Maintenance
How to Read a Viral Trend's Attention Curve
Not a roundup of viral trends updates 2027: read attention curves, spot manufactured activity, and understand disclosure duties when you pay for reach.
Archive
September 2026
Industry
A viral spike has four phases: what onset, peak, decay, and residue tell you
A viral spike in four phases: what onset, peak, decay, and residue can and cannot tell you, when to stop counting, and how to measure the residue.
Guides
How to read a viral trends comparison
A skeptical guide to viral trends comparison: spot survivorship bias, test five dimensions, and decide what an episode comparison can actually show.
Industry
Recommendation systems: six cases, including two audiences that cancel out
Six recommendation cases with names attached, from YouTube's cold start to TikTok's regional gate, and the flat average that hides two opposed audiences.
Features
Recommendation systems timeline without dates: item, profile, and generations
An undated account of recommendation systems: the states an item passes through, the states a profile passes through, where the two lines cross, and generations.
Features
Why there is no honest list of ranking signals, and the four stages instead
Why nobody can give you a list of ranking signals, plus the four jobs every ranking system has and how to read a symptom back to the stage it belongs to.
Features
Ranking signals updates under review: the good, the weak and the verdict
Ranking signals updates under review, ranked: the ones with published documentation, the ones resting on inference, and the verdict on each.
Reviews
Feature updates updates: staged rollouts on social platforms
Feature updates updates 2027: how social platforms stage a new feature with flags, rings and holdbacks, why rolling out cannot be checked, and how to measure.
Guides
Feature updates research: a practical sequence
Feature updates research for social platforms: capture a native-analytics baseline, wait through rollout, compare an untouched series, and decide when to act.
Industry
Feature updates: six places to look and what each source is for
Feature updates: six sources to check, from help pages and changelogs to forums and your own analytics, plus what each one is for and what to do when two disagree.
Reviews
Recommendation systems platforms compared: what US platforms actually deploy
Six US platforms compared by retrieval stage, ranking signal and public detail, plus what each architecture forces a publisher to do before committing.
Rules
Annual trend reports research: what outlasts the next update
Annual trend reports research up close: how two annual surveys are fielded, why tactics built on a report age fast, and which work outlasts the next update.
Maintenance
Before you act on annual trend reports comparison
Annual trend reports comparison: the six method changes that quietly break a year-over-year series, and what to publish when the series is broken.
Reviews
Annual trend reports timeline: find the field date
The annual trend reports timeline runs from design to publication. Why the cover date is not the real date, and the three places to find the field date.
Guides
Annual trend reports risks: reading sponsored research
Annual trend reports risks often lie in framing, not just numbers. Read sponsored social media trend research without treating disclosure as proof.
Rules
Annual trend reports examples and how to read them
Annual trend reports examples worth citing, what each one measures, and the method checks that keep a trend score from misleading you during planning.
Features
Trend reports: six places where measurement tools diverge
Trend reports: the six design choices that make measurement tools disagree, why the sample is the deep problem, and what to check before quoting a figure.
Costs
The risks of going viral arrive in a predictable order
Viral trends risks in the order they arrive: the mismatched audience, the unreadable baseline, and the pressure to repeat that forms afterward.
Features
How viral trends spread: the ratio, the S curve, thresholds
Viral trends trends 2027: the reproduction ratio, why growth is bounded by an S curve, thresholds, and the two exposure mechanisms people confuse.
Maintenance
Viral reach: six properties that decide whether strangers ever see it
Viral trends platforms and the structural routes to strangers: six properties that decide whether wide spread is possible, and how to read a surface.
Rules
Reading a feature update announcement: six things worth extracting
Feature updates trends 2027 in reading announcements: who the text is written for, what the timing words mean, and six things to extract from any of them.
Reviews
How to read an annual trend report in about twenty minutes
Annual trend reports as a practical reference: how one gets made, reading it in twenty minutes, description against prediction, and taking a single decision.
Maintenance
5 details of feature updates statistics people miss
Five overlooked details explain why feature update statistics mislead, from missing change registers to staged rollouts and self-selected surveys.
Maintenance
Ranking signals comparison: evidence types
Ranking signals comparison for social feeds: what correlation studies, before-and-after tests, and account-level controls can show.
Rules
How to run ranking signals research on your social posts
Ranking signals research on your own social account: choosing a signal to test, building a split that cannot flatter you, and reading the result honestly.
Reviews
How a claim about ranking signals turns into folklore
Ranking signals timeline of a claim, from one account's odd week to folklore: seven stages, why aggregation is the dangerous one, and how to date a claim.
Guides
A working brief on recommendation systems platforms
Six recommendation systems platforms compared by item lifetime, trial cost, error cost and integration effort, and what each shape forces a publisher to do.
Industry
How to think about recommendation systems research
Recommendation systems research and its five kinds of evidence: the gap between offline and live results, reading a claim, and auditing your own material.
Rules
Seven risks in recommendation systems and what they mean for a publisher
Seven risks in recommendation systems, from inference and narrowing to fabricated behavior, and what each one means for a publisher as controls arrive.
Industry
How to tell whether sharing or recommendation carried a viral spike
How sharing and recommendation engines move a viral item, how the hourly shape of views shows which one carried a spike, and what you can plan before publishing.
Costs
Five kinds of platform feature updates and a rule for adopting them
Platform feature updates sorted into five kinds, a register for tracking them, a rule for when to adopt, and why the flow of changes never stops.
Reviews
A ranking signal is not the same thing as a ranking factor
Ranking signals sorted by the five producers of evidence, why a signal is not a factor, four tests for any claimed input, and examples read the right way.
Features
Recommendation systems: cold starts and feedback loops
How recommendation systems use content and behavior evidence, handle cold starts, and create feedback loops across YouTube, TikTok, and Instagram.