[Discussion] Detecting Spider Traps by analyzing offline URL-MD5 data
Core
Hi everyone,
I'm working on solving the Spider-Trap problem in my personal project.
My goal is to analyze offline URL-MD5 data to generate regex rules that prevent our crawler from downloading different links that yield identical content.
Currently, I'm applying a Trie data structure (drawing from my experience in ICPC contests) to analyze the data. specifically, I build a Trie from the URL paths, calculate statistics for each node, and mark a node as a "BadNode" if its sons contains a high volume of duplicate content (identical MD5s). Finally, I extract the path prefixes from these BadNodes to generate the blocking rules.
However, this strategy doesn't seem to be very effective in practice.
I am writing to ask for suggestions. What are the industry-standard methods for detecting these types of traps using offline data analysis?
Thanks, Bohan Mao
Al-Meer Technology
That's a very interesting approach using a Trie for URL path analysis!
While your Trie-based method is creative, the industry-standard approach for identifying boilerplate content and structural repetition (which is often the root cause of spider traps) typically involves a more holistic view of the URL and the content structure.
Some common methods for detecting these traps using offline data analysis include:
1. URL Template Mining: Instead of just path prefixes, this method focuses on identifying repeating URL patterns that indicate generated links (e.g., /product/id-[0-9]+ or /page/[A-Z]/). Tools like Warcbase or custom scripts can help extract and cluster common URL structures.
2. Content-Based Near-Duplicate Detection: Use techniques like shingling and MinHash or Locality-Sensitive Hashing (LSH) on the actual page content (after stripping boilerplate elements like headers/footers) to identify pages that are structurally or semantically identical, even if their URLs are slightly different.
3. Visual Boilerplate Removal/Extraction: Analyzing the DOM structure or visual layout to strip out generic navigation and footer elements before calculating the content MD5. This ensures that the MD5 comparison is only based on the unique, relevant part of the page.
4. Parameter Analysis: For URLs with query parameters (e.g., ?session=..., ?sort=...), identify parameters that change the URL but not the resulting content MD5. You can then create rules to normalize or ignore these parameters during crawling.
Focusing on URL Template Mining alongside robust content near-duplicate detection should give you a much more effective set of blocking rules.
Let me know if you have any questions about implementing these!
Best,
Mahmud
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