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Research Bibliography — Viraltest v2
Every constant and design decision in Viraltest is backed by a verifiable source. This document groups sources by quality tier so any reviewer can audit our claims.
Source quality bar
| Tier | Criteria | Example |
|---|---|---|
| T1 — Peer-reviewed | Published in a journal or arXiv with disclosed methodology, sample, and peer review | Van Dongen 2003 Sleep |
| T2 — Industry research | Named org, disclosed methodology, sample ≥100K data points | Buffer 9.6M post study |
| T3 — Official platform | Public statement by platform leadership | Adam Mosseri, Head of Instagram |
| T4 — Survey (cite with caveat) | Named org, disclosed sample, no external audit | Awin 2024 (n=300+) |
| T5 — Rejected | SEO/affiliate blog, no methodology, no auditable sample | Not cited |
Tier 1 — Peer-reviewed
Van Dongen HPA, Maislin G, Mullington JM, Dinges DF (2003)
Title: The cumulative cost of additional wakefulness: dose-response effects on neurobehavioral functions and sleep physiology from chronic sleep restriction and total sleep deprivation
Venue: Sleep 26(2):117–126 (Oxford University Press) Type: Randomized controlled trial PMID: 12683469 DOI: 10.1093/sleep/26.2.117 Sample: n=48 healthy adults (ages 21–38), laboratory conditions, 14 consecutive days
Methodology: Subjects randomized to 4h, 6h, or 8h time-in-bed per night for 14 days, or 0h for 3 days. Continuous behavioral/physiological monitoring. Performance measured via psychomotor vigilance task (PVT), digit symbol substitution, serial addition/subtraction.
Key finding: Lapses in behavioral alertness were near-linearly related to cumulative wakefulness exceeding 15.84 hours (SE 0.73h), regardless of whether deprivation was chronic or total. 6h sleep/night for 14 days produced deficits equivalent to 1–2 nights of total sleep deprivation. Subjects were largely unaware of their impairment.
What we use: SLEEP_OPTIMAL_AWAKE = 16 (rounded from 15.84). Piecewise-linear quality decay: no loss below 16h awake, then SLEEP_LINEAR_DECAY_PER_HOUR = 0.0625 (reaches ~50% at 24h), floor at SLEEP_MIN_QUALITY = 0.30.
Cen Y et al. (2024)
Title: Algorithmic Content Selection and the Impact of User Disengagement Venue: arXiv 2410.13108 (v2, Feb 2025) Type: Theoretical (multi-armed bandit model with user engagement states)
Methodology: Introduces a content selection model where users have k engagement levels. Derives O(k²) dynamic programming for optimal policy. Proves no-regret online learning guarantees.
Key finding: Content maximizing immediate reward is not necessarily optimal for sustained engagement. Higher friction (reduced re-engagement likelihood) counterintuitively leads to higher engagement under optimal policies. Modified demand elasticity captures how satisfaction changes affect long-term revenue.
What we use: Justifies tiered fatigue model (FATIGUE_TIERS) — over-posting creates diminishing returns, not a cliff. Also informs the ALGORITHM_PENALTY mechanic.
Aouali I et al. (2024)
Title: System-2 Recommenders: Disentangling Utility and Engagement in Recommendation Systems via Temporal Point-Processes Venue: arXiv 2406.01611 Type: Theoretical + synthetic experiments
Methodology: Generative model where user return probability depends on Hawkes process with System-1 (impulse) and System-2 (utility) components. Proves identifiability of utility from engagement data.
Key finding: Pure engagement-driven optimization ≠ user utility. Utility-driven interactions have lasting return effects; impulse-driven interactions vanish rapidly. Platforms can disentangle the two from return-probability data.
What we use: Informs the Mosseri-aligned reward decomposition (watch_time ≈ System-1 impulse; saves ≈ System-2 utility). Validates splitting engagement into distinct signals rather than a single float.
Yu Y et al. (2024)
Title: Uncovering the Interaction Equation: Quantifying the Effect of User Interactions on Social Media Homepage Recommendations Venue: arXiv 2407.07227 Type: Empirical (controlled experiments on YouTube, Reddit, X)
Key finding: Platform algorithms respond to user interactions by adjusting content distribution. Evidence of topic deprioritization when engagement drops. Inactivity leads to reduced content surfacing.
