Real Users and Real Needs: A Study and Analysis of User Queries on the Web

Full text

Real life, real users, and real needs: a study and analysis
of user queries on the web
Bernard J. Jansen
a
, Amanda Spink
b,
*, Tefko Saracevic
c
a
Department of Electrical Engineering and Computer Science, United States Military Academy, West Point,
NY 10996, USA
b
School of Information Sciences and Technology, The Pennsylvania State University, State College, PA 16801, USA
c
School of Communication, Information and Library Studies, Rutgers University, 4 Huntington Street, New Brunswick,
NJ 08903, USA
Abstract
We analyzed transaction logs containing 51,473 queries posed by 18,113 users of Excite, a major
Internet search service. We provide data on: (i) sessions Ð changes in queries during a session, number
of pages viewed, and use of relevance feedback; (ii) queries Ð the number of search terms, and the use
of logic and modi®ers; and (iii) terms Ð their rank/frequency distribution and the most highly used
search terms. We then shift the focus of analysis from the query to the user to gain insight to the
characteristics of the Web user. With these characteristics as a basis, we then conducted a failure
analysis, identifying trends among user mistakes. We conclude with a summary of ®ndings and a
discussion of the implications of these ®ndings. #2000 Elsevier Science Ltd. All rights reserved.
1. Introduction
A panel session at the 1997 ACM Special Interest Group on Research Issues In Information
Retrieval conference entitled ``Real Life Information Retrieval: Commercial Search Engines''
included representatives from several Internet search services. Doug Cutting represented Excite,
one of the major services. Graciously, he oered to make available a set of user queries as
submitted to his service for research. The analysis we present here on the nature of sessions,
queries, and terms resulted from this oer. Interestingly, the ®rst two authors expressed their
Information Processing and Management 36 (2000) 207±227
0306-4573/00/$ - see front matter #2000 Elsevier Science Ltd. All rights reserved.
PII: S 0 3 0 6 -4 5 7 3 ( 9 9 ) 0 0 0 5 6 - 4
www.elsevier.com/locate/infoproman
* Corresponding author.

interest independently of each other, then met via email, exchanged messages and data, and
conducted collaborative research exclusively through the Internet, before ever meeting in
person at a Rutgers conference in February 1998, when the results were ®rst presented. In
itself, this is an example of how the Internet changed and is changing the conduct of research.
We will argue in the conclusions that real life Internet searching is changing information
retrieval (IR) as well. While Internet search engines are based on IR principles, Internet
searching is very dierent from IR searching as traditionally practised and researched in online
databases, CD-ROMs and online public access catalogs (OPACS). Internet IR is a dierent
IR, with a number of implications that could portend changes in other areas of IR as well.
With the phenomenal increase in usage of the Web, there has been a growing interest in the
study of a variety of topics and issues related to use of the Web. For instance, on the hardware
side, Crovella and Bestavros (1996) studied client-side trac; and Abdulla, Fox and Abrams,
(1997) analyzed server usage. On the software side, there have been many descriptive
evaluations of Web search engines (e.g. Lynch, 1997). Statistics of Web use appear regularly
(e.g. Kehoe, Pitkow & Morton, 1997; FIND/SVP, 1997), but as soon as they appear, they are
out of date. The coverage of various Web search engine services was analyzed in several works.
A recent article on this topic by Lawrence and Giles (1998) attracted a lot of attention. The
pattern of Web sur®ng by users was analyzed as well (Huberman, Pirolli, Pitkow & Lukose,
1998). However, to date there has been no large-scale, quantitative or qualitative study of Web
searching.
How do they search the Web?What do they search for on the Web? These questions are
addressed in a large scale and academic manner in this study. Given the recent yearly
exponential increase in the estimated number of Web users, this lack of scholarly research is
surprising and disappointing. In contrast, there have been an abundance of user studies of on-
line public access categories (OPAC) users. Many of these studies are reviewed in Peters (1993).
Similarly, there are numerous studies of users of traditional IR systems. The combined
proceedings of the International Conference on Research Issues in Information Retrieval
(ACM SIGIR) present many of these studies.
In the area of Web users, however, there were only two narrow studies that we could ®nd.
One focused on the THOMAS system (Croft, Cook & Wilder, 1995) and contained some
general information about users at that site. However, this study focused exclusively on the
THOMAS Web site, did not attempt to characterize Web searching in a systematic way, and is
devoted primarily to a description of the THOMAS system. The second paper was by Jones,
Cunningham and McNab (1998) and focused again on a single Web site, the New Zealand
Digital Library, which contains computer science technical reports. Given the technical nature
of this site, it is questionable whether these users represent Web users in general. There is a
small but growing body of Web user studies compared to the numerous studies of OPAC and
IR system use.
In this paper, we report results from a major and ongoing study of users' searching behavior
on the Web. We examined a set of transaction logs of users' searches from Excite (http://
www.excite.com). This study involved real users, using real queries, with real information
needs, using a real search engine. The strength of this study is that it involved a real slice of
life on the Web. The weakness is that it involved only a slice Ð an observable artifact of what
the users actually did, without any information about the users themselves or about the results
B.J. Jansen et al. / Information Processing and Management 36 (2000) 207±227208

