Journal reference: Computer Networks and ISDN Systems, Volume 28, issues 7–11, p. 963.

An Investigation of Documents from the World Wide Web

Allison Woodruff
Paul M. Aoki
Eric Brewer
Paul Gauthier
Lawrence A. Rowe

Computer Science Division
University of California at Berkeley
Berkeley, CA 94720-1776

WWW pages: Woodruff, Aoki, Brewer, Gauthier and Rowe
email: Woodruff, Aoki, Brewer, Gauthier and Rowe

We report on our examination of pages from the World Wide Web. We have analyzed data collected by the Inktomi Web crawler (this data currently comprises over 2.6 million HTML documents). We have examined many characteristics of these documents, including: document size; number and types of tags, attributes, file extensions, protocols, and ports; the number of in-links; and the ratio of document size to the number of tags and attributes. For a more limited set of documents, we have examined the following: the number and types of syntax errors and readability scores. These data have been aggregated to create a number of ranked lists, e.g., the ten most-used tags, the ten most common HTML errors.
HTML, statistics, tools, World Wide Web.



We report the results of an extensive analysis of HTML documents from the World Wide Web. Our data set, collected by the Inktomi Web crawler, currently comprises over 2.6 million HTML documents. We present a broad range of statistics pertaining to these pages.

Such an analysis of the content of HTML documents is of interest for several reasons:

Despite these motivations, however, previous studies relating to the Web have either focused on other topics or have been limited in scope. The most closely related work includes: To complement the above work, we have conducted a large-scale investigation of the content of HTML documents from the Web. The remainder of this paper is structured as follows. First, we describe the tools we used to perform our study. We next discuss the scope of our study and our results. Finally, we present some lessons learned and possible future directions.


The tools used to perform the data collection and data analysis for this study represent the integration of software from a variety of sources. Specifically, we have developed or adapted software to perform the following tasks: We discuss each set of tools in turn.

Web Data Collection

The Inktomi research project at Berkeley, consisting of Prof. Eric Brewer and graduate student Paul Gauthier, conducts research in the construction of scalable Web servers using parallel processing technology. To date, the project has produced two major software components: a parallel Web crawler and a parallel Web index search engine. In this paper, where we mention Inktomi, it may be assumed that we refer to the crawler.

The data presented in this study comes entirely from Inktomi. The high speed of the crawler enables us, for the first time, to consider taking ``snapshots'' of the Web and analyzing them. As of this writing, the Inktomi team has crawled twice. The first set of runs, from July to October 1995, collected 1.3 million unique HTML documents. The second set of runs, in November 1995, collected 2.6 million unique HTML documents.

HTML Data Extraction and Manipulation: libink

Although toolkits such as the W3C Reference Library [FRYS94] already exist for manipulating HTML and HTTP objects, we have developed our own special-purpose library, libink. This was necessitated by the fact that our performance and functionality needs were very different from those of the other toolkit developers.

libink consists of four major subcomponents:

Natural Language Analysis: style

We scored English language documents using the standard UNIX style program [CHER81]. style reports a variety of statistical properties of each document, such as the average sentence length and the number of complex sentences. It also scores the document using four readability metrics. These metrics indicate the nominal educational (grade) level a reader would need to understand the document.

Since most HTML documents do not conform to an internationalization standard, we applied heuristics to screen out non-English documents. We filtered out documents that contained any character with the high bit set (indicating a non-ASCII character set) or containing character sequences indicating known encodings (such as the Shift-JIS encoding of the Japanese character set).

Markup Language Analysis: weblint

We scored documents using weblint [BOWE96], an analogue to the standard UNIX lint utility, written in Perl. We modified weblint to report the classes of errors in a document rather than a line-by-line analysis.


