value of soft intelligence analyzed using NLP. As a case study, we selected and used a commercially available NLP platform to identify, collect, and interrogate a large collection of UK tweets relating to mental health during the COVID-19 pandemic. Methods A search strategy comprised of a list of terms related to mental health, COVID-19, and lockdown restrictions was developed to prospectively collate relevant tweets via Twitter’s advanced search application programming interface over a 24-week period. We deployed a readily and commercially available NLP platform to explore tweet frequency and sentiment across the United Kingdom and identify key topics of discussion. A series of keyword filters were used to clean the initial data retrieved and also set up to track specific mental health problems. All collated tweets were anonymized. Results We identified and analyzed 286,902 tweets posted from UK user accounts from July 23, 2020 to January 6, 2021. The average sentiment score was 50%, suggesting overall neutral sentiment across all tweets over the study period. Major fluctuations in volume (between 12,622 and 51,340) and sentiment (between 25% and 49%) appeared to coincide with key changes to any local and/or national social distancing measures. Tweets around mental health were polarizing, discussed with both positive and negative sentiment. Key topics of consistent discussion over the study period included the impact of the pandemic on people’s mental health (both positively and negatively), fear and anxiety over lockdowns, and anger and mistrust toward the government. Conclusions Using an NLP platform, we were able to rapidly mine and analyze emerging health-related insights from UK tweets into how the pandemic may be impacting people’s mental health and well-being. This type of real-time analyzed evidence could act as a useful intelligence source that agencies, local leaders, and health care decision makers can potentially draw from, particularly during a health crisis.

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Using Natural Language Processing to Explore Mental Health Insights From UK Tweets During the COVID-19 Pandemic: Infodemiology Study (Preprint)

Marshall¹,

Lanyi²,

Green³

et al. 2021

Preprint

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BACKGROUND There is increasing need to explore the value of soft-intelligence, leveraged using the latest artificial intelligence (AI) and natural language processing (NLP) techniques, as a source of analysed evidence to support public health research activity and decision-making. OBJECTIVE The aim of this study was to further explore the value of soft-intelligence analysed using AI through a case study, which examined a large collection of UK tweets relating to mental health during the COVID-19 pandemic. METHODS A search strategy comprising a list of terms related to mental health, COVID-19, and lockdown restrictions was developed to prospectively collate relevant tweets via Twitter’s advanced search application programming interface over a 24-week period. We deployed a specialist NLP platform to explore tweet frequency and sentiment across the UK and identify key topics of discussion. A series of keyword filters were used to clean the initial data retrieved and also set up to track specific mental health problems. Qualitative document analysis was carried out to further explore and expand upon the results generated by the NLP platform. All collated tweets were anonymised RESULTS We identified and analysed 286,902 tweets posted from UK user accounts from 23 July 2020 to 6 January 2021. The average sentiment score was 50%, suggesting overall neutral sentiment across all tweets over the study period. Major fluctuations in volume and sentiment appeared to coincide with key changes to any local and/or national social-distancing measures. Tweets around mental health were polarising, discussed with both positive and negative sentiment. Key topics of consistent discussion over the study period included the impact of the pandemic on people’s mental health (both positively and negatively), fear and anxiety over lockdowns, and anger and mistrust toward the government. CONCLUSIONS Through the primary use of an AI-based NLP platform, we were able to rapidly mine and analyse emerging health-related insights from UK tweets into how the pandemic may be impacting people’s mental health and well-being. This type of real-time analysed evidence could act as a useful intelligence source that agencies, local leaders, and health care decision makers can potentially draw from, particularly during a health crisis.

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Lay perspectives on social distancing and other official recommendations and regulations in the time of COVID-19: a qualitative study of social media posts

Cited by 3 publications

References 10 publications

The COVID-19 infodemic in social media: Political exaggeration and communicative autonomy

The COVID-19 infodemic in social media: Political exaggeration and communicative autonomy

Using Natural Language Processing to Explore Mental Health Insights From UK Tweets During the COVID-19 Pandemic: Infodemiology Study

Using Natural Language Processing to Explore Mental Health Insights From UK Tweets During the COVID-19 Pandemic: Infodemiology Study (Preprint)

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