and uses. Users are anonymous, but we can identify one or a sequence of queries originating
with a speci®c user. We know when they searched and what they searched for, but we do not
know anything beyond that. We report on artifactual behavior, but without a context.
However, the observation and analysis of such behavior provide for a fascinating and
surprising insight into the interaction between users and the search engines on the Web. More
importantly, this study provides detailed statistics currently lacking on Web user behavior. It
also provides a basis for comparison with similar studies of user searching of more traditional
IR and OPAC systems.
The Web has a number of search engines. The approaches to searching, including
algorithms, displays, modes of interaction and so on, vary from one search engine to another.
Still, all Web search engines are IR tools for searching highly diverse and distributed
information resources as found on the Web. But by the nature of the Web resources, they are
faced with dierent issues requiring dierent solutions than the search engines found in well
organized systems, such as in DIALOG, or in lab experiments, such as in the Text Retrieval
Conference (TREC) (Sparck Jones, 1995). Moreover, from all that we know, Web users span a
vastly broader and thus probably dierent population of users (Spink, Bateman & Jansen,
1999) and information needs, which may greatly aect the queries, searches, and interactions.
Thus, it is of considerable interest to examine the similarities and/or dierences in Web
searching compared to traditional IR systems. In either case, it is potentially a very dierent
IR.
The signi®cance of this study is the same as all other related studies of IR interaction,
queries and searching. By axiom and from lessons learned from experience and numerous
studies:
``The success or failure of any interactive system and technology is contingent on the extent
to which user issues, the human factors, are addressed right from the beginning to the very
end, right from theory, conceptualization, and design process to development, evaluation, and
to provision of services'' (Saracevic, 1997).
2. Related IR studies
In this paper, we concentrate on users' sessions, queries, and terms as key variables in IR
interaction on the Web. While there are many papers that discuss many aspects of Web
searching, most of those are descriptive, prescriptive, or commentary. Other than the two
mentioned previously, we could not ®nd any similar studies of Web searching. However, there
were several studies that included data on searching of existing, mostly commercial, IR
systems, and we culled data from those to provide a basis for comparison between searches as
done on the Web and those as done on IR systems outside the Web. A representative sample
of such studies is reviewed.
The studies cited below concentrated on dierent aspects and variables related to searching,
using dierent methodologies and are dicult to compare. Still, each of them had data on the
mean number of search terms in queries constructed by the searchers under study as follows:
.Fenichel (1981): Novice searchers: 7.9. Moderately experienced: 9.6. Experienced: 14.4
B.J. Jansen et al. / Information Processing and Management 36 (2000) 207±227 209

.Hsieh-yee (1993): Familiar topics: Novices: 8.77. Experienced: 7.28. Non-familiar topics:
Novices: 9.67. Experienced: 9.00
.Bates, Wilde and Siegfried (1993): Humanities scholars: 14.95
.Spink and Saracevic (1997): Experienced searchers: 14.8.
The studies indicated that searches by various populations contain a range of some 7±15 terms.
As will be discussed below, this is a considerably higher range than the mean number of terms
found in this study that concentrated on Web searches from the Excite search engine.
3. Background on Excite and data
Founded in 1994, Excite Inc. is a major Internet media public company which oers free
Web searching and a variety of other services. The company and its services are described at
its Web site (http://www.excite.com), thus not repeated here. Only the search capabilities
relevant to out results are summarized.
Excite searches are based on the exact terms that a user enters in the query, however,
capitalization is disregarded, with the exception of logical commands AND, OR, and AND
NOT. Stemming is not available. An online thesaurus and concept linking method called
Intelligent Concept Extraction (ICE) is used, to ®nd related terms in addition to terms entered.
Search results are provided in ranked relevance order. A number of advanced search features
are available. Those that pertain to our results are described here:
.As to search logic, Boolean operators AND, OR, AND NOT, and parentheses can be used,
but these operators must appear in ALL CAPS and with a space on each side. When using
Boolean operator ICE (concept-based search mechanism) is turned o.
.A set of terms enclosed in quotation marks (no space between quotation marks and terms)
returns web sites with the terms as a phrase in the exact order they were entered.
.A + (plus) sign before a term (no space) requires that the term must be in an answer.
.Aÿ(minus) sign before a term (no space) requires that the term must NOT be in an
answer. We denote plus and minus signs, and quotation marks, as modi®ers.
.A page of search results contains ten answers at a time ranked as to relevance. For each site
provided is the title, URL (Web site address), and a summary of its contents. Results can
also be displayed by site and titles only. A user can click on the title to go to the Web site.
A user can also click for the next page of ten answers. In addition, there is a clickable
option More Like This, which is a relevance feedback mechanism to ®nd similar sites.
.When More Like This is clicked, Excite enters and counts this as a query with zero terms.
Each transaction record contained three ®elds. With these three ®elds, we were able to locate a
user's initial query and recreate the chronological series of actions by each user in a session:
1. Time of Day: measured in hours, minutes, and seconds from midnight of 9 March 1997.
2. User Identi®cation: an anonymous user code assigned by the Excite server.
3. Query Terms: exactly as entered by the given user.
Focusing on our three levels of analysis Ð sessions, queries, and terms Ð we de®ned our
variables in the following way.
B.J. Jansen et al. / Information Processing and Management 36 (2000) 207±227210