We examined over 2.6 million HTML documents collected by the Inktomi crawler in November of 1995. Although Inktomi occasionally downloads non-HTML documents, the results presented reflect only HTML documents. (For example, we filtered out all binary files, such as images.) Furthermore, because Inktomi implements the Robot Exclusion Standard, the contents of automated databases which follow the standard (e.g., genome data sets) have also been excluded. The distribution of the documents in the data set by domain appears in Table 1.

Table 1: Documents Studied by Domain
Domain# of HTML Documents % of Total
other1064318 41%
com516709 20%
edu698616 27%
gov117125 4%
net113595 4%
mil14734 1%
org89939 3%
total2615036 100%

Here, ``other'' includes all domains other than the given top-level domains. For example, ``other'' contains all non-US top-level domains (such as Germany's .de).

We analyzed a variety of properties of these documents. In this paper, we present results on the following:

Document Size

After all markup had been extracted, the size of each HTML document was measured. For the entire data set, the mean size was 4.4KB, the median size was 2.0KB, and the maximum size was 1.6MB.

Figure 1 presents different views of the size distribution. On first inspection, this distribution appears to be exponential (the magenta line represents the location of the mean). However, further zooming indicates a curve before the distribution begins to taper off. The final graph in Figure 1 contains a semilog plot of the same data (in which the sizes are plotted logarithmically and the number of documents is plotted arithmetically).

Figure 1: Size Distribution

These simple size distribution plots proved to be very useful in detecting several problems with the data set. Many of the outliers were caused by one of two major classes of errors:

Tag/Size Ratio

For each document we examined the ratio of the total number of tags to its size. Figure 2 contains the results. An interesting pattern emerges - rays radiating out from the origin, indicating a number of documents with constant tag/size ratios. One such ray is indicated by the green ellipse. We examined a number of these rays and determined that they represented different versions of the same document (occurring in archives or mirrored sites). This suggests that the tag/size ratio might be used as a component of a signature for an HTML document, e.g., for purposes of copy detection.

Figure 2: Tag/Size Ratio

Tag Usage

We examined the distribution of tags. We obtained a list of valid tags from the Sandia HTML Reference Manual [HANN95]. The average number of total tags per document was 71. The average number of unique tags per document was 11.

We examined the most popular tags. The top graph of Figure 3 shows the top ten tags (ranked according to the number of documents in which the tag appeared at least once). The bottom graph indicates the average number of occurrences of the tag per document.

Figure 3: Ten Most-Used Tags

We also examined the least popular tags. Several tags, BDO, COLGROUP, and NOEMBED were used zero times in our data set of over 2.6 million HTML documents. A number of other tags appeared a very limited number of times.

Attribute Usage

We examined the distribution of attributes. The average number of total attributes per document was 29. The average number of unique attributes per document was 4.

We examined the most popular attributes. Figure 4 shows the top ten attributes (ranked according to the number of documents in which the attribute appeared at least once). HREF appeared an average of 14 times per document.

Figure 4: Ten Most-Used Attributes

We also examined the least popular attributes. Several attributes, ACCEPT-CHARSET, AXIS, CHAROFF, and CONTROLS, were used zero times in our data set of 2.6 million HTML documents. A number of other attributes appeared a very limited number of times.

Browser-specific Extension Usage

We also studied the use of browser-specific extensions. These consist of HTML features (i.e., tags or attributes) added by vendors rather than by the standards process. Here, we contrast the use of such extensions in the first Inktomi data set (1.3 million documents, collected in mid-1995) and the second Inktomi data set (2.6 million documents, collected in November 1995).

Figure 5 shows the percentage of documents in which the four most popular extensions are used. The usage of most of these features has risen dramatically, indicating wide user acceptance. Other features, such as BLINK, have not experienced such growth.

Figure 5: Browser-Specific Extensions Usage

Figure 6 indicates the popularity of various proposals for dynamic addition of functionality to browsers. APP and APPLET support SunSoft's Java ``applet'' language, DYNSRC supports VRML markup, and EMBED supports Netscape's third-party ``plug-in'' modules. All have enjoyed significant growth, though the oldest and most popular method (Java, first released in May 1995 [KARP95]) still has very low usage.

