How Social Media Algorithms Work to Rank Content and Boost Your Online Reach

"Instagram's social media algorithm explained works through multi-stage filtering that gathers posts from followed accounts, scores roughly 500 posts using machine learning predictions, and ranks them according to expected engagement. Content ranking favors Reels most heavily, followed by carousels and Stories, while saves count three times more than likes in determining reach. Key signals include watch time, where videos retaining viewers for at least 70% of duration rank twice as high."

"Intent prediction draws on cross-platform behavior from Threads or Facebook to forecast interest. Relationships matter too-posts from frequent DMs or interactions get priority. Recency still plays a role, with newer posts edging older content, while evergreen saves sustain long-term visibility. Audio trends and original sounds amplify reach by roughly 40%, but hashtag stuffing can reduce ranking when AI flags irrelevant usage."

Social media platforms use predictive machine-learning models to filter thousands of posts into personalized feeds based on engagement metrics, relevancy signals, and user behavior patterns. Instagram scores roughly 500 candidate posts per user, prioritizes Reels, and values saves and watch time heavily; saves count three times more than likes and videos retaining viewers for at least 70% rank twice as high. TikTok emphasizes watch time while Facebook weighs relationships, trust scores, content type, timing, and demographics. Cross-platform signals and intent prediction inform interest forecasts. Audio trends and original sounds can amplify reach, whereas irrelevant hashtag stuffing can reduce ranking when flagged by AI.

Read at Tech Times