1. Session: A session is the entire series of queries by a user over a number of minutes or
hours. A session could be as short as one query or contain many queries.
2. Query: A query consists of one or more search terms, and possibly includes logical
operators and modi®ers.
3. Term: A term is any unbroken string of characters (i.e. a series of characters with no space
between any of the characters). The characters in terms included everything Ð letters,
numbers, and symbols. Terms were words, abbreviations, numbers, symbols, URLs, and any
combination thereof. We counted logical operators in capitals as terms. However, in a
separate analysis we isolated them as commands, not terms.
The raw data collected are very messy. Users entered terms, commands and modi®ers in all
kinds of ways, including many misspellings and mistakes. In many cases, Excite conventions
were not followed. We count these deviations as mistakes and report them in the failure
analysis portion of the paper. For the most part, we took the data `as is,' i.e., we did not
`clean' the data in any way Ð these queries represent real searches by real users. The only
normalization we undertook in one of the counts (unique terms Ð not case sensitive) was to
disregard capitalization, because Excite disregards it as well. (i.e. TOPIC, topic and Topic
retrieve the same answers). Excite does not oer automatic stemming, thus topic and topics
count as two unique terms, and `?' or `

' as stemming commands at the end of terms are
mistakes, but when used counted as separate terms. We also analyzed a cleaned set of terms,
that is we removed term modi®ers such as the + or ÿsigns. We took great care in the
derivation of counts due to the `messiness' of data. This paper extends ®ndings from (Jansen,
Spink, Bateman & Saracevic, 1998a,b; Jansen, Spink & Saracevic, 1998c).
4. Results
First, what is the pattern of user queries? We looked at the number of queries by each
speci®c user and how successive queries diered from other queries by the same user. We
classi®ed the 51,474 queries as to unique, modi®ed,oridentical as shown in Table 1.
A unique query was the ®rst query by a user (this represents the number of users). A
modi®ed query is a subsequent query in succession (second, third ...) by the same user with
terms added to, removed from, or both added to and removed from the unique query. Unique
and modi®ed queries together represent those queries where the user did something with terms.
Identical queries are queries by the same user that are identical to the query preceding it. They
Table 1
Unique, modi®ed, and identical queries
Query type Number Percent of all queries
Unique 18,098 35%
Modi®ed 11,249 22%
Identical 22,127 43%
Total 100%
B.J. Jansen et al. / Information Processing and Management 36 (2000) 207±227 211

