How to Build a Facebook Public Engagement List: Tools, Metrics, and Templates

Building a reliable Facebook public engagement list helps you compare performance across public Pages and Groups, track trends, and turn observations into actionable tests. This guide explains what “engagement” means, where to source compliant data, and how to normalize, rank, and QA posts for fair comparisons. Use the templates and workflow below to create a repeatable leaderboard your team can trust.

How to Build a Facebook Public Engagement List: Tools, Metrics, and Templates

If you’re benchmarking competitors, scouting trends, or sharpening your content strategy, a facebook public engagement list is a practical, repeatable asset. In plain terms, it’s a ranked view of public Facebook entities (Pages, public Groups, and sometimes public creator profiles) by the interactions their posts earn, over a defined time window. Done well, it gives you a trustworthy “market scoreboard” to spot winners, compare apples to apples, and turn observations into tests and briefs.

This guide walks you through the definitions, data paths, workflow, automation, QA, and templates to build one responsibly and consistently.

What a public engagement list is and why it matters

A facebook public engagement list ranks public entities and their recent public posts by interactions. It answers questions like:

• Which Pages or Groups are dominating engagement in my niche this week?
• Which post formats, topics, and calls-to-action seem to outperform?
• Where are there gaps we could fill?

Why it matters:

• Benchmarking: Track your position versus peers on an objective cadence.
• Trend-spotting: See what content types, narratives, and formats are taking off.
• Strategy fuel: Turn observed patterns into content tests, briefs, and KPIs.
• Risk awareness: Monitor engagement bait and misinformation patterns in your space.

Define “engagement” and pick metrics

Start with explicit definitions. You want transparency and comparability.

• Core interactions (publicly observable for most posts):
• Reactions (Like, Love, Care, Haha, Wow, Sad, Angry)
• Comments
• Shares
• Additional metrics (often only for your own assets):
• Saves: not publicly exposed; available to Page admins in Insights.
• Clicks / Link clicks: internal only.
• Video views / 3-second / 10-second / ThruPlays: internal only.

Post-level vs. Page-level:

• Post-level engagement aggregates interactions on a single piece of content.
• Page-level engagement sums across posts over a period, or normalizes per-post.

Engagement rate options:

• Interactions per 1,000 followers (publicly derivable for Pages with visible follower counts).
• Interactions per reach or impressions (more accurate, but typically only for your own assets).
• Interactions per post (simple and comparable if post counts are similar).

Time normalization:

• Normalize per 24 hours to compare fresh posts versus older ones fairly.
• Example: interactions_per_24h = total_interactions / age_hours * 24.

Sample formulas you can reuse:

Total_Interactions = Reactions + Comments + Shares

ER_per_1k_Followers (%) = (Total_Interactions / Followers) * 100,000

Interactions_per_24h = Total_Interactions / (MAX(AgeHours, 1)) * 24

Weighted interactions (optional to emphasize deeper engagement):

Weighted_Interactions = (1.0 * Reactions) + (2.0 * Comments) + (3.0 * Shares)

Adjust weights based on your goals (e.g., prioritize conversation with higher weight on comments, or virality with shares).

Data sources and access paths

Choose compliant, reliable sources. Avoid unauthorized scraping; respect platform policies.

• Meta Content Library and API
• Meta’s official solution for eligible researchers, academics, civil society, and certain approved partners to analyze public content at scale.
• Pros: Compliance, comprehensive public coverage, API access for automation (subject to eligibility).
• Cons: Access is restricted; approval may take time; rate limits and policy constraints apply.
• Meta Business Suite / Insights (your own assets)
• Best for validating your internal performance, benchmarking engagement rate using reach or impressions.
• Pros: Accurate reach/impressions, saves, clicks, and video view metrics.
• Cons: Not usable for competitor internals; only public counts are visible externally.
• Third-party tools (check access, features, and compliance)
• Examples: NewsWhip, Brandwatch, BuzzSumo, Emplifi, Sprout Social.
• Pros: Speed, dashboards, historical data, exports, alerts, benchmarks.
• Cons: Coverage and metrics depend on vendor integrations and contracts; may not include all entities or formats.
• Manual collection from public pages and groups
• Pros: Zero cost, high fidelity for visible counts, great for small scopes.
• Cons: Labor-intensive, error-prone, hard to scale, and may miss backfilled interactions.

