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Measuring the Outreach Efforts of Public Health Authorities and the Public Response on Facebook During the COVID-19 Pandemic in Early 2020: Cross-Country Comparison
1. Turning Discovery into Healthier Communities 1
Turning Discovery into Healthier Communities
Measuring the Outreach Efforts of Public
Health Authorities and the Public
Response on Facebook During the
COVID-19 Pandemic in Early 2020:
Cross-Country Comparison
Aravind Sesagiri Raamkumar, Soon Guan Tan, Hwee Lin Wee
Paper published in Journal of Medical Internet Research (JMIR),
COVID-19 Special Issue
https://doi.org/10.2196/19334
2. Turning Discovery into Healthier Communities 2
Background
COVID-19
Public Health
Authorities
3. Turning Discovery into Healthier Communities 3
Research Objectives
• RQ1: How frequently do the PHAs of Singapore, the US and
England use Facebook for risk communication?
• RQ2: What were the primary themes of the COVID-19 related
posts by PHAs?
• RQ3: What are the Facebook followers’ sentiments and
emotions in response to these COVID-19 related posts by PHAs?
• RQ4: How common are toxic comments that may incite public
unrest and do these toxic comments gain traction?
4. Turning Discovery into Healthier Communities 4
Methods
Extraction of Facebook Posts and
Comments using Facepager
[January 1, 2019, to March 18, 2020]
Manual classification
of Facebook posts into
Themes
Algorithmic identification of Sentiments
and Emotions in Facebook Comments
using VADER and DeepMoji
Algorithmic identification of
Toxicity in Facebook Comments
using Google Perspective API
5. Turning Discovery into Healthier Communities 5
Measures
• Outreach and Engagement
• Frequency
• Mean posts per day (PPD)
• Mean reactions per post (RPP)
• Mean shares per post (SPP)
• Mean comments per post (CPP)
• Responses to the COVID-19 Posts
• Frequency
• Mean sentiment polarity (SP)
• Positive to negative sentiments ratio (PNSR)
• Positive to negative emotions ratio (PNER)
• Toxicity in Comments
• Frequency
• Mean likes per toxic comment (LPTC)
• Mean replies per toxic comment (RPTC)
6. Turning Discovery into Healthier Communities 6
Salient Results – RQ1
• MOH published more COVID-19 posts (n=271, PPD=5.0) compared to CDC
(n=94, PPD=2.2) and PHE (n=45, PPD=1.4)
• CDC COVID-19 posts received more comments (CPP=255.3) compared to
MOH (CPP=15.6) and PHE (CPP=12.5)
7. Turning Discovery into Healthier Communities 7
Theme MOH (n=271) CDC (n=94) PHE (n=45)
Preventive Measures, N (%) 60 (22.1) 50 (53.2) 18 (40.0)
Situation Update, N (%) 78 (28.8) 21 (22.3) 7 (15.6)
Disease Information, N (%) 16 (5.9) 17 (18.1) 17 (37.8)
Public Reassurance, N (%) 32 (11.8) 2 (2.1) 0 (0)
Falsehood Correction, N (%) 16 (5.9) 0 (0) 0 (0)
Others, N (%) 69 (25.5) 4 (4.3) 3 (6.7)
Salient Results – RQ2
• Posts about preventive/safety measures and situation updates were prevalent
across the three PHAs
• MOH posts covered diverse themes
8. Turning Discovery into Healthier Communities 8
Salient Results – RQ3 and RQ4
• Response sentiments for MOH’s COVID-19 posts were largely negative initially
but improved over time as MOH started posting more diverse and timely posts
• Response sentiments for MOH posts (PNSR=0.94) were more favorable
compared to CDC (PNSR=0.57) and PHE (PNSR=0.55) posts
• Toxic comments were rare (0.01%) across all PHAs and such comments did
not receive more attention than non-toxic comments
9. Turning Discovery into Healthier Communities 9
Conclusion
• Extent of Facebook usage for outreach purposes during the
COVID-19 pandemic varied among the three PHAs
• MOH stepped up its outreach efforts on Facebook more
intensively compared to CDC and PHE
• Public sentiments towards the contents posted by the MOH
improved as the pandemic evolved
• This study shows that timely and multi-themed dissemination
of health information helps in improving public response
• PHAs can potentially mine user comments in social media
platforms to identify criticisms and suggestions