can come about in two ways. The ®rst possibility is that the user retyped the query. Studies
have shown that users often do this (Peters, 1993). The second possibility is that the query was
generated by Excite. When a user views the second and further pages (i.e., a page is a group of
10 results) of results from the same query, Excite provides another query, but a query that is
identical to the preceding one. Our analysis did not allow disambiguation of these two causes
of identical queries.
The unique plus modi®ed queries (where users actively entered or modi®ed terms) amounted
to 29,437 queries or 57% of all queries. If we assume that all identical queries were generated
as a request to view subsequent pages, then 43% of queries come as a result of a desire to view
more pages after the ®rst one. Modi®cations and viewing are further elaborated in the next two
tables.
4.1. Modi®cations
Some users used only one query in their session, others used a number of successive queries.
The average session, including all three query types, was 2.84 queries per session. This means
that a number of users went on to either modify their query, view subsequent results, or both.
The average session length, ignoring identical queries, was 1.6 queries per user. Table 2 lists the
number of queries per user.
This analysis includes only the 29,337 unique and modi®ed queries. We ignored the identical
queries because as stated above, it was impossible to interpret them meaningfully in this
context, in order to concentrate only on those queries where users themselves did something to
the queries. A substantial majority of users (67%) did not go beyond their ®rst and only query.
Table 2
Number of queries per user
Queries per user Number of users Percent of users
1 12,068 67
2 3501 19
3 1321 7
4 583 3
5 287 1.6
6 144 0.80
7 79 0.44
8 32 0.18
9 36 0.20
10 17 0.09
11 7 0.04
12 8 0.04
13 15 0.08
14 2 0.01
15 2 0.01
17 1 0.01
25 1 0.01
B.J. Jansen et al. / Information Processing and Management 36 (2000) 207±227212

Query modi®cation was not a typical occurrence. This ®nding is contrary to experiences in
searching regular IR systems, where modi®cation of queries is much more common. Having
said this, however, 33% of the users did go beyond their ®rst query. Approximately 14% of
users had entered three or more queries. These percentages of 33% and 14% are not
insigni®cant proportions of system users. It suggests that a substantial percentage of Web users
do not ®t the stereotypical naõ
Ève Web user. These sub-populations of users should receive
further study. They could represent sub-populations of Web users with more experience or
higher motivation who perform query modi®cation on the Web.
We also examined how user modi®ed their queries. These results are display in Table 3.
Here we concentrate on the 11,247 queries that were modi®ed by either an increase or a
decrease in the number of terms from one user's query to that user's next query (i.e., successive
queries by the same user at time T and T+1). Zero change means that the user modi®ed one
or more terms in a query, but did not change the number of terms in the successive query.
Increase or decrease of one means that one term was added to or subtracted from the
preceding query. Percent is based on the number of queries in relation to all modi®ed (11,247)
queries.
We can see that users typically do not add or delete much in respect to the number of terms
Table 3
Changes in number of terms in successive queries
Increase in terms Number Percent
0 3909 34.76
1 2140 19.03
2 1068 9.50
3 367 3.26
4 155 1.38
5 70 0.62
6 22 0.20
7 6 0.05
8 10 0.09
9 1 0.01
10 4 0.04
Decrease in terms Number Percent
ÿ1 1837 16.33
ÿ2 937 8.33
ÿ3 388 3.45
ÿ4 181 1.61
ÿ5 76 0.68
ÿ6 46 0.41
ÿ7 14 0.12
ÿ8 8 0.07
ÿ9 2 0.02
ÿ10 6 0.05
B.J. Jansen et al. / Information Processing and Management 36 (2000) 207±227 213

in their successive queries. Modi®cations to queries are done in small increments, if at all. The
most common modi®cation is to change a term. This number is re¯ected in the queries with
zero (0) increase or decrease in terms. About one in every three queries that is modi®ed still had
the same number of terms as the preceding one. In the remaining 7338 successive queries where
terms were either added or subtracted about equal number had terms added as subtracted (52±
48%) Ð thus users go both ways in increasing and decreasing number of terms in queries.
About one in ®ve queries that is modi®ed has one more term than the preceding one, and about
one in six has one less term.
4.2. Viewing of results
Excite displays query results in groups of ten. Each time that a user accesses another group
of 10, which we term another page, an identical query is generated. We analyzed the number
of pages each user viewed and the percentage that this represented based on the total number
of users. The results are shown in Table 4.
The mean number of pages examined per user was 2.35. Most users, 58% of them, did not
access any results past the ®rst page. Were they so satis®ed with the results that they did not
need to view more? Were a few answers good enough? Is the precision that high? Are the users
after precision? Or did they just give up and get tired of viewing results? Using only
transaction logs, we cannot provide answers to these questions. But in any case, this result
combined with the small number of queries per session has interesting implications for recall
and may illustrate a need for high precision in Web IR algorithms. For example, using a
classical measurement of precision, any search result beyond the tenth position in the list
would be meaningless for 58% of Web users. Another possible interpretation is that people use
partially relevant items in the ®rst page to avoid further searching through subsequent pages.
Given the hypertext nature of the Web, partially relevant items (Spink, Greisdorf & Bateman,
1998) in the top ten maybe used as a jumping o point to ®nd relevant items. For example, a
user looking for a faculty member's homepage at a university does not retrieve the faculty's
homepage in the top ten but gets the university homepage. Rather than continue searching, the
user starts browsing, beginning with the university page.
4.3. Queries
From the session level of analysis, we then moved to the query level. The basic statistics
related to queries and search terms are given in Table 5.
We analyzed the queries based on length (i.e. number of terms), structure (use of Boolean
operators and modi®ers), and failure analysis (deviations from published rules of query
construction). We also identi®ed the number of users of Boolean logic and modi®ers.
4.3.1. Length
On the average, a query contained 2.21 terms. Table 6 shows the ranking of all queries by
number of terms.
Percent is the percentage of queries containing that number of terms relative to the total
number of queries. Web queries are short. About 62% of all queries contained one or two
B.J. Jansen et al. / Information Processing and Management 36 (2000) 207±227214