Figure 6: Browser-Specific Extensions Usage

Port Usage

For each of the HTML documents in our data set, we extracted the port number used to access the document. We analyzed the distribution of port numbers. While 418 unique ports were observed, six ports accounted for over 98% of the documents. Table 2 presents the most popular ports.

Table 2: Port Usage
Category Port % of Docs
Standard 80 93.6%
< 1024 70 0.3%
>= 1024 8000 0.5%
8001 0.5%
8080 0.7%
8888 2.8%

Port 80, the standard HTTP port, was used for approximately 94% of the documents. Port 70 (the standard Gopher port) was used for approximately 0.3% of the documents (this number is slightly lower than the 1% usage of port 70 observed in our earlier data set). We checked many of the documents being served from port 70; all the ones we examined were in fact HTML documents. Ports 8000, 8001, and 8080, and 8888 accounted for the majority of the remaining documents. The strong preference for ``8'' and ``80'' in the non-standard ports is presumably related to the standard port number ``80''

Protocols Used in Child URLs

As discussed above, we extracted child URLs from all HTML documents in our data set. Figure 7 presents the distribution of protocols in this set of child URLs. By far, the most dominant protocol observed was HTTP (there were an average of 17 HTTP URLs per document).

Figure 7: Protocol Usage

File Types Used in Child URLs

We also studied the distribution of file types described in the set of extracted child URLs. We inferred the file type from the file name extensions (e.g., ``.gif'') found in the URL path. In Table 3, the ``% of Docs'' column indicates the percentage of documents which contained a file of a given type. The ``# of Occurrences'' column shows the total number of extensions of the given file type that were observed. The ``# of Docs'' column indicates the number of documents which contained one or more extensions of the indicated type. Note that files can be counted multiple times, e.g., would be counted as a file having both ``.ps'' and ``.Z'' extensions.

Table 3: File Type and File Name Extensions
Category Type (Extension) % of Docs # of Occurrences # of Docs
Compression/Archive GNU zip (gz/gzip/taz/tgz) 0.7% 126839 18694
Zip (zip) 0.7% 157918 17277
compress (Z) 0.6% 121519 16857
BinHex (hqx) 0.3% 138259 7188
StuffIt (sea) 0.1% 5290 2615
LHArc (lha/lharc) 0.0% 20985 597
ARC archive (arc) 0.0% 432 129
Document HTML (htm/html) 76.3% 21982792 1995731
text (txt) 2.2% 325165 57476
PostScript (eps/ps) 1.8% 239949 46977
MS Word (doc) 0.2% 20153 5959
Adobe Acrobat (pdf) 0.2% 30640 5360
TeX DVI (dvi) 0.2% 14680 4163
Tex (tex) 0.1% 11998 2993
TROFF (man/me/ms) 0.1% 6488 2191
Rich Text (rtf) 0.0% 3921 1184
Maker Interchange (mif) 0.0% 262 113
Audio Sun audio (au) 0.7% 60405 18865
MS WAVE (wav) 0.3% 24361 7325
Audio IFF (aif/aifc/aiff) 0.1% 7761 2611
MIME audio (snd) 0.0% 1839 600
Amiga MOD (mod/nst) 0.0% 4202 254
IRCOM (sf) 0.0% 353 161
IFF (iff) 0.0% 322 47
SoundBlaster (voc) 0.0% 122 27
U-law (ul) 0.0% 21 19
FSSD (fssd/hcom) 0.0% 3 3
Image GIF (gif) 61.7% 9990239 1614244
JPEG (jpe/jpeg/jpg) 7.8% 811353 205088
X bitmap (xbm) 2.9% 968410 75825
TIFF (tif/tiff) 0.2% 22546 5416
X pixmap (xpm) 0.0% 3448 814
RGB (rgb) 0.0% 985 259
portable pixmap (ppm) 0.0% 646 124
portable graymap (pgm) 0.0% 219 78
portable bitmap (pbm) 0.0% 114 70
X window dump (xwd) 0.0% 277 66
raster (ras) 0.0% 221 54
portable anymap (pnm) 0.0% 51 7
Movie MPEG (mpe/mpeg/mpg) 0.3% 21496 7460
QuickTime (mov/qt) 0.2% 15026 5199
MS video (avi) 0.1% 5589 1742
SGI (movie) 0.0% 538 313

