Hacker News Tech Trend Velocity Final

By shrijeetverma13 · March 21, 2026

Datasource:

The analysis successfully produced a bar chart and data table showing the viral rate (percentage of posts with Is Viral=1) for each Post Type. You can see the breakdown visually in the chart, with each bar representing how often that post type goes viral, and the exact percentages labeled on each bar.

The analysis reveals two different sweet spots depending on what you're optimizing for. Titles with 14 words achieve the highest median Score of 3 (based on 372 posts), while titles with 17 words have the highest viral rate at 15.4% (based on 13 posts). A bar and line chart was generated showing how both metrics vary across all title word counts with at least 10 posts.

The analysis compared median values across four key metrics for viral and non-viral posts. A grouped bar chart and data table were generated to visualize the differences clearly. Both groups share the same median title length (56 characters), but viral posts differ significantly in velocity and age metrics.

The analysis produced a bar chart and data table showing the top 20 highest-scoring posts, broken down by Post Type. The chart displays each post's score ranked from highest to lowest, color-coded by Post Type, so you can quickly see which types dominate the top rankings. Hovering over each bar reveals the post title, title length, and word count for deeper context.

The best Score Velocity threshold for separating viral from non-viral posts is 0.30 . At this cutoff, the classifier achieves an exceptional F1 score of 0.987 and 99.7% accuracy, making it a highly reliable separator. A chart was generated showing how F1, Accuracy, Precision, and Recall change across all tested thresholds, with a purple dotted line marking the optimal point.

The analysis reveals two distinct sweet spots depending on the metric you care about. Titles with 21-30 characters achieve the highest average score (22.5), while titles with 31-40 characters have the highest viral rate (10.9%). A data table was generated showing the full breakdown across all length ranges.

Score Velocity and Comment Velocity are strongly correlated with each other (Pearson r = 0.767), meaning posts that gain upvotes quickly also tend to attract comments quickly. When it comes to predicting virality, Score Velocity is the better predictor with a point-biserial correlation of r = 0.44, compared to Comment Velocity's r = 0.33. Both metrics show dramatic differences between viral and non-viral posts — viral posts have a Score Velocity 67.5x higher and a Comment Velocity 121.5x higher than non-viral posts. Data tables were generated summarizing these relationships.

Among the 110 posts under 2 hours old in this dataset, every single one has a viral status — giving a 100% viral rate. This means that for this dataset, being under 2 hours old is itself strongly associated with virality. The analysis produced a heatmap showing viral rates across Score Velocity and Comment Velocity bins, along with bar charts comparing how each velocity metric relates to virality. Viral posts average a Score Velocity of 7.5 and the 50%+ viral rate threshold begins at the lowest bin (0-5), confirming that even modest early velocity is predictive.

A grouped bar chart has been generated comparing Score Velocity and Comment Velocity across the 25th, 50th, 75th, and 95th percentiles. The visualization clearly shows how each velocity metric is distributed across these benchmark thresholds, making it easy to compare the two metrics side by side at each percentile level.

The analysis produced a bar chart showing the average Comments-to-Score (CS) ratio broken down by Post Type. This ratio tells you how much discussion (comments) a post generates relative to its popularity (score) — a higher ratio means more conversation per upvote, indicating strong community engagement even if the post isn't highly scored.

A bar chart and data table have been generated showing the percentage of viral posts across different post types. The visualization displays each Post Type on the x-axis with its corresponding viral percentage on the y-axis, along with a dashed red reference line indicating the overall viral rate across all posts. Each bar is labeled with its exact viral percentage, making it easy to compare which post types tend to go viral more often.

Among the 4,888 posts older than 100 hours, a bar chart and data tables have been generated highlighting the top 15 posts with the highest Comment Velocity — a measure of sustained engagement over time. These posts stand out as having continued to attract comments long after their initial posting, indicating lasting community interest.

We identified 155 posts that show signs of peaking early and stalling — they have high Score Velocity (at or above the 75th percentile of 0.08) but low absolute Scores (at or below the 25th percentile of 1). A scatter plot highlights these posts in red against all others in gray, making it easy to spot the pattern visually.

The sentiment analysis of the 'Title' column reveals that the vast majority of titles carry a neutral tone. A bar chart has been generated showing the full sentiment distribution across all 10,000 entries.

A word cloud has been generated from the 'Title' column of your dataset, visually representing the most frequently occurring words in the titles.