How to Track Tech Trends with the Hacker News Scraper

Uncovering Emerging Tech Trends from Hacker News

In the fast-paced world of technology, staying ahead of the curve is not just an advantage – it's a necessity. For product managers, market researchers, venture capitalists, and even individual developers, understanding what’s gaining traction, what's generating buzz, and what the community truly thinks can be the difference between innovation and obsolescence. But how do you efficiently sift through the constant deluge of information on platforms like Hacker News to extract actionable insights?

Manually tracking the "Top Stories" or "New Stories" is tedious and time-consuming. You miss critical context from comments, struggle to filter out irrelevant noise, and can't easily perform historical analysis. Imagine needing to:

• Identify the most discussed programming languages or frameworks over the last month.
• Monitor competitor mentions and community sentiment around their new product launches.
• Pinpoint specific domains that consistently make it to the front page, signaling new players or influential content sources.
• Analyze the depth of engagement and specific concerns within "Ask HN" threads related to a particular industry challenge.

This is where the Hacker News Scraper from Apify comes in. This powerful Actor automates the process of extracting stories, comments, jobs, and user profiles directly from Hacker News via its public APIs, providing structured data for your analysis needs.

How the Hacker News Scraper Works for Trend Monitoring

The Hacker News Scraper is designed to interact with Hacker News's public Firebase and Algolia APIs. This means it doesn't rely on proxies or logins, making the data extraction process robust and efficient. It offers various mode options to target specific data types:

• topStories: Capture what's currently most popular.
• newStories: Discover the latest submissions as they appear.
• bestStories: Focus on high-quality, recently trending content.
• askStories / showStories / jobStories: Isolate specific types of posts for targeted analysis.
• search: Perform full-text queries across all Hacker News history using the Algolia HN API.
• item / user: Look up specific stories, comments, or user profiles by ID or username.

Let's explore some concrete examples of how you can use this Actor to track tech trends and gather market intelligence.

1. Identifying Dominant Domains and Emerging Topics

You want to understand which websites or content sources are consistently reaching the Hacker News front page, indicating influence or high-quality content.

The Problem: Manually checking the front page daily and recording domains is impractical. You need a systematic way to aggregate this information over time and filter out irrelevant content.

The Solution: Use the topStories mode to regularly scrape the top stories. The Actor's output for each story includes the domain (parsed host) and url. You can then aggregate and count these domains over a specified period.

To refine your results, you can use input fields like:

• maxItems: Cap the total number of stories to fetch, for example, maxItems: 100 for the top 100 stories.
• minScore: Filter out stories that don't meet a certain engagement threshold, e.g., minScore: 50.
• domainBlocklist: Exclude domains that are not relevant to your research, such as social media sites. For instance, domainBlocklist: ["twitter.com", "x.com"] will prevent stories linking to these platforms from being emitted.
• dateRangeFrom and dateRangeTo: Analyze trends within specific timeframes by setting these to ISO-formatted dates.

By running this setup weekly or daily, you can build a historical dataset of prominent domains, revealing which companies or content types are consistently gaining attention.

2. Analyzing Community Sentiment Around New Technologies or Products

Understanding how the developer community reacts to a new framework, tool, or product launch is invaluable. You need to capture not just the announcement, but the detailed discussions and opinions.

The Problem: Reading through hundreds of comments on a popular story to gauge sentiment is time-consuming and subjective. You need structured access to comment content and authorship.

The Solution: First, identify the relevant story ID (either manually or by scraping newStories or search for keywords). Then, use the item mode with that itemIds to pull the story and its comments.

Crucially, set maxComments to a high value (e.g., maxComments: 500) to ensure you fetch all relevant discussions, and enableCommentHierarchy: true to get nested replies, which is essential for understanding conversation flow. The maxDepth field (e.g., maxDepth: 5) lets you control how many levels deep the replies go.

The output for each comment includes text, author, createdAt, and parentId, allowing you to reconstruct discussion threads. You can even filter comments by commentAuthorFilter if you're interested in specific influential voices.

Once you have this structured comment data, you can apply natural language processing (NLP) techniques to analyze sentiment, identify recurring themes, and track community concerns or enthusiasm over time.

3. Monitoring "Ask HN" and "Show HN" for Industry Pain Points and Innovations

"Ask HN" threads are a goldmine for understanding the challenges and questions developers face, while "Show HN" posts reveal new projects and solutions being built.

The Problem: It's hard to consistently track "Ask HN" posts related to a specific niche or to get a comprehensive view of new "Show HN" projects without manual scanning.

The Solution: To monitor industry pain points, use mode: "askStories" and combine it with searchQuery if you have specific keywords (e.g., "AI ethics" or "serverless challenges"). For new innovations, use mode: "showStories".

You can use minCommentCount (e.g., minCommentCount: 10) to focus on discussions that have generated significant engagement, signaling more impactful problems or solutions. Enabling maxComments and enableCommentHierarchy will give you the full context of the discussions around these posts, similar to the sentiment analysis use case.

How to Use the Hacker News Scraper

Here’s a quick guide to getting started with the Hacker News Scraper on Apify:

1. Find the Actor: Navigate to the Apify Store and search for "Hacker News Scraper."
2. Start a New Task: Click the "Try for free" or "Start new task" button.

3. Configure Input: The "Input" tab is where you define what you want to scrape.

   • Select a Mode: Choose your desired mode (e.g., topStories, search, item).
   • Add Filters: Use fields like maxItems, minScore, domainAllowlist, domainBlocklist, dateRangeFrom, dateRangeTo, maxComments, and enableCommentHierarchy to refine your data.
   • Specify Search Query/Item IDs: If using search mode, enter your searchQuery. For item mode, provide itemIds.

   • Example Input: To get the top 50 stories with at least 100 points, excluding Twitter links, you'd use:

     {
     "mode": "topStories",
     "maxItems": 50,
     "minScore": 100,
     "domainBlocklist": ["twitter.com", "x.com"],
     "excludeDeadOrDeleted": true
     }

4. Run the Actor: Click the "Start" button to initiate the scraping process.

5. Download Results: Once the run completes, go to the "Storage" tab and download your data in your preferred format (JSON, CSV, Excel, etc.).

The output records will include fields like title, url, domain, score, numComments, author, and createdAt for stories, and text, author, createdAt, parentId for comments, providing a rich dataset for your analysis.

Beyond Basic Trend Monitoring

The Hacker News Scraper is a versatile tool that extends beyond these examples. You can use it to:

• Build a weekly digest of YC job ads by setting mode: "jobStories".
• Perform user research by fetching user profiles to understand karma, account age, and submittedCount for potential outreach.
• Feed Algolia search results into downstream AI models for automated tagging or summarization, leveraging the search mode to gather relevant content.

By transforming unstructured web content into clean, structured data, the Hacker News Scraper empowers you to make data-driven decisions, stay competitive, and discover the next big thing in tech. Give it a try and unlock the insights hidden within Hacker News!

Ready to try it yourself? Run *Hacker News Scraper** on the Apify Store -- no setup required.*