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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".