Information Seeking Behavior in Peer-to-Peer Networks: An Exploratory
Study
K. Y. Chan and S. H. Kwok
Department of Information and Systems Management
The Hong Kong University of Science and Technology
Clear Water Bay, Hong Kong{frankcky, jkwok}@ust.hk
Abstract
Digital Library (DL) has become a mean to
distribute multimedia contents in the networked
information environment. Previous researchers havesuggested ways to improve the design of multimedia
DLs, however, users’ information seeking behavior
is often neglected. Existing studies on users’
searching behavior do not provide any insights
specifically into the behavior of users searching for
multimedia contents, therefore, we intend to address
this issue by conducting experiments in peer-to-peer(P2P) networks, in which multimedia contents areextensively shared. Anonymity, one of thecharacteristics of P2P networks, has hinderedprevious researchers from studying individuals’
information seeking behavior. In this paper, we
propose a methodology to identify users’ searchsessions and capture their searching behavior in P2P
networks at the level of individuals. Our preliminary
results show that when users search for multimedia
files, they are likely to submit successive queries inthe same topic. The results of this study will be
instrumental in providing ways of understanding
users’
information
seekingbehavior
andincorporating human factors into the design of DLs.
Keywords: Digital library, information seeking
behavior, peer-to-peer

1 Introduction
With technology advancements in data storageand network communications, Digital Library (DL)has become a mean to distribute multimedia contents
in networked information environment. For instance,
Jun [1] is an open-source graphics and multimedia
library that supports multimedia contents such as
movies and sound. The IRCAM Multimedia Library[8] provides an online integrated networked accessto collections of multimedia contents such as audio
recordings, still photos and films.
To cope with the huge volume of multimedia
data, previous researchers have suggested ways toimprove the design of DLs. de Vries et al. [6]developed an infrastructure for managing, indexing
and serving multimedia contents in DLs. Martinez[19] proposed a library of abstract classes to model
multimedia objects in a coherent, reusable andextensible framework, in order to facilitate thedesign and development of multimedia applications
by using an object-oriented database management
system. Chee et al. [2] proposed a High-speed
Distributed File System (HDFS) which aimed toprovide storage to large files while read and write
performance would not degrade significantly
compared to access for small files. Liu and Chen[17] developed a three-dimensional pattern matchingmechanism for efficient video query processing.
However, these works focused only on technicalmechanisms of DLs, such as data organization andquery processing, while they did not incorporate
users’ information behavior into the design of DLs.
Information seeking behavior is regarded as animportant concern in the design of information
systems. Information seeking behavior is defined as“the purposive seeking for information as aconsequence of a need to satisfy some goal” [28]. To
cope with the growing number of Internet users andthe huge amount of information, designers of DLs
should take account of not only the information
requirements, but also users’ information seeking
behavior.
Prior researchers have devoted their efforts to theevaluation of user interactions with Web searchengines and other information retrieval systems [4,5, 12, 15, 21-23, 25, 26], for example, Spink et al.
[25] suggested that only one in five Excite users
reformulated their queries during their informationseeking processes. However, these works could only
examine the searching behavior of general public asa whole, but they did not successfully look into thesearching behavior of a particular group of users,
who had special interests in multimedia contents,
since multimedia queries accounted for only a small
proportion (less than 5%) of users’ web queries [24,27]. Due to the difference in the nature between
multimedia contents and other general documentary
contents, users may have different information
seeking behavior during their searching processes.
Therefore, we choose the peer-to-peer (P2P)
network, a popular platform for sharing ofmultimedia files, to study the information seeking
behavior of users searching for multimedia contents.