Note: Access to public content via APIs requires specific permissions and adherence to Meta’s Terms. Do not scrape or automate in ways that violate policies.

Scope the list so comparisons are fair

Clarity in scope is the difference between useful and misleading.

• Niche/vertical: e.g., “US consumer tech news,” “Premier League clubs,” “Plant-based recipes.”
• Geography: Country or region; consider local Pages versus global ones.
• Language: Filter both entities and posts (if tools allow) to maintain relevance.
• Entity types: Pages, public Groups, verified/creator profiles with public posts.
• Surfaces: Feed posts, native video, Live, Reels; be explicit about what’s included.
• Time window: Common options are 24 hours, 72 hours, 7 days, 28 days.
• Post maturity: Normalize by hours live; optionally cap to “posts younger than 7 days.”
• Exclusions: Branded content, ads/sponsored posts, or cross-posts if they distort comparisons.

Step-by-step build (manual workflow)

For a lean, compliant build without special API access:

1. Compile a seed entity list

• Start with 20–100 Pages/Groups in your niche.
• Include official handles, URLs, follower counts, and location/language tags.

1. Gather recent public posts

• For each entity, open the Page/Group and capture the last n posts within your time window.
• Record for each post: URL, timestamp, format (link, photo, video, Reel, Live), visible reactions, comments, shares.
• Note: Saves, reach, and impressions aren’t public; skip them unless it’s your own content.

1. Deduplicate cross-posts

• Some Pages cross-post identical videos or share the same link across sister Pages.
• Use URL canonicalization (landing page URL) or video IDs (if visible in the player details) to mark duplicates.

1. Compute totals and normalized scores

• Total interactions = reactions + comments + shares.
• Normalize per 24 hours to control for age.
• Optionally compute ER per 1k followers using the entity’s follower count (snapshot at time of collection).

1. Apply a simple weighting (optional)

• If conversation matters: weight comments higher.
• If virality matters: weight shares higher.
• Keep weights documented and stable across reports for comparability.

1. Rank

• Choose a primary ranking metric: interactions per 24h (post-level) or total interactions (page-level).
• Break ties with secondary metrics like comments or ER per 1k.

1. Annotate

• Tag topic, format, hook/CTA, and whether it looks like branded content or engagement bait.
• Add notes for context (e.g., timely news, celebrity feature, a giveaway).

Example Google Sheets formulas:

=SUM([@Reactions], [@Comments], [@Shares])

=IF([@Age_Hours]<1, [@Total_Interactions]*24, [@Total_Interactions]/[@Age_Hours]*24)

=IF([@Followers]>0, ([@Total_Interactions]/[@Followers])*100000, )

Weighted interactions:

=1*[@Reactions] + 2*[@Comments] + 3*[@Shares]

Automation options (for scale)

When your scope grows, automate responsibly.

• Scheduled exports from approved tools
• Many vendors let you schedule CSV exports to email, S3, or Google Drive.
• Import to a central Sheet or data warehouse.
• API pipelines
• If eligible for Meta Content Library/API or vendor APIs, build a small job (e.g., Airflow, cron) to pull posts and metrics.
• Land data in BigQuery, Snowflake, PostgreSQL, or Sheets.
• Basic QA checks
• Nulls/missing timestamps
• Negative counts or >99th percentile spikes
• Duplicates by URL or content hash
• Time zone alignment to your reporting standard (e.g., UTC)
• Alerting for spikes
• Send Slack/Email alerts when a post’s interactions_per_24h exceeds a threshold or z-score.
• Flag sustained surges to review for virality vs. misinformation.
• Compliance first
• Only use approved APIs and data partners.
• Do not bypass technical controls; follow Meta’s Developer Policies and ToS.

