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SlashDot Technology News Scraper

This Apify actor scrapes technology news articles from SlashDot.org, extracting comprehensive information about articles, their content, engagement metrics, and community discussions.

Features

• Comprehensive Article Data: Scrapes detailed information about technology news articles
• Content Analysis: Extracts full article content, summaries, and metadata
• Engagement Metrics: Collects comment counts, scores, views, and ratings
• Community Features: Gathers comments, discussions, and user interactions
• Categorization: Extracts sections, tags, and topic classifications
• Related Content: Finds related articles and cross-references
• Filtering Options: Supports filtering by sections and sorting methods
• HTML Debugging: Saves HTML content for selector analysis during development

Input Parameters

Output Data

Each article record includes:

Basic Information

• article_id: Unique article identifier
• title: Article title
• summary: Article summary/teaser
• url: URL to the full article
• image_url: Article thumbnail/preview image URL

Author and Publication

• author: Article author name
• published_date: When the article was published
• section: Article section/category

Categorization

• tags: Array of tags and labels

Engagement Metrics

• comment_count: Number of comments
• score: Article score/rating
• views: Number of views

Timestamps

• scraped_at: When the data was scraped

Detailed Information (if scrapeDetails=true)

• full_content: Complete article content
• paragraphs: Array of article paragraphs
• related_articles: Array of related articles with title and URL
• comments: Array of comments with text, author, date, and score
• media_files: Array of media files with URL, type, and alt text
• source_links: Array of external source links
• metadata: Article metadata from meta tags

Metadata

• source: Source website (slashdot.org)

Usage Examples

Basic Usage

{
"maxArticles":50,
"scrapeDetails":true
}

Filtered by Section

{
"maxArticles":200,
"scrapeDetails":true,
"sections":["technology","science"],
"sortBy":"popular"
}

Most Commented Articles

{
"maxArticles":100,
"scrapeDetails":true,
"sortBy":"most_commented"
}

Quick Scraping (No Details)

{
"maxArticles":500,
"scrapeDetails":false,
"sortBy":"latest"
}

Development Features

HTML Debugging

During development, the scraper saves HTML content to the key-value store for selector analysis:

• debug_slashdot_html: Contains the HTML content of the main page

Error Handling

• Comprehensive error handling with detailed logging
• Graceful handling of missing elements
• Retry logic for failed requests

Browser Automation

• Uses Playwright for reliable browser automation
• Handles dynamic content loading
• Implements proper delays and waits

Installation

1. Install dependencies:

$pip install-r requirements.txt

2. Install Playwright browsers:

$playwright install chromium

3. Run the scraper:

$python -m src

Docker Usage

docker build -t slashdot-scraper .
docker run -eAPIFY_TOKEN=your_token slashdot-scraper

Notes

• The scraper respects rate limits and implements delays between requests
• HTML content is saved for debugging purposes during development
• The scraper handles various article listing layouts and structures
• All URLs are properly resolved and normalized
• Comment extraction includes author information and engagement metrics
• The scraper can handle both article listings and detailed article pages