Crawling and Mining Web Sources

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1 Crawling and Mining Web Sources Flávio Martins Web Search 1

2 Sources of data Desktop search / Enterprise search Local files Networked drives (e.g., NFS/SAMBA shares) Web search All published pages on a (sub-)network Document feeds RSS/Atom feeds Commercial news feeds: AP, Reuters, Lusa Real-time content streams: Twitter, Instagram 2

3 Crawling Acquiring documents Desktop / Enterprise search. Crawling the Web Document feeds Extracting the content Format conversion HTML/DOM and Tag-plateau method XML Schema and JSON 3

4 What do we want to crawl? Acquire everything that is feasible Do not filter by subject / quality / trustworthiness No such thing as bad data or too much data Filtering is much easier once it s indexed Better models of subject / authority / spam Fast and space-efficient, if designed right Respect the rules robots.txt File system permissions visible!= accessible!= storable!= presentable 4

5 Crawling the Web Web Crawlers 5

6 Web Crawler Starts with a set of seeds, which are a set of URLs given to it as parameters Seeds are added to a URL request queue Crawler starts fetching pages from the request queue Downloaded pages are parsed to find link tags that might contain other useful URLs to fetch New URLs added to the crawler s request queue, or frontier Continue until no more new URLs or disk full

7 Web Crawler Massachusetts Institute of Technology,

8 Retrieving Web Pages Web crawler client program connects to a domain name system (DNS) server DNS server translates the hostname into an internet protocol (IP) address Crawler then attempts to connect to server host using specific port After connection, crawler sends an HTTP request to the web server to request a page usually a GET request

9 Retrieving Web Pages 9

10 Freshness Web pages are constantly being added, deleted, and modified Web crawler must continually revisit pages it has already crawled to see if they have changed in order to maintain the freshness of the document collection stale copies no longer reflect the real contents of the web pages

11 Freshness HTTP protocol has a special request type called HEAD that makes it easy to check for page changes returns information about page, not page itself

12 HTTP pipelining 12

13 Retrieving Web Pages Politeness Respect the rules Do not hit a web server too frequently Insert a time gap between successive requests to the same server Freshness Crawl some pages more often than others (e.g., news sites) Not and easy problem Simple priority queue fails 13

14 Sec Crawling picture URLs crawled and parsed Unseen Web Web Seed pages URLs frontier 14

15 Distributed Crawling Run multiple crawl threads, under different processes Potentially at different nodes Geographically distributed nodes Partition hosts being crawled into nodes Hash function used for partition 15

16 Distributed Crawling 16

17 Controlling Crawling Politeness - Don t hit a site too often Only crawl pages you are allowed to crawl Robots.txt file can be used to control crawlers

18 Spider trap Malicious server that generates an infinite sequence of linked pages Sophisticated spider traps generate pages that are not easily identified as dynamic. 18

19 What s in a URL? 19

20 Conversion Text is stored in hundreds of incompatible file formats e.g., raw text, RTF, HTML, XML, Microsoft Word, ODF, PDF Other types of files also important e.g., PowerPoint, Excel Typically use a conversion tool converts the document content into a tagged text format such as HTML or XML retains some of the important formatting information

21 Character Encoding A character encoding is a mapping between bits and glyphs i.e., getting from bits in a file to characters on a screen Can be a major source of incompatibility ASCII is basic character encoding scheme for English encodes 128 letters, numbers, special characters, and control characters in 7 bits, extended with an extra bit for storage in bytes

22 Unicode Proliferation of encodings comes from a need for compatibility and to save space UTF-8 uses one byte for English (ASCII), as many as 4 bytes for some traditional Chinese characters variable length encoding, more difficult to do string operations UTF-32 uses 4 bytes for every character Many applications use UTF-32 for internal text encoding (fast random lookup) and UTF-8 for disk storage (less space)

23 Unicode e.g., Greek letter pi (π) is Unicode symbol number 960 In binary, (3C0 in hexadecimal) Final encoding is (CF80 in hexadecimal)

24 Storing the Documents Many reasons to store converted document text saves crawling time when page is not updated provides efficient access to text for snippet generation, information extraction, etc. Database systems can provide document storage for some applications web search engines use customized document storage systems

