Understanding how young African adults interact with peer-generated sexual health information on Facebook and uncovering strategies for successful organic engagement
Bibliographic record
Abstract
BACKGROUND: The use of social media for sexual health communication is gaining intense discussion both globally and in Africa. Despite this reality, it remains unclear whether and how young African adults use digital innovations like social media to access sexual health information. More importantly, the unique properties of messages that increase message reach and propagation are not well understood. This study aims to fill the gaps in scholarship by identifying post features and content associated with greater user engagement. METHODS: We analyzed a corpus of 3533 sexual and reproductive health messages shared on a public Facebook group by and for young African adults between June 1, 2018, and May 31, 2019, to understand better the unique features associated with higher engagement with peer-generated sexual health education. Facebook posts were independently classified into thematic categories such as topic, strategy, and tone of communication. RESULTS: The participants generally engaged with posts superficially by liking (x̃ = 54; x̄ = 109.28; σ = 159.24) rather than leaving comments (x̃ = 10; x̄ = 32.03; σ = 62.65) or sharing (x̃ = 3; x̄ = 11.34; σ = 55.12) the wallposts. Messages with fear [IRR:0.75, 95% CI: 0.66-0.86] or guilt [IRR:0.82, 95% CI: 0.72-0.92] appeals received a significantly lower number of reactions compared to neutral messages. Messages requesting an opinion [IRR:4.25, 95% CI: 3.57-5.10] had a significantly higher number of comments compared to status updates. The use of multimedia and storytelling formats were also significantly associated with a higher level of engagement and propagation of sexual health messages on the group. CONCLUSION: Young adults in our sample tend to superficially interact with peer-communicated sexual health information through likes than engage (comments) or propagate such messages. Message features that increase engagements and propagation of messages include multimedia and engaging styles like storytelling. Our findings provide valuable insight and pave the way for the design of effective and context-specific sexual health information use of features that attract young African adults.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".