Algorithm gaming is the practice of shaping content to exploit a platform’s ranking system — its recommendation, search, or trending logic — so the content wins reach regardless of its quality, accuracy, or value to the audience. The technique treats the algorithm’s proxy metrics (watch time, dwell time, shares, keyword matches, early engagement velocity) as the true objective, and reverse-engineers content to satisfy them. Because every major feed decides visibility by these signals, whoever games them best is seen most.
There is a legitimate cousin: honest search-engine optimization and platform best-practice are simply the craft of making genuinely good work discoverable. Algorithm gaming crosses into manipulation when the optimization replaces substance rather than surfacing it — engineered hooks over an empty payload, keyword-stuffed filler, reused outrage templates, coordinated early engagement to trip the “trending” threshold, or format mimicry that borrows the shape of value without the content. The result is a feed that reflects what defeated the ranking system, not what deserved your attention.
Its defining tell is a mismatch between packaging and payload: the piece is exquisitely tuned to the machine and thin for the human. This is why the durable defense is to judge the substance you actually received and to diversify away from any single ranking system’s verdict. Reach is a fact about distribution — social proof of visibility, not of quality (see T3.12 Viral-Popularity Signaling) — and mistaking one for the other is exactly the confusion the technique is built to exploit.