Facebook Advertising Across an Engagement Spectrum: A Case Example for Public Health Communication
Bibliographic record
Abstract
BACKGROUND: The interpersonal, dialogic features of social networking sites have untapped potential for public health communication. We ran a Facebook advertising campaign to raise statewide awareness of Michigan's newborn screening and biobanking programs. OBJECTIVE: We ran a Facebook advertising campaign to stimulate public engagement on the complex and sensitive issue of Michigan's newborn screening and biobank programs. METHODS: We ran an 11-week, US $15,000 Facebook advertising campaign engaging Michigan Facebook users aged 18-64 years about the state's newborn screening and population biobank programs, and we used a novel "engagement spectrum" framework to contextualize and evaluate engagement outcomes ranging from observation to multi-way conversation. RESULTS: The campaign reached 1.88 million Facebook users, yielding a range of engagement outcomes across ad sets that varied by objective, content, budget, duration, and bid type. Ad sets yielded 9009 page likes (US $4125), 15,958 website clicks (US $5578), and 12,909 complete video views to 100% (US $3750). "Boosted posts" yielded 528 comments and 35,966 page post engagements (US $1500). Overall, the campaign led to 452 shares and 642 comments, including 176 discussing newborn screening and biobanking. CONCLUSIONS: Facebook advertising campaigns can efficiently reach large populations and achieve a range of engagement outcomes by diversifying ad types, bid types, and content. This campaign provided a population-based approach to communication that also increased transparency on a sensitive and complex topic by creating a forum for multi-way interaction.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".