What we use: FOLLOWER_DECAY_HOURS = 72 and ALGORITHM_PENALTY scaling with gap length.
Lin Y et al. (2024)
Title: Unveiling User Satisfaction and Creator Productivity Trade-Offs in Recommendation Platforms Venue: arXiv 2410.23683 Type: Theoretical + empirical
Key finding: Relevance-driven recommendation boosts short-term satisfaction but harms long-term content richness. Explorative policy slightly lowers satisfaction but promotes content production volume.
What we use: Justifies multi-episode brand persistence — the creator's long-term niche identity matters more than per-post optimization.
Cao X, Wu Y, Cheng B et al. (2024)
Title: An investigation of the social media overload and academic performance Venue: Education and Information Technologies 29:10303–10328 (Springer) DOI: 10.1007/s10639-023-12213-6 Sample: n=249 university students, survey Type: Quantitative survey study
Key finding: Techno-invasion and techno-overload create psychological stress → exhaustion → perceived irreplaceability → reduced performance. Social support partially buffers the effect.
What we use: burnout_risk observation field — exhaustion accumulates gradually (not binary), mirrors the stress→exhaustion→performance pathway.
Wen J, Wang H, Chen H (2026)
Title: Research on the formation mechanism of social media burnout among college students based on the ISM-MICMAC model Venue: Scientific Reports (Nature) DOI: 10.1038/s41598-026-42958-2 Sample: 8 experts (Delphi method), 58 papers reviewed, 15 factors identified
Key finding: Algorithm recommendations and social comparison are the root-level structural drivers of burnout. Platform-technical mechanisms exert high driving power over subsequent overloads.
What we use: Contextualizes the burnout_risk mechanic — algorithm pressure (our trending/saturation system) is a documented root cause.
Tier 2 — Industry research (methodology disclosed, large N)
Buffer (2026) — Best Time to Post on Instagram
URL: buffer.com/resources/when-is-the-best-time-to-post-on-instagram Sample: 9.6 million posts Methodology: Engagement data aggregated by hour and day of week across Buffer users. Times in local timezone.
Key findings: Peak: Thu 9am, Wed 12pm, Wed 6pm. Evenings 6–11pm strongest overall. Fri/Sat weakest. Wed best overall day.
What we use: server/data/hour_heatmap.json — 7×24 multiplier grid.
Buffer (2026) — How Often to Post on Instagram
URL: buffer.com/resources/how-often-to-post-on-instagram Sample: 2.1 million posts, 102K accounts Methodology: Julian Goldie analyzed posting frequency buckets (0, 1–2, 3–5, 6–9, 10+/week) vs follower growth and reach per post.
Key findings: 3–5 posts/week doubles follower growth vs 1–2. 7+/week shows 20–35% engagement drop per post. Diminishing returns above 5/week.
What we use: FATIGUE_TIERS, WEEKLY_FATIGUE_THRESHOLD = 7, _theoretical_max_engagement uses 5 posts/week × 4 weeks.
Sprout Social (2025) — The Sprout Social Index Edition XX
URL: sproutsocial.com/insights/index Sample: 4,044 consumers, 900 practitioners, 322 leaders (US/UK/Canada/Australia) Methodology: Online survey by Glimpse, Sept 13–27, 2024. Representative sampling.
What we use: Audience preference context for audience_segments.json.
Sprout Social (2026) — Best Times to Post on Social Media
URL: sproutsocial.com/insights/best-times-to-post-on-social-media Sample: ~2 billion engagements, 307,000 social profiles, 30K customers Period: Nov 27, 2025 – Feb 27, 2026 Methodology: Internal Data Science team analysis. All times in local time.
Key findings: IG peaks: Mon 2–4pm, Tue 1–7pm, Wed 12–9pm, Thu 12–2pm. Weekends worst.
What we use: Cross-validates hour_heatmap.json. FOLLOWER_DECAY_HOURS informed by their reporting that reach decline starts after 3–4 days inactivity.
Rival IQ (2025) — Social Media Industry Benchmark Report
URL: rivaliq.com/blog/social-media-industry-benchmark-report Sample: 1.9 million IG posts, 2,100 brands (150 per industry × 14 industries) Methodology: Engagement = (likes + comments + shares + reactions) / followers. Median performance per industry. Companies with 25K–1M FB followers, >5K IG followers.
