Rules
Platform trends guide: Track updates without the noise
Platform trends guide to tracking algorithm changes, emerging trends, and outages with named sources, discovery tools, and a weekly half-hour routine.
Desk
Field Desk
First-hand accounts of algorithm trend updates, including the parts that did not go to plan.
Maintenance
Why platform trends converge on the same handful of shapes
Why social platforms keep converging on three shapes, the feed, short vertical video and ephemeral stories, with dated examples, three durability tests and a way to read a trend claim.
Costs
Social media holidays research: a design you can run
Social media holidays research you can run: the seasonal confound, a matched holdout, named observances and surfaces, and what a year of pairs will license.
Industry
Social media holidays: six origins and a five-minute check
Social media holidays platforms and provenance: the six origins a date can have, a five-minute traceback, and why listing counts prove nothing at all.
Rules
Social media holidays with verified dates and sources: the 2027 view
Social media holidays for 2027, with the dates that hold and the sources behind them, plus how to check a day, platform by platform, before you plan a post.
Rules
Platform outage or algorithm change: triage a traffic drop
Platform outages updates 2027 can't be predicted, so use this order to triage a traffic drop and separate a real outage from an algorithm change.
Guides
Platform outages: down for you or down for everyone, and how to tell
Check platform outages with public report sites, official status pages, network tests, and uptime monitors to tell whether a problem is local or widespread.
Industry
Feed algorithms risks: six to watch, two to plan for
Feed algorithms risks: concentration, proxy capture, opacity, volatility, the eligibility cliff and dependence — what a publisher controls and what to plan for.
Rules
Feed algorithms compared across seven platforms and four structural questions
TikTok, Instagram, YouTube, X, LinkedIn, Facebook and Reddit rank on four structural questions about feed algorithms, and the answers explain creator reach.
Guides
Why no feed ordering is neutral: the constraints that force a feed
An argument that no feed order is neutral: the five constraints that force one, why every objective is a proxy, and what to check first when reach drops.
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.
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.
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.
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.
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.
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.
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.
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
Ranking signals comparison: evidence types
Ranking signals comparison for social feeds: what correlation studies, before-and-after tests, and account-level controls can show.
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.