IRobot: An Intelligent Crawler for Web Forums Rui Cai, Jiang-Ming Yang, Wei Lai, Yida Wang, and Lei Zhang Microsoft Research, Asia November 3, 2013.

iRobot: An Intelligent Crawler for Web Forums

Rui Cai, Jiang-Ming Yang, Wei Lai, Yida Wang, and Lei Zhang

Microsoft Research, Asia

April 10, 2023

Outline

• Motivation & Challenge• iRobot – Our Solution

– System Overview– Module Details

• Evaluation

2

Outline

• Motivation & Challenge• iRobot – Our Solution

– System Overview– Module Details

• Evaluation

3

Why Web Forum is Important

• Forum is a huge resource of human knowledge– Popular all over the world– Contain any conceivable topics and issues

• Forum data can benefit many applications– Improve quality of search result– Various data mining on forum data

• Collecting forum data– Is the basis of all forum related research– Is not a trivial task

4

Why Forum Crawling is Difficult

• Duplicate Pages– Forum is with complex in-site structure– Many shortcuts for browsing

• Invalid Pages– Most forums are with access control– Some pages can only be visited after registration

• Page-flipping– Long thread is shown in multiple pages– Deep navigation levels

5

The Limitation of Generic Crawlers

• In general crawling, each page is treated independently– Fixed crawling depth– Cannot avoid duplicates before downloading– Fetch lots of invalid pages, such as login prompt– Ignore the relationships between pages from a

same thread

• Forum crawling needs a site-level perspective!

6

Statistics on Some Forums

• Around 50% crawled pages are useless• Waste of both bandwidth and storage

7

Outline

• Motivation & Challenge• Our Solution – iRobot

– System Overview– Module Details

• Evaluation

8

What is Site-Level Perspective?

• Understand the organization structure• Find our an optimal crawling strategy

9

List-of-Board

List-of-Thread

Browse-by-Tag

Search Result

Post-of-Thread

Login Portal

Entry

Digest

The site-level perspective of "forums.asp.net"

iRobot: An Intelligent Forum Crawler

Crawler

General Web Crawling

Sitemap Construction

Traversal Path Selection

Forum Crawling

Segmentation & Archiving

Raw Pages Meta

Restart

10

Outline

• Motivation & Challenge• Our Solution – iRobot

– System Overview– Module Details

• How many kinds of pages? • How do these pages link with each other?• Which pages are valuable?• Which links should be followed?

• Evaluation

11

Sitemap Construction

Sitemap Construction

Traversal Path Selection

Traversal Path Selection

Page Clustering• Forum pages are based on database & template• Layout is robust to describe template

– Repetitive regions are everywhere on forum pages– Layout can be characterized by repetitive regions

(b) (d)(a) (c)

12

Page Clustering

13

14

List-of-Board

List-of-Thread

Browse-by-Tag

Search Result

Post-of-Thread

Login Portal

Digest

Link Analysis• URL Pattern can distinguish links, but not

reliable on all the sites• Location can also distinguish links

15

1. Login

4. Thread List

5. Thread

A Link = URL Pattern + Location

16

List-of-Board

List-of-Thread

Browse-by-Tag

Search Result

Post-of-Thread

Login Portal

Entry

Digest

Informativeness Evaluation

• Which kind of pages (nodes) are valuable?• Some heuristic criteria

– A larger node is more like to be valuable– Page with large size are more like to be valuable– A diverse node is more like to be valuable

• Based on content de-dup

17

18

List-of-Board

List-of-Thread

Browse-by-Tag

Search Result

Post-of-Thread

Login Portal

Entry

Digest

Traversal Path Selection

• Clean sitemap– Remove valueless nodes– Remove duplicate nodes– Remove links to valueless / duplicate nodes

• Find an optimal path– Construct a spanning tree– Use depth as cost

• User browsing behaviors– Identify page-flipping links

• Number, Pre/Next19

20

List-of-Board

List-of-Thread

Browse-by-Tag

Search Result

Post-of-Thread

Login Portal

Entry

Digest

Outline

• Motivation & Challenge• iRobot – Our Solution

– System Overview– Module Details

• Evaluation

21

Evaluation Criteria

• Duplicate ratio

• Invalid ratio

• Coverage ratio

0%

10%

20%

30%

40%

50%

60%

70%

Biketo Asp Baidu Douban CQZG Tripadvisor Hoopchina

Mirrored Pages iRobot

0%

5%

10%

15%

20%

25%

Biketo Asp Baidu Douban CQZG Tripadvisor Hoopchina

Mirrored Pages iRobot

0%10%20%30%40%50%60%70%80%90%

100%

Biketo Asp Baidu Douban CQZG Tripadvisor Hoopchina

Coverage ratio

22

Effectiveness and Efficiency• Effectiveness

• Efficiency0

1000

2000

3000

4000

5000

6000

Biketo Asp Baidu Douban CQZG Tripadvisor Hoopchina

(a) A Generic Crawler Invalididate Duplicate Valuable

0

1000

2000

3000

4000

5000

6000

Biketo Asp Baidu Douban CQZG Tripadvisor Gentoo

(b) iRobot Invalididate Duplicate Valuable

0

2500

5000

7500

10000

12500

15000

17500

20000

Biketo Asp Baidu Douban CQZG Tripadvisor Hoopchina

(a) A Generic Crawler Invalididate

Duplicate

Valuable

0

2500

5000

7500

10000

12500

15000

17500

20000

Biketo Asp Baidu Douban CQZG Tripadvisor Hoopchina

(b) iRobot Invalididate

Duplicate

Valuable

23

Performance vs. Sampled Page#

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

10 20 50 100 500 1000Number of Sampled Pages

Coverage ratio

Duplicate ratio

Invalid ratio

24

Preserved Discussion Threads

Forums MirroredCrawled by

iRobotCorrectly

RecoveredBiketo 1584 1313 1293

Asp 600 536 536

Baidu − − −

Douban 62 60 37

CQZG 1393 1384 1311

Tripadvisor 326 272 272

Hoopchina 2935 2829 2593

25

Conclusions

• An intelligent forum crawler based on site-level structure analysis– Identify page templates / valuable pages / link

analysis / traversal path selection• Some modules can still be improved

– More automated & mature algorithms in SIGIR’08• More future work directions

– Queue management– Refresh strategies

26

Thanks!

27