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Record W3200537693 · doi:10.2196/30449

Young Adults’ Responses to an African and US-Based COVID-19 Edutainment Miniseries: Real-Time Qualitative Analysis of Online Social Media Engagement

2021· article· en· W3200537693 on OpenAlexvenueno aff
Venetia Baker, Georgia Arnold, Sara Piot, Lesedi Thwala, Judith R. Glynn, James Hargreaves, Isolde Birdthistle

Bibliographic record

VenueJMIR Formative Research · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicMedia Influence and Health
Canadian institutionsnot available
FundersWorld Health Organization
KeywordsGlobeThematic analysisSocial mediaEntertainmentPandemicCoronavirus disease 2019 (COVID-19)Media studiesPsychologyPublic relationsPolitical scienceGeographySociologyQualitative researchMedicineSocial science

Abstract

fetched live from OpenAlex

BACKGROUND: In April 2020, as cases of the novel COVID-19 spread across the globe, MTV Staying Alive Foundation created the educational entertainment miniseries MTV Shuga: Alone Together. In 70 short episodes released daily on YouTube, Alone Together aimed to disseminate timely and accurate information to increase young people's knowledge, motivation, and actions to prevent COVID-19. OBJECTIVE: We sought to identify Alone Together viewer's perspectives on the global COVID-19 pandemic and national lockdowns by examining the words, conversations, experiences, and emotions expressed on social media in response to the Alone Together episodes. We also assessed how viewers used the series and its online community as a source of support during the global pandemic. METHODS: A total of 3982 comments and 70 live chat conversations were extracted from YouTube between April and October 2020 and analyzed through a data-led inductive thematic approach. Aggregated demographic and geographical data were collected using YouTube Analytics. RESULTS: The miniseries had a global reach across 5 continents, with a total of 7.7 million views across MTV Shuga platforms. The series had over 1 million views over 70 episodes on YouTube and an average of 5683 unique viewers per episode on YouTube. The dominant audience was adults under the age of 35 years and women. Across diverse countries such as Nigeria, Ghana, the United States, and the UK, viewers believed that COVID-19 was serious and expressed that it was socially responsible to follow public health measures. Storylines of the series about the impact of self-isolation on mental health, exposure to violence in lockdowns, and restricted employment opportunities due to the pandemic resonated with young viewers. Tuning in to the miniseries provided viewers with reliable information, entertainment, and an online community during an isolating, confusing, and worrying time. CONCLUSIONS: During the first wave of COVID-19, viewers from at least 53 countries connected on social media via the MTV miniseries. The analysis showed how digitally connected people under the age of 35 years, predominantly women, felt compelled to follow COVID-19 safety measures despite the pandemic's impact on their social, educational, and financial needs. Viewers used social media to reach out to fellow viewers for advice, solace, support, and resources. Organizations, governments, and individuals have been forced to innovate during the pandemic to ensure people can access services safely and remotely. This analysis showed that women under 35 years of age were especially receptive to receiving support from online communities and media services. Peer influence and support online can be a powerful public health tool as people have a great capacity to influence each other and shape norms around public health. However, online services are not accessible to everyone, and COVID-19 has increased disparities between digitally connected and unconnected younger adults.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.003
Scholarly communication0.0030.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.207
GPT teacher head0.502
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2021
Admission routes1
Has abstractyes

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