The P2P also represents a simplified informationseeking environment, where extensive searchingprocesses take place.
One of the important characteristics of P2P filesharing network is anonymity. Existing P2P
protocols [7, 9] intend to hide users’ information bymeasures such as encrypting the query messages andnot embedding locative information in the query
messages. These measures that enforce anonymity
have hindered the study of information seeking
behavior of individuals because those query
messages provide no hints on identifying theoriginators of a particular query message. Due to the
problem of anonymity, previous researchers [13, 14]
can only provide a broad picture of how a large
group of P2P users behave, such as categorization ofqueries and ranking of popular query terms, but they
provide no insight into information seeking behavior
at the level of individuals.
In this paper, we propose a methodology tocapture the searching activities of individuals in the
Gnutella P2P network [9] and to define users’ searchsessions for analysis. We will also present the
preliminary results of our experiment.
The organization of this paper is as follows.
Section 2 defines terms used in the paper anddiscusses the background of Gnutella P2P networks.
Section 3 introduces the proposed methodology tocapture users’ information seeking behavior. Section
4 presents the preliminary results of our experiment.
The paper concludes in Section 5.

2 Background
2.1 Definition of terms
•
•
•

•

A servent is defined as a host acting as both aserver and a client in P2P networks.
A query is defined as a string of key words
entered by users when they perform searching.
A search session is defined as the time period
starting from the time the servents connect to a
Gnutella network to the time the servents
disconnect from the network, in which theservents have performed searching.
A topic is defined as the subject to which aquery belongs. We picked some topics provided
in Spink, , H. C. Ozmutlu and S. Ozmutlu [26]for categorization of queries, since the P2P
network contains less variety of contents than
the web does. The contents offered in the web
are of general and broad topics, while thecontents offered in P2P networks are usuallymore specific to serve particular interests, suchas entertainment and computer software. For
example, some topics, such as News, Medical,
Business and Shopping, exist in the web but are
not applicable to the contents in P2P networks.

•

P2P queries are categorized into five topics:
Computer, Education, Entertainment, Sexualand Inexplicit.
o Computer: Includes software, applications
and games.
o Education: Includes books and otherdocuments for educational purposes.
o Entertainment: Includes multimedia files
for entertainment purposes, such as pop
songs and movies. Our study will focus on
this topic.
o Sexual: Includes sexual contents, such as
pornography.
o Inexplicit: Includes any other unclassified
queries.
We do not further narrow down the topics
because it is difficult to infer the specificpurpose of a query from just a few terms. For
example, a query with an actor’s name may aim
for a movie or an image, so we can classify thequeries with broad topics only.
Metadata is a definition or description of data.
Examples include the title of a song and the
director of a movie.

2.2 Gnutella Protocol Specification 0.4
Gnutella protocol 0.4 [3] employs a puredecentralized model [20]. In this model, all serventsare equal in terms of functionality. They offer clientside functions such as accepting queries from users
and returning search results, while they also perform
server-side roles such as matching incoming queries
against their local resources and returning results toother servents.
The Gnutella protocol defines five different typesof packets, namely QUERY, HIT, PING, PONG,
and
PUSH.
1. QUERY – requests for sharable files matching
the given criteria.
2. HIT- responds to a query by returning a list of
sharable files matching the given criteria and theIP addresses of their providers. There may be
multiple responders to a query due to the natureof distributed network.
3. PING - requests the transitive closure ofconnected peers to identify themselves.
4. PONG - responds by a peer upon receiving a
PING, the responding peer provides its IP
address and the number of sharable files it
contains.
5. PUSH - requests a file provider to contact therequester. This provides a simple mechanism toattempt to get through the firewalls.
To search for files in the Gnutella network, theGnutella servent sends a QUERY packet that
contains the query string. The neighboring servents

will receive this QUERY packet and they will takethe query string as search criteria to check their
sharable local files that match the criteria. If theservent finds relevant files in its local resource, itwill generate one or more HIT packets containing
information about the relevant sharable files. The
HIT packets will be back-propagated, i.e., sent on
the reverse of the path taken by the initial QUERY
message, to the requesting servents.
For the searching operation, messages are
distributed based on a flooding mechanism, in which
a servent receiving a query message will makecopies of the query message and broadcast them toall its neighboring servents. A mechanism named
time-to-live (TTL) counter is introduced in Gnutella
protocol in order to relieve the excessive network
traffic caused by the message flooding mechanism.
TTL counter, which default value is 7, is decreased
by one when the query message passes through apeer. The query messages are relayed until theirembedded TTL counters reach zero.
To monitor the searching activities in the
Gnutella network, previous researchers [13, 14]placed a monitoring servent with log-recording
capability to capture all the query messages which
passed through the monitoring servent. Thisapproach could capture the query terms searched by
the users as a whole, however, it failed to identify
the originators of query messages and to keep trackof the searching activities of individuals. Thus,
search sessions of individuals cannot be identified.
Figure 1 illustrates the topology of the experiment.

