HackerNoon Trending Tech Stories Ranking

HackerNoon ranks its technology stories by combining three core signals: total pageviews, measured user engagement, and the volume of comments. Each signal is normalized and weighted to produce a composite score that highlights the most popular and discussed articles each week, offering readers a quick view of current online interest.

Scoring Algorithm Details

The algorithm first scales raw pageview counts to a 0‑100 range, then applies a logarithmic curve to smooth out outliers. Engagement metrics such as average time on page and scroll depth receive a multiplier based on recent activity trends. Comment counts are adjusted for spam and duplicate postings before contributing to the final score.

Pageview Normalization

Raw pageview numbers are divided by the highest‑recorded pageview within the evaluation window, then multiplied by 100. This ensures that a story with the most traffic receives a perfect pageview score while others are proportionally assessed.

Engagement Weighting

Engagement is calculated from time‑on‑page, scroll depth, and click‑through rates. Each factor receives an equal share of the engagement portion of the composite score, reflecting how deeply readers interact with the content.

Comment Activity Adjustment

Comment volume is first filtered through a spam‑detection model. Valid comments are then normalized against the median comment count across all stories, allowing the metric to reward genuinely discussed pieces without inflating scores for outlier spikes.