Key findings by industry (IG): Higher Ed 2.10%, Sports 1.30%, Tech 0.33%, Food 0.37%, Fashion 0.14%.
What we use: _NICHE_MULTIPLIERS in topics.json. Normalized by dividing by median (1.53) to create relative multipliers.
Hootsuite (2025) — Social Trends Report 2025
URL: hootsuite.com/research/social-trends Type: Annual industry report
Key finding: Optimal posting frequency 3–5/week for IG. 48–72 posts/week across all platforms for brands. 83% of marketers say AI helps create significantly more content.
What we use: Validates frequency constants.
Socialinsider (2026) — Instagram Organic Engagement Benchmarks
URL: socialinsider.io/blog/instagram-content-research Sample: 31 million posts analyzed
Key findings: Carousels 0.55%, Reels 0.52%, Images 0.45%, text_post ~0.37%. Reels reach 30.81% (2.25× static). Carousels reach 14.45%.
What we use: BASE_ENGAGEMENT, REACH_MULT constants.
Goldman Sachs Global Investment Research (March 2025)
Title: Creator Economy: Framing the Market Opportunity URL: goldmansachs.com/insights/articles/the-creator-economy-could-approach-half-a-trillion-dollars-by-2027 Type: Equity research note
Key findings: ~67M global creators in 2025, growing 10% CAGR to 107M by 2030. Only 3% are professional (>$100K/yr). TAM ~$250B → $480B by 2027. 3% of YouTubers capture 90% of earnings.
What we use: Problem framing in README. INITIAL_FOLLOWERS = 10000 (micro-creator tier). target_growth = 0.04 monthly (micro avg 0.8–1.5%/month → 0.04 as top-decile 4%/month target).
Tier 3 — Official platform statements
Adam Mosseri, Head of Instagram (January 2025)
Source: Public statements (Instagram posts, interviews) Confirmed signals:
- Watch time — most important ranking factor, especially Reels completion past 3 seconds
- Sends per reach — DM shares, strongest signal for reaching new audiences
- Likes per reach — key for existing followers
- Saves — content quality signal (not explicitly ranked top-3 but confirmed as strong)
What we use: FORMAT_SIGNAL_WEIGHTS, INTENT_MULTIPLIER, EngagementSignals model, reward weights 0.4·watch + 0.3·sends + 0.2·saves + 0.1·likes.
Tier 4 — Surveys (cite with caveat)
Awin / ShareASale (September 2024)
Sample: 300+ creators (majority female, 25–44, 1K–5K followers, Instagram 90%) Finding: 73% suffer burnout at least sometimes (down from 87% in 2022). Instagram drives 88% of burnout. Top cause: constant platform changes (70%). URL: prweb.com/releases/...creator-burnout
Caveat: Self-selected sample, not probability-based. Small N. But directionally consistent with Wen 2026 (T1).
What we use: burnout_risk contextual framing (73% baseline prevalence).
Vibely — Creator Burnout Report
Finding: 90% of creators experienced burnout. 71% considered quitting. Caveat: No sample size or methodology disclosed. Treat as directional only.
Tier 5 — Rejected sources (NOT cited in env constants)
The following sites were found during research but are not cited because they do not disclose methodology, sample sizes, or data collection processes. Their claims cannot be independently verified.
| Site | Why rejected |
|---|---|
| instacarousel.com | Affiliate blog, cites Socialinsider without adding primary data |
| midastools.co | SEO content, no methodology |
| kicksta.co | Growth tool vendor, no audit trail |
| postplanify.com | Aggregates others' data without attribution |
| monolit.sh | Blog post, no primary research |
| useadmetrics.com | Self-reported benchmarks, methodology unclear |
| creatorflow.so | Aggregates without disclosure |
| slumbertheory.com | Health blog, no clinical data source |
| dataslayer.ai | Marketing tool blog |
| almcorp.com | Agency blog |
| loopexdigital.com | Agency blog |
| carouselli.com | Tool vendor |
| influize.com | Tag listicle, no methodology |
This bibliography was compiled April 2026. All URLs verified at time of writing.