Number of In-links

We sorted the child URLs which we extracted according to the number of times they occurred in our data set. This showed us the most ``popular'' sites, as measured by the number of in-links observed. These appear in Table 4.

The in-link entries marked with (*) indicate sites that are highly self-referential. That is, these sites (by inspection) appear to contain a great number of links to their own top-level pages. It would probably be instructive to count only links from outside a given site.

Table 4: Most-linked-to URLs
SiteDescriptionIn-links Xerox PARC (*) 28188 Yahoo 19424 Cool Site of the Day 19028 Bible (in Finnish) (*) 17243 CERN preprint service (*) 16049 Best of the Web '94 14685 U.S. Gazetteer 14369 Cell database (*) 12750 Netscape Communications 12081 Ultimate Band List 11014 Comprehensive TeX Archive Network 10650 IBM Corp. 10617 Bibliography Server on Database Systems & Logic Programming (*) 10212 wusage 3.2 (WWW usage statistics) 9038 Jane Austen's Pride & Prejudice (*) 8928 StarWave 8721 Rob & Jen's Genealogy Page (*) 8476 Japan Information Center of Science and Technology 8331 NetSurf mailing list (*) 8036


The UNIX utility style was used to assess the readability level of a subset of the HTML documents in our data set (approximately 150,000). We remove HTML markup before invoking style on each document. We do this for two reasons. First, style does not understand HTML, so the extra punctuation would confuse its analyzer. Second, breaking English text into sentences and sentence fragments can be tricky and we need to provide the style analyzer with some assistance. For example, it is not always clear when a bulleted list should be ignored, treated as a single long sentence, or treated as a list of individual sentences. When invoked on troff documents, style uses a set of heuristics to insert punctuation into text, using the markup to assist it [CHER81]. This information is then used by later passes of the analyzer to determine sentence and sentence fragment breaks. We use a similar set of heuristics to insert periods and commas into HTML documents as we strip out markup.

The numbers presented in Table 5 represent the scores of the different domains on the Kincaid readability test. Higher numbers represent more grammatical and lexical complexity. Lower numbers represent more simple structure and word choice. Documents with lower numbers are considered to be more ``readable''. The ``other'' domain is excluded because it represents extraordinarily diverse sources.

Table 5: Average Readability broken down by Domain
Domain Readability Score
com 10.3
edu 11.0
gov 10.0
net 12.3
mil 12.1
org 11.2

Syntax Errors

weblint was used to assess the syntactic correctness of a subset of the HTML documents in our data set (approximately 92,000). Figure 6 presents the top ten syntax errors ranked according to the percentage of documents in which they appear. (Note that ``netscape-attribute'' is not necessarily an error, but rather indicates the percentage of documents using Netscape-specific extensions.) Observe that over 40% of the documents in our study contain at least one error. Descriptions of the errors appear in Table 6.

Figure 6: Ten Most Common Syntax Errors

Error Name Explanation
html-outer outer tags should be <HTML> .. </HTML>
no-head missing <HEAD>
head-element heading-only tag (TITLE, NEXTID, LINK, BASE, META) found outside of heading
no-body missing <BODY>
must-follow required tag does not immediately follow another
unclosed-element unclosed elements (e.g., <H1> ... )
netscape-markup Netscape-specific tag
empty-container empty container element
mis-match mis-matched tag (e.g., <H1> ... </H2>)
heading-order order of headings (e.g., <H3> following <H1>)
Table 6: List of weblint Errors


We have reported the results of our examination of pages from the World Wide Web. Additional data not presented in the hardcopy version of this paper may be found at .