Table 4
Number of pages viewed per user
Pages viewed Number of users Percent of all users
1 10,474 58
2 3363 19
3 1563 9
4 896 5
5 530 3
6 354 2
7 252 1
8 153 0.85
9 109 0.60
10 85 0.47
11 75 0.41
12 47 0.26
13 31 0.17
14 29 0.16
15 25 0.14
16 28 0.15
17 13 0.07
18 4 0.02
19 14 0.08
20 9 0.05
21 3 0.02
22 4 0.02
23 5 0.03
24 7 0.04
25 4 0.02
26 7 0.04
27 2 0.01
28 3 0.02
29 1 0.01
32 4 0.02
33 1 0.01
40 1 0.01
43 1 0.01
49 1 0.01
50 2 0.01
55 1 0.01
Table 5
Numbers of users, queries, and terms
No. of
users
Total no.
of queries
Non-unique
terms
Mean no. of
terms per query (range)
Unique terms with
case sensitive
Unique terms without
case sensitive
18,113 51,473 113,793 2.21 (0±10) 27,459 21,862
B.J. Jansen et al. / Information Processing and Management 36 (2000) 207±227 215

terms. Fewer than 4% of the queries had more than 6 terms. As mentioned, we could not ®nd
any other data on Web searches from a major Web search engine, thus, the only comparisons
are with the two smaller studies by Croft et al. (1995) and Jones et al. (1998). The query length
observed in our research is similar to results from these two studies. This deviates signi®cantly
from traditional IR searching. As shown above, the mean number of search terms used in
searching of regular IR systems ranged from about 7 to 15. This is about three to seven times
higher than the mean number of times found in this study, and our count is on the high side,
because we counted operators as well. Admittedly, the circumstances and context between
searches done by users of IR systems such as DIALOG, and searches of the Web, done by the
general Internet population, may be vastly dierent. Thus this comparison may have little
meaning.
4.3.2. Relevance feedback
A note should be made on queries with zero terms (last row of Table 6). As mentioned,
when a user enters a command for relevance feedback (More Like This ), the Excite transaction
log counts that as a query, but a query with zero terms. Thus, the last row represents the
largest possible number of queries that used relevance feedback, or a combination of those and
queries where users made some mistake that triggered this result. Assuming they were all
relevance feedback, only 5% of queries used that feature Ð a small use of relevance feedback
capability. In comparison, a study involving IR searches conducted by professional searchers
as they interact with users found that some 11% of search terms came from relevance feedback
(Spink & Saracevic, 1997), albeit this study looked at human initiated relevance feedback.
Thus, in these two studies, relevance feedback on the Web is used half as much as in
traditional IR searches. This in itself warrants further study, particularly given the low use of
this potentially highly useful and certainly highly vaunted feature.
4.3.3. Structure
Next, we examined the structure of queries, focusing ®rst on how many of the 51,473 queries
explicitly utilized Boolean operators or modi®ers (see Table 7).
Table 6
Number of terms in queries (Nqueries=51,473)
Terms in query Number of queries Percent of all queries
10 185 0.36
9 125 0.24
8 224 0.44
7 484 0.94
6 617 1
5 2158 4
4 3789 7
3 9242 18
2 16,191 31
1 15,874 31
0 2584 5
B.J. Jansen et al. / Information Processing and Management 36 (2000) 207±227216

