How I Found The Most Influential Users on Hacker News

Lucija Perkovic's exploration into the dynamics of Hacker News, a tech industry content platform, led her to investigate the factors influencing whether a post lands on the coveted first page. Initially unfamiliar with Hacker News, she discovered that the timing of post publication and the pattern of upvotes and comments significantly affected a post's score, with a steady rise in engagement being more beneficial. To analyze this, Perkovic utilized the Hacker News API and Kafka, collecting real-time data and employing Memgraph's PageRank algorithm to identify influential stories. Her findings suggested that experienced users had a higher likelihood of reaching the first page, likely due to their familiarity with audience preferences. However, she ultimately concluded that while experience plays a role, luck remains a crucial factor in determining a post's success.

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