25 Storing the Documents Requirements for document storage system: Random access request the content of a document based on its URL hash function based on URL is typical Compression and large files reducing storage requirements and efficient access Update handling large volumes of new and modified documents adding new anchor text

26 Storing the Documents Store many documents in large files, rather than each document in a file avoids overhead in opening and closing files reduces seek time relative to read time Compound documents formats used to store multiple documents in a file e.g., TREC Web

27 Compression Text is highly redundant (or predictable) Compression techniques exploit this redundancy to make files smaller without losing any of the content Popular algorithms can compress HTML and XML text by 80% e.g., DEFLATE (zip, gzip) and LZW (UNIX compress, PDF) may compress large files in blocks to make access faster

28 Detecting Duplicates Duplicate and near-duplicate documents occur in many situations Copies, versions, plagiarism, spam, mirror sites 30% of the web pages in a large crawl are exact or near duplicates of pages in the other 70% Duplicates consume significant resources during crawling, indexing, and search Little value to most users Exact duplicate detection is relatively easy CRC, Adler-32 checksums

29 Near-Duplicate Detection More challenging task Are web pages with same text context but different advertising or format near-duplicates? A near-duplicate document is defined using a threshold value for some similarity measure between pairs of documents e.g., document D1 is a near-duplicate of document D2 if more than 90% of the words in the documents are the same Fingerprinting SimHash Similar items are hashed to similar hash values Used by Google Crawler to find near duplicate pages

30 Fingerprints

31 Fingerprint Example

32 Fingerprint Example For a given shingle size, the degree to which two documents A and B resemble each other can be expressed as the ratio of the magnitudes of their shinglings' intersection and union, or r A, B = S(A) S(B) S(A) S(B) S(A) is the w-shingles for Document A S(B) is the w-shingles for Document B For a more efficient processing w-shingles are selected by 0 mod p (fish, include, fish) ->

33 Document Similarity Jaccard similarity J A, B = A B A B = Binary feature-vectors A B A + B A B Cosine similarity cos(θ) = A B A B binary feature vectors TF-IDF feature vectors 33

34 Removing Noise Many web pages contain text, links, and pictures that are not directly related to the main content of the page This additional material is mostly noise that could negatively affect the ranking of the page Techniques have been developed to detect the content blocks in a web page Non-content material is either ignored or reduced in importance in the indexing process

35 Removing Noise Web pages contain junk Navigation controls Interactive / multimedia Advertisements Copyright notices Want to extract content Block of text containing description of the page What you want to search Addison Wesley, 2008

36 Finding Content Blocks: Tag plateau Cumulative distribution of tags in the example web page Main text content of the page corresponds to the plateau in the middle of the distribution

37 Finding Content Blocks: DOM Use DOM structure and visual (layout) features Explicit markup Some sites will tag content Rare and site-specific Or learn statistically Brittle, needs re-training E.g., boilerpipe, goose

38 Finding Content Blocks: Libraries Boilerpipe Christian Kohlschütter et al Boilerplate detection using shallow text features. In WSDM '10. Main text of an article Main image of article Goose Main text of an article Main image of article Meta Description Meta tags Publish Date 38

39 Document Structure and Markup Some parts of documents are more important than others Document parser recognizes structure using markup, such as HTML tags Headers, anchor text, bolded text all likely to be important Metadata can also be important Links used for link analysis

40 Link Analysis Links are a key component of the Web Important for navigation, but also for search e.g., <a href=" >Example website</a> Example website is the anchor text is the destination link both are used by search engines

41 Anchor Text Used as a description of the content of the destination page i.e., collection of anchor text in all links pointing to a page used as an additional text field Anchor text tends to be short, descriptive, and similar to query text Retrieval experiments have shown that anchor text has significant impact on effectiveness for some types of queries i.e., more than PageRank

42 Link Quality Link quality is affected by spam and other factors e.g., link farms to increase PageRank trackback links in blogs can create loops links from comments section of popular blogs Blog services modify comment links to contain rel=nofollow attribute e.g., Come visit my <a rel=nofollow href=" page</a>.