The Number column lists the number of queries that contained that particular Boolean
operator or modi®er. The next column is the percentage that number represents of all queries.
Incorrect means the number of queries containing a speci®c operator or modi®er that was
constructed not following Excite rules Ð they could be considered as mistakes. The last
column is the percentage of queries containing a given operator or modi®er that were
incorrectly constructed. We discuss the failures in a later section.
From Table 7, at least one thing is obvious Ð Boolean operators were not used much, with
AND receiving the greatest use. These numbers were signi®cantly lower than those reported by
Jones et al. (1998), and signi®cantly lower than studies of searches from IR systems and OPAC
systems [Croft et al. (1995) did not report this information]. Modi®ers were used a little more
often, with the `+' and ``'' (i.e., phrase searching) being used the most. For example, based on
what we reviewed so far in this paper, we have a large set of queries that are extremely short,
seldom modi®ed, and very simple in structure. Yet, the vast majority of users never viewed
anything beyond the ®rst 10 results. Is the recall and precision rate of Excite that good? Is
something else at work here? One interpretation may be that users only glance at the ®rst page
to see how poorly they performed their search. Rather than taking time to learn the detailed
procedures of Excite, they try anything (trial and error) and then try to judge from the hits
what they did wrong.
Table 7
Use of Boolean operators and modi®ers in queries (Nqueries=51,473)
Operator or modi®er Number of queries Percent of all queries Incorrect Percent incorrect
AND 4094 8 1309 32
OR 177 0.34 46 26
AND NOT 105 0.20 39 37
( ) 273 0.53 0 0
+ (plus) 3010 6 1182 39
ÿ(minus) 1766 3 1678 95
`` '' 3282 6 179 5
Table 8
Use of logic and modi®ers by users (Nusers=18,113)
Operator or modi®er Number of users using it Percent of all users Incorrect Percent incorrect
AND 832 5 418 50
OR 39 0 11 28
AND NOT 47 0 9 19
( ) 120 1 0 0
+ (plus) 826 5 303 30
ÿ(minus) 508 3 362 38
`` '' 1019 6 32 0
B.J. Jansen et al. / Information Processing and Management 36 (2000) 207±227 217

4.3.4. Number of users
In Table 8, we examine how many of the 18,113 users, opposed to the number of queries,
used any Boolean logic (®rst four rows) or modi®ers (last three rows) in their queries
(regardless of how many queries they had).
We relate these numbers to the number of queries. Incorrect means the number of users
committing mistakes by not following Excite rules as stated in instructions for use of these
operators and modi®ers. Percent incorrect is the proportion of those users using a given
operator or modi®er incorrectly or as a mistake. The user population that incorporated
Boolean operators was very small. Only 6% of the 18,113 users used any of the Boolean
capabilities, and these were used in less than 10% of the 51,473 queries. A minuscule
percentage of users and queries used OR or AND NOT. Only about 1% of users and 1
2%of
queries used nested logic as expressed by a use of parentheses. The `+' and `ÿ' modi®ers were
used by about the same number of people that used Boolean operators. Together `+' and `ÿ'
were used by 1334 or 7% of users in 4776 (9%) queries. The ability to create phrases (terms
enclosed by quotation marks) was also seldom used Ð only 6% of users and 6% of queries
used them. From this, it appears that a small number of users account for the occurrences of
the more sophisticated queries, indicating that there is little experimentation by users during
their sessions. About 5% of the users account for the 8.5% of queries that contained Boolean
operators. We discuss the rami®cations of this ®nding for system design later in the paper.
5. Failure analysis
Next, we turn to a discussion of the surprisingly high number of incorrect uses or mistakes.
When they used it, 50% of users made a mistake in the use of the Boolean AND; 28% made
an error in uses of OR, and only 19% used AND NOT incorrectly, but only 47 users, a
negligible percent, used AND NOT at all. The most common mistake was not capitalizing the
Boolean operator, as required by the Excite search engine. For example, a correct query would
be: information AND processing. The most common mistake would be: information and
processing.
When we look at queries, 32% contained an incorrect use of AND, 26% of OR, and 37%
of AND NOT. `AND' presents a special problem, so we did a further analysis. We had 4094
queries that used AND in some form (as `AND,' ``And, or `and'). Some queries had more than
one AND. Altogether, there were 4828 appearances of all forms of AND: 3067 as `AND', 41
as `And,' and 1720 as `and.' If considered as Boolean operators, the last two or 1761 instances
were mistakes. Most of them were, but not all. In a number of queries `and' was used as
conjunction e.g. as in query College and university harassment policy. Unfortunately, we could
not distinguish the intended use of `and' as a conjunction from that as a mistake, thus our
count of AND mistakes is on the high end.
There was a similarly high percentage of mistakes in the use of plus and minus operators Ð
respectively 30% and 38%. Most of the time, spaces were used incorrectly. Minus presents an
especially vexing problem, because it is also used in phrases such as pre-teen. Thus, our count
of mistakes is at the high end. It is easy to see that Web users are not up to Boolean, and even
less to follow searching rules. At the very least, system redesign seems to be in order. The most
B.J. Jansen et al. / Information Processing and Management 36 (2000) 207±227218