Example SQL for normalized scoring (conceptual):

SELECT
  p.entity_id,
  p.post_url,
  p.timestamp,
  p.format,
  p.reactions + p.comments + p.shares AS total_interactions,
  (p.reactions + p.comments + p.shares) / GREATEST(EXTRACT(EPOCH FROM (NOW() - p.timestamp))/3600, 1) * 24 AS interactions_per_24h,
  CASE WHEN e.followers > 0 THEN ((p.reactions + p.comments + p.shares) / e.followers) * 100000 END AS er_per_1k
FROM posts p
JOIN entities e ON e.entity_id = p.entity_id
WHERE p.timestamp >= NOW() - INTERVAL '7 days';

Quality control and bias checks

Engagement is not the same as quality or truth. Bake in safeguards.

• Engagement bait
• Detect giveaways, “comment YES,” and other bait tactics. Tag and optionally exclude.
• Misinformation and harmful content
• Route flagged posts for human review. Consider an allowlist of reputable entities.
• Paid vs. organic
• Watch for “Sponsored” or “Paid partnership” labels. Public data may not capture dark posts; note this limitation.
• Time zones
• Normalize timestamps to a single zone (e.g., UTC). Display local time where needed for stakeholders.
• Anomalies
• Investigate sudden spikes, deleted posts, and backfilled interactions (especially on videos).
• Format parity
• Reels vs. link posts behave differently. Consider separate leaderboards by format to avoid skew.
• Survivorship bias
• Don’t only log viral posts; consistent sampling across all posts in the window gives a truer picture.

Turn the list into insights

A list is a starting point. Convert it into decisions and experiments.

• Winning formats
• Are short Reels with captions outperforming link posts? Are quote cards working?
• Posting cadence and timing
• What days/hours correlate with interactions_per_24h peaks in your niche?
• Creator collaborations
• Which posts with co-creators or cross-posts show lifts?
• Seasonality and events
• Map spikes against holidays, launches, and industry events.
• Content gaps
• Topics your audience cares about that competitors dominate but you don’t cover yet.
• Test design
• Turn observations into A/B tests: hook lines, thumbnails, aspect ratios, length, CTA language.
• KPIs and briefs
• Update briefs with “what good looks like” benchmarks (e.g., top quartile ER per 1k followers).
• Align team KPIs with normalized metrics to avoid time/size confounds.

Templates, columns, and cadence

Use a consistent schema so you can trend over time and compare across teams.

Recommended columns:

Ranking formula options:

• Primary: Interactions per 24h (post-level), then ER per 1k as tiebreaker.
• Alternative: Weighted interactions per 24h if emphasizing conversations or shares.
• Page-level: Sum of Interactions per 24h across posts in the window, or median to reduce outlier effects.

Sheets examples:

Primary_Rank = RANK.EQ([@Interactions_per_24h], [Interactions_per_24h_Column], 0)

Tiebreaker = RANK.EQ([@ER_per_1k], [ER_per_1k_Column], 0)

Cadence:

• Weekly (recommended): Fresh enough for action, stable enough for trend detection.
• Daily (for newsroom/fast niches): Add stronger normalization and spike alerts.
• Monthly (strategic reviews): Pivot by topic, format, and creator collabs.

Practical tips and gotchas

• Keep a changelog: Document scope changes, new entities, or formula tweaks. It preserves comparability.
• Separate leaderboards: Run separate tabs for formats (e.g., Reels vs. Link posts).
• Track post count: Volume matters; compute interactions per post at the Page-level to avoid volume bias.
• Snapshot follower counts: Followers change; capture them at collection time for accurate ER.
• Recalculate after 24–48 hours: Videos and Reels may accumulate interactions over longer tails. Consider a T+24 and T+72 snapshot.

Final note on compliance

Building a facebook public engagement list should never rely on scraping or unapproved automation. Use Meta’s approved access paths, vendor tools with clear licenses, and manual collection where needed. Always cite limitations when sharing insights, especially around paid vs. organic separation and the absence of reach/impressions for competitor posts.

With clear definitions, careful scoping, and a light but reliable workflow, you’ll have a leaderboard that your team trusts—and that translates into smarter creative, planning, and results.

Summary

• Define engagement clearly, scope your niche and window, collect and deduplicate posts, then normalize (e.g., per 24 hours, ER per 1k) before ranking and annotating.
• Use compliant data sources, automate with approved APIs or scheduled exports, and apply QA plus bias checks for trustworthy comparisons.
• Maintain a consistent template and cadence so insights flow into tests, briefs, and KPIs your team can act on with confidence.