43 Focused Crawling Attempts to download only those pages that are about a particular topic used by vertical search applications Rely on the fact that pages about a topic tend to have links to other pages on the same topic popular pages for a topic are typically used as seeds Crawler uses text classifier to decide whether a page is on topic Content of the pointing pages Anchor text of the pointing links Topic-weighted PageRank

44 Document Feeds Pull and Push 44

45 Document Feeds Many documents are published created at a fixed time and rarely updated again e.g., news articles, blog posts, press releases, Published documents from a single source can be ordered in a sequence called a document feed new documents found by examining the end of the feed timestamps determine what needs to be indexed

46 Document Feeds Two types: A push feed alerts the subscriber to new documents A pull feed requires the subscriber to check periodically for new documents Most common formats for pull feeds Really Simple Syndication, RDF Site Summary, Rich Site Summary Atom Syndication Format

47 Pull feeds Usually news articles / blogs via RSS/Atom Engine periodically checks a known URL Gets a list of all currently-available posts List contains timestamp, title, URL for each post Easy to determine what s new and fetch only that List contains a time-to-live (TTL) field Tells you how long list won t be updated (minutes) Main drawback: coverage Some sources provide only a small subset of the data via RSS 47

48 RSS Example

49 Twitter Search API (REST) How to use First, test the search on twitter.com/search Replace with and you will get: Execute this URL to do the search in the API Documentation 49

50 Twitter-Text (Java) Extractor extractor = new Extractor(); List<Extractor.Entity> entitylist = extractor.extractentitieswithindices(tweet.gettext()); for (Extractor.Entity entity : entitylist) { } switch (entity.gettype()) { } case URL: numurls++; break; case HASHTAG: numhashtags++; break; case MENTION: nummentions++; break; default: 50

51 Push feeds Commercial newswire / PR providers Vendor provides an API (usually closed-source) Login, subscribe, register call-back, feed Subscribe to everything if possible, filter yourself Call-back function called with each new post Usually passed as an XML-annotated string Very little processing time (hundreds/s) Cache and move on, index asynchronously Vendors focus on latency (ms) They provide classification, entity extraction tags Reliable timestamps 51

52 Real-time content streams Social media sites User generated content Users can tag their own and other s content Users can share favorites, tags, etc., with others Examples: Digg, Twitter, Flickr, YouTube, Del.icio.us, CiteULike, MySpace, Facebook, and LinkedIn Another example: Wikipedia

53 Real-time content streams: Twitter High volume stream Lots of users Lots of posts Temporally relevant information breaking news popular trends Information related to people information about people of interest general sentiment and opinion. 53

54 Twitter Streaming API 54

55 Twitter Streaming API Clients Twitter s Hosebird Automatic reconnections with appropriate backfill counts Access to raw bytes payload Proper backoffs/retry schemes HBC-Twitter4J module Twitter4J Standard Java library (Unofficial) 55

56 HBC-Twitter4J (Java client) // client is our Client object // msgqueue is our BlockingQueue<String> of messages that the handlers will receive from // listeners is a List<StatusListener> of the t4j StatusListeners Twitter4jClient t4jclient = new Twitter4jStatusClient(client, msgqueue, listeners, executorservice); t4jclient.connect(); // Establish a connection t4jclient.connect(); for (int threads = 0; threads < numprocessingthreads; threads++) { // This must be called once per processing thread t4jclient.process(); } 56

57 HBC-Twitter4J (Java client) StatusListener listener = new StatusStreamHandler() public void onstatus(status status) { // when a status message is received } } listeners.append(listener); 57

58 Web Mining Evidences and open information extraction 58

59 Web Mining Many other types of information are important for search applications e.g., linked documents, author information, named entities Retrieval algorithms can be specified based on any contentrelated features that can be extracted Metadata, publishing date

60 Web Dynamics Documents Lifetime and aging of documents Other measures of recency Recency and synchronization policies User s Behaviour Browsing behaviour Surfing models Search behaviour 60

61 Integrating linked pages Field-based weighting Field1: Content Field2: Linked Content Virtual Document Virtual Document Content Linked Content 61

62 Wikipedia edits 62

63 Wikipedia page views API 63

64 Summary Crawling the Web Web technologies Duplicate detection Removing noise Crawling document feeds Pull Feeds Push Feeds Real-time content streams Web Mining 64

65 Bibliography Section 15: Section 3: Section 19-20: 65

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