common mistake was stringing all the terms of the query together, as in a mathematical
formula. For example, a correct query would be: +information +processing. The most
common mistake would be: +information+processing (with no space between information
and the next +). Consistent spacing rules between Boolean operators and term modi®er may
solve this problem. In the use of Boolean operator, a space between the operator and the term
is required. With the use of term modi®ers, the space must not be there.
There were also a large number of queries that incorporated searching techniques that Excite
does not support. These failures can be classi®ed as a carry over from user learning associated
with other search engines, including those from other Web, OPAC, and IR systems. For
example, there were 26 occurrences of the proximity operator NEAR. There were 79 uses of
the `:' as a separator for terms. There were numerous occurrences of `.' used as a term
separator. The symbol `&' was used in-lieu of the Boolean AND over 200 times. These
symbols are common in many other search engines.
6. Terms
We also analyzed user queries according to the terms they included. A term was any series
of characters bounded by white space. There were 113,793 terms (all terms from all queries).
After eliminating duplicate terms, there were 21,862 unique terms that were non-case sensitive
(in other words, all upper cases are here reduced to lower case). In this distribution logical
operators AND, OR, NOT were also treated as terms, because they were used not only as
operators but also as conjunctions (we already discussed the case of `and.' and presented the
®gures for various forms of the term, thus subtraction can be easily done). We discuss terms
from the perspective of their occurrence, their ®t with known distributions, and classi®cation
into some broader subject headings.
6.1. Occurrences
We constructed a complete rank-frequency table for all 113,793 terms. Out of the complete
rank-frequency-table we took the top terms, i.e. those that appeared 100 times or more, as
presented in Table 9.
The 74 terms that were used 100 or more times across all queries appeared a total of 20,698
times as search terms. They represent only 0.34% of all unique terms, yet they account for
18.2% of all 113,776 search terms in all queries. If we delete the 9121 occurrences of 11
common terms that do not carry any content by themselves (and, of, the, in, for, +, on, to, or,
&, a), we are left with 63 subject terms that have a total frequency of 11,577 occurrences Ð
that is 0.29% of unique subject terms account for 10.3% of all terms in all queries. The high
appearance of `+' represents a probable mistake Ð the inclusion of space between the sign
and a term, as required by Excite rules.
Similarly, `&'' was used often as a part of an abbreviation, such as in AT&T, but also as a
substitute for logical AND, as in Ontario & map. In the latter case, it is a mistake and would
appear as a separate term. On the other end of the distribution we have 9790 terms that
appeared only once. These terms amounted to 44.78% of all unique terms and 8.6% of all
B.J. Jansen et al. / Information Processing and Management 36 (2000) 207±227 219

terms in all queries. The tail end of unique terms is very long and warrants in itself a linguistic
investigation. In fact, the whole area of query language needs further investigation. There are
no comprehensive studies of terms, the distribution of those terms, the modi®cation of those
terms, etc., of Web queries. Such studies have potential to bene®t IR system and Web site
development.
6.2. Term categories
In order to ascertain some broad subjects of searching, we classi®ed the 63 top subject terms
into a set of common themes. Admittedly, such a classi®cation is arbitrary and each reader can
use his/her own criteria. Still a rough picture emerges. These subjects are displayed in Table 10.
About 25% of the highest used terms apparently dealt with some or other sexual topic.
However, that represents fewer than 3% of all terms. Of course, if one classi®es additional
terms further down the distribution (such as those listed in the ``Gender'' category as Sexual )
the percent will be higher. We perused the rest of the terms and came to the conclusion than
no more than some two dozen of the other terms will unmistakably fall into that category. If
we added them all together, the frequency of terms in Sexual will increase but not that much,
Table 9
Listing of terms occurring more than 100 times (

=expletive)
Term Frequency Term Frequency Term Frequency
and (incl. `AND', & `And') 4828 & 188 estate 123
Of 1266 stories 186 magazine 123
The 791 p

182 computer 122
Sex 763 college 180 news 121
Nude 647 naked 180 texas 119
Free 610 adult 179 games 118
In 593 state 176 war 117
Pictures 457 big 170 john 115
For 340 basketball 166 de 113
New 334 men 163 internet 111
+ 330 employment 157 car 110
University 291 school 156 wrestling 110
Women 262 jobs 155 high 109
Chat 256 american 153 company 108
On 252 real 153 ¯orida 108
Gay 234 world 152 business 107
Girls 223 black 150 service 106
Xxx 222 porn 147 video 105
To 218 photos 142 anal 104
Or 213 york 140 erotic 104
Music 209 A 132 stock 102
Software 204 Young 132 art 101
Pics 202 History 131 city 100
Ncaa 201 Page 131 porno 100
Home 196 Celebrities 129
B.J. Jansen et al. / Information Processing and Management 36 (2000) 207±227220

