1. Crawlers Padmini Srinivasan Computer Science Department Department of Management Sciences http://cs.uiowa.edu/~psriniva padmini-srinivasan@uiowa.edu

2. Basics What is a crawler? HTTP client software that sends out an HTTP request for a page and reads a resppnse. Timeouts How much to download? Exception handling Error handling Collect statistics: time-outs, etc. Follows Robot Exclusion Protocol (de facto, 1994 onwards)

3. Tippie web site # robots.txt for http://www.tippie.uiowa.edu/ or http://tippie.uiowa.edu/ # Rules for all robots accessing the site. User-agent: * Disallow: /error-pages/ Disallow: /includes/ Disallow: /Redirects/ Disallow: /scripts/ Disallow: /CFIDE/ # Individual folders that should not be indexed Disallow: /vaughan/Board/ Disallow: /economics/mwieg/ Disallow: /economics/midwesttheory/ Disallow: /undergraduate/scholars/ Sitemap: http://tippie.uiowa.edu/sitemap.xml

4. Robots.txt User-agent: * Disallow: User-agent: BadBot Disallow: / User-agent: Google Disallow: User-agent: * Disallow: / http://www.robotstxt.org/ Legal? No. But has been used in legal cases.

5. Types of crawlers Get everything? – Broad….. Get everything within on a topic? – Preferential, topical, focused, thematic – What are your objectives behind the crawl? Keep it fresh – When does one run it? Get new versus check old? How does one evaluate performance? – Sometimes? Continuously? What’s the Gold Standard?

6. Design

7. Crawler Parts Frontier – List of “to be visited” URLS – FIFO (first in first out) – Priority queue (preferential) – When the Frontier is full Does this happen? What to do? – When the Frontier is empty Does this happen? 10,000 pages crawled, average 7 links / page: 60,000 URLS in the frontier, how so? Unique URLS?

8. Crawler Parts History – Time-stamped listing of visited URLS – take out of frontier first – Can keep other information too: quality estimate, update frequency, rate of errors (in accessing page), last update date, anything you want to track related to the fetching of the page. – Fast lookup Hashing scheme on the URL itself Canonicalize: – Lowercasing; – remove anchor reference parts: » http://www……./faq.html#time » http://www……./faq.html – Remove tildas – Add or subtract trailing / – Remove default pages: index.html – Normalize paths: removing parent pointers in url » http://www…./data/../mydata.html – Normalize port numbers: default numbers (80) Spider traps: long URLS, limit length.

9. Crawler Parts Page Repository – Keep all of it? Some of it? Just the anchor texts? – You decide Parse the web page – Index and store information (if creating a search engine of some kind) – What to index? How to index? How to store? Stopwords, stemming, phrases, Tag tree evidence (DOM), NOISE! – Extract URLS Google initially: show you next time.

11. And… But why do crawlers actually work? – Topical locality hypothesis An on topic page tends to link to other on topic pages. Empirical test : that two pages that are topically similar have higher probability of linking to each other than two random pages on the web. (Davison, 2000) And too browsing works! – Status locality? high status web pages are more likely to link to other high status pages than to low status pages Rationale from social theories: relationship asymmetry in social groups and the spontaneous development of social hierarchies.

12. Crawler Algorithms Naïve best-first crawler – Best-N-first crawler SharkSearch crawler – FishSearch Focused crawler Context Focused crawler InfoSpiders Utility-biased web crawlers

13. Naïve Best First Crawler Compute cosine between page and query/description as URL score Term frequency (TF) and Inverse Document Frequency (IDF) weights Multi-threaded: Best-N-crawler (256)

14. Naïve best-first crawler Bayesian classifier to score URLS Chakrabarti et al. 1999 SVM (Pant and Srinivasan, 2005) better. Naïve Bayes tends to produce skewed scores. Use PageRank to score URLS? – How to compute? Partial data. Based on crawled data – poor results – Later: utility-biased crawler

15. Shark Search Crawler From earlier Fish Search (de Bra et al.) – Depth bound; anchor text; link context; inherited scores score(u) = g * inherited(u) + (1 – g) * neighbourhood (u) inherited(u) = x * sim(p, q) if sim(p, q) > 0 else inherited(p) (x < 1). neighbourhood(u) = b * anchor(u) + (1-b) * context(u) (b < 1) context(u) = 1 if anchor(u) > 0 else sim(aug_context, q) Depth: controls travel in a sub space; no more ‘relevant’ information found.

16. Focused Crawler Chakrabarti et al. Stanford/IIT – Topic taxonomy – User provided sample URLs – Classify these onto the taxonomy (Prob(c|url) where Prob(root|url) = 1. – User iterates selecting and deselecting categories – Mark the ‘good’ categories – When page crawled: relevance(page) = sum(Prob(c|page)) where sum is over the good categories; score URLS – When crawling: Soft mode: use this relevance score to rank URLS Hard mode: find leaf node with highest score, if any ancestor marked relevant then add to frontier else not

17. Context Focused Crawler A rather different strategy – Topic locality hypothesis somewhat explicitly used here – Classifiers estimate distance to relevant page from a crawled page. This estimate scores urls.

18. Context Graph

19. Levels: L Probability (page in class, i.e., level x) x = 1, 2, 3 (other) Bayes theorem: Prob(L1|page) = {Prob(page|L1) * Prob(L1)}/Prob(page) Prob(L1) = 1/L (number of levels)

20. Utility-Biased Crawler Considers both topical and status locality. Estimates status via local properties Combines using several functions. – One: Cobb-Douglas function Utility(URL) = topicality a * status b (a + b = 1) – if a page is twice as high in topicality and twice as high in status then twice as high utility as well. – Increases in topicality (or status) cause smaller increases in utility as the topicality (or status) increases.

21. Estimating Status ~ cool part Local properties – M5’ decision tree algorithm Information volume Information location – http://www.somedomain.com/products/news/daily.html Information specificity Information brokerage Link ratio: # links/ # words Quantitative ratio: # numbers/# words Domain traffic: ‘reach’ data for domain obtained from Alexa Web Information Service Pant & Srinivasan, 2010, ISR

22. Utility-Biased Crawler Cobb-Douglas function – Utility(URL) = topicality a * status b (a + b = 1) Should a be fixed? “one size fits all” Or should it vary based on the subspace? Target topicality level (d) a = a + delta (d – t), 0 <= a <= 1 – t: average estimated topicality of the last 25 pages fetched – Delta is a step size (0.01) Assume a = 0.7, delta = 0.01 and t = 0.9 » a = 0.7 + 0.01(0.7 – 0.9) = 0.7 – 0.002 Assume a = 0.7, delta = 0.01 and t = 0.4 » a = 0.7 + 0.01(0.7 – 0.4) = 0.7 + 0.003

23. It’s a matter of balance

24. Crawler Evaluation What are good pages? Web scale is daunting User based crawls are short, but web agents? Page importance assessed – Presence of query keywords – Similarity of page to query/description – Similarity to seed pages (held out sample) – Use a classifier – not the same as used in crawler – Link-based popularity (but within topic?)

25. Summarizing Performance Precision – Relevance is Boolean: yes/no Harvest rate: # of good pages/total # pages – Relevance is continuous Average relevance over crawled set – Recall Target recall: held out seed pages (H) – |H ∧ pages crawled|/|pages crawled| Robustness – Start same crawler on disjoint seed sets. Examine overlap of fetched pages

26. Sample Performance Graph

27. Summary Crawler architecture Crawler algorithms Crawler evaluation Assignment 1 – Run two crawlers for 5000 pages. – Start with the same set of seed pages for a topic. – Look at overlap and report this over time (robustness)