There are two maxims which are particularly apropos of our experience. First, dealing with large data sets is difficult and time-consuming. None of the existing tools which we used scaled adequately to dealing with a data set on the order of millions of documents.

Second, we observed empirically that the Web changes exceptionally quickly. Many properties of the documents in our first data set have altered in the months since the data was collected. The largest document in our data set was 1.6Mbytes; we checked the current size of that same document. It has grown to 9Mbytes. As another example, many of the most popular URLs in the first data set no longer exist.

Future Directions

A longitudinal study examining trends would be extremely interesting. Our limited observation reveals that while certain charactertistics change fairly quickly (e.g., new features are introduced) others appear to change more slowly (e.g., average document size and reading level did not appear to change between the time periods we observed). One could also consider how the introduction of new tools impact these characteristics. For example, as authoring tools become more common, one could study their impact on the number and type of syntax errors.

Structural graph analysis has many applications in this area. In particular, analysis of the kind practiced by sociologists in structural network analysis [WASS94] promises insight. However, existing social network algorithms are several orders of magnitude more complex than is viable for a data set of this size. Significant work would have to be done to make such analysis feasible.

It would also be interesting to allow user-defined queries against the data set. The simplest functionality would be to allow a user to ascertain how a form-specified URL compared with the data set. A more interesting and complex interface would allow the user to define arbitrary queries on the data set.


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About the authors

Allison Woodruff is a PhD student in the Electrical Engineering and Computer Science Department at the University of California, Berkeley. Her research interests include spatial information systems, multimedia databases, visual programming languages, and user interfaces. She has worked as a geographic information systems specialist for the California Department of Water Resources. Woodruff holds a BA in English from California State University, Chico and an MA in Linguistics and an MS in Computer Science from the University of California, Davis.

Paul M. Aoki is a PhD student in the Department of Electrical Engineering and Computer Sciences at the University of Califormia at Berkeley. He holds a B.S. in Electrical Engineering and a M.S. in Computer Science from the University of California at Berkeley. His research interests include query optimization for parallel and distributed databases and index support for non- traditional data types.

Eric Brewer is an Assistant Professor of Computer Science at the University of California at Berkeley, and received his PhD in CS from MIT in 1994. Interests include mobile and wireless computing (the InfoPad and Daedalus projects); scalable servers (the NOW and Inktomi projects); and application- and system-level security (The ISAAC project and Netscape security holes). Previous work includes multiprocessor-network software and topologies (Strata, metabutterflies), high performance multiprocessor simulation (Proteus).

Paul Gauthier has served as Director and Vice President of Research and Development of Inktomi Corporation since February 1996. Mr. Gauthier is also in the doctorate program in the Department of Electrical Engineering and Computer Sciences at the University of California at Berkeley, where he is working towards a doctoral degree in computer science. Mr. Gauthier holds a Bachelor of Science degree, with honors, in Computer Science from Dalhousie University (located in Nova Scotia, Canada).

Professor Rowe received a BA in mathematics and a PhD in information and computer science from the University of California at Irvine in 1970 and 1976, respectively. Since 1976 he has been on the faculty at the University of California at Berkeley where he is now a Professor of Electrical Engineering and Computer Science and the founding director of the Berkeley Multimedia Research Center. His current research interests are multimedia applications, systems, and databases on which he has published over fifty papers. He is an editorial board member for the ACM Multimedia Systems Journal. Professor Rowe heads the research group that developed the Berkeley Distributed Video-on-Demand System, algorithms to compute special effects on compressed images, the Berkeley Continuous Media Toolkit, and the Berkeley MPEG1 video tools.