Table 10
Subject categories for terms appearing more than 100 times
Category Terms selected from 63 terms with
frequency of 100 and higher
Frequency for
category
Percent of
frequency -63 terms
Percent of
all terms
Sexual sex, nude, gay, xxx, pussy, naked, adult,
porn, anal, erotic, porno
2862 24.72 2.51
Modi®ers free, new, big, real, black, young, de, high,
page
1902 16.42 1.67
Place state, american, home, world, york, texas,
¯orida, city
1144 9.88 1.01
Economic employment, jobs, company, business,
service, stock, estate, car
968 8.36 0.85
Pictures pictures, pics, photos, video 906 7.82 0.80
Social chat, stories, celebrities, games, john 804 6.94 0.71
Education university, college, school, history 758 6.54 0.67
Gender women, girls, men 648 5.59 0.60
Sports ncaa, basketball, wrestling 477 4.12 0.42
Computing software, computer, internet 437 3.77 0.38
News magazine, news, war 361 3.12 0.32
Fine arts music, art 310 2.68 0.72
B.J. Jansen et al. / Information Processing and Management 36 (2000) 207±227 221

and particularly not in relation to thousands of terms in other categories that are widely spread
across all frequencies. In other words, as to frequency of appearance of terms among the 63
highest frequency terms, those in category Sexual have the highest frequency of all categories,
but still three out of every four terms of the 63 highest frequency terms are not sexual; if
extended to the frequency of use of all terms we estimate that 39 out of 40 of all terms were
not sexual.
While the category Sexual is certainly big, in comparison to all other categories in no way
does it dominate searching. Interest in other categories is high. Of the 63 highest terms, 16%
are modi®ers (free, new, big ...), 10% deal with places (state, american ...), 8% with
economics (employment, jobs ...), and the rest with social activities, education, sports,
computing, and arts. In other words, Web searching does cover a gamut of human interests. It
is very diverse. In light of this, the stereotypical view of the Web user searching primarily for
sexual information may not be valid.
There are two other groupings not listed in the table that should be noted. First, there were
1398 queries for various uniform resource locators (URL). Although no one URL made the
top of the list, if lumped together as a category, it was one of, if not the largest query category.
The second group was searching for multimedia documents (e.g. images, videos, and audio
®les). There were 708 queries for these multimedia ®les, with many of these terms looking for
speci®c formats.
6.3. Distribution of terms
We constructed a graph of rank±frequency distribution of all terms. This graph is shown in
Fig. 1.
The resulting distribution seems to be unbalanced at the ends of the graph, the high and low
ranking terms. In the center and lower regions, the graph follows the traditional slope of a
Zipf distribution representing the distribution of words in long English texts. At the beginning,
it falls o very gently, and toward the end it shows discontinuities and an unusually long tail,
representing terms with a frequency of one. A trend line is plotted on the ®gure with the
corresponding equation. The trend line is approximately that of the Zipf distribution. A proper
Zipf distribution would be a straight line with slope of ÿ1. The trend line does not plot well
for the higher frequency terms due to the large number of terms occurring only once or twice.
We wondered if the number of modi®ers (e.g. `+', `ÿ', ``, etc.) and the number of queries
with all terms strung together (e.g. +information+processing+journal) could be aecting the
rank±frequency distribution, that is, if the number of modi®ers, stray characters, and run-on
terms, were creating such a long tail of single occurrence terms. Therefore, we decided to clean
all terms and re-plot the rank±frequency graph. In cleaning terms, we removed all modi®ers
and separated all terms that were obviously strung together. Due to the varying nature of the
terms, this could not be done automatically. For example, one could not just remove `+' from
all terms because, for example, with c++ (the programming language), the `+' is part of a
valid term. In the cleaning process, all 113,793 terms were qualitatively examined. In most
cases, a decision would clearly be made on whether or not to clean the term. In cases were
there was doubt, the term was not modi®ed. Once clean, we again generated a rank±frequency
(log) plot. This rank±frequency plot is shown in Fig. 2.
B.J. Jansen et al. / Information Processing and Management 36 (2000) 207±227222

Fig. 1. Rank vs frequency (log) of all terms.
Fig. 2. Rank (log) vs frequency (log) of cleaned terms.
B.J. Jansen et al. / Information Processing and Management 36 (2000) 207±227 223