Publicservent

Monitoring
servent

Query
flow

Figure 1: Topology of the Gnutella (Protocol Specification
0.4) Network.

2.3 Gnutella Protocol Specification 0.6
Gnutella protocol 0.6 [10] employs a hybridarchitecture [20] combining centralized anddecentralized model. Servents are categorized into“leaf” and “ultrapeer”. To be qualified as utlrapeers,
servents should possess sufficient computational
power, network bandwidth and long expected
uptimes. Detailed principles of electing ultrapeers
are described in the Gnutella Protocol Specification
v0.6 [10].

An ultrapeer maintains connections with otherultrapeers and acts as a proxy to the Gnutella
network for the leaves connected to it. A leaf keeps
only a small number of connections to ultrapeers. An
ultrapeer only forwards a query to a leaf if it
believes the leaf can answer it, and leaves neverrelay queries between ultrapeers. Figure 2 illustrates
the topology of the Gnutella (Protocol Specification
0.6) network.

Ultrapeer

Leaf

Figure 2: Topology of the Gnutella (Protocol Specification
0.6) Network.

In this model, since an ultrapeer acts as a proxy to
the Gnutella network for it connected leaves, notonly can it collect the query messages at theultrapeer level through the ultrapeer-to-ultrapeer
connections, but also it can keep track of searchingactivity of individual leaf through the ultrapeer-toleaf connections. By maintaining a number of leaf
connections, an ultrapeer can uniquely identify theoriginators of query messages sent by its connected
leaves. For instance, it is possible to record thesearching activity of a leaf throughout the period
starting from the time the leaf connects to the time
the leaf disconnects. Tracking of searching habit ofindividual P2P users becomes possible byimplementing a monitoring ultrapeer in the Gnutella
network.

3 Research Design
In related research, Markatos [18] has suggested
that the overall P2P network characteristics can be
represented by a randomly-chosen peer in the P2P
network. Not only does every peer have similar
network characteristics, but also the characteristics
are location independent. To capture users’
information seeking behavior in the P2P network, aGnutella P2P servent was developed based on an
open-source servent, LimeWire [16]. The P2P
servent was connected to the Gnutella P2P network
and was programmed to always act as an ultrapeer.
The servent accepted incoming connections fromleaves and routed the queries from its shielded
leaves to other connected ultrapeers. Therefore, theservent was able to capture the searching activities
of its shielded leaves. Figure 3 illustrates the
network connections between our servent and other
P2P servents.

Monitored
Monitoring
Leaf
Ultrapeer
Topology of the Gnutella Network in our

Ultrapeer
Figure 3:experiment.

Leaf

Our Gnutella servent operated on a broadband
connection through the Internet Service Provider(ISP), i-Cable, in Hong Kong. A set of data wascollected from 0:00, 3 October 2003 to 0:00, 4October 2003 Hong Kong time. To improve theefficiency of data collection, our servent was
programmed to maintain at most 20 ultrapeer
connections and 100 leaf-node connections
simultaneously. There were 3025 servents
connecting to our servent as leaves in the one-day
data collection period. The searching activities ofthese servents were recorded in a log file, which waslater imported into a database system for processing.
Table 1 summarizes the settings of our datacollection experiment. Table 2 shows an example ofthe log file recording queries from an individual
servent.
Table 1: Summary of our experiments.
Date

Duration

Number ofultrapeer
connections(maximum)

October
3, 200324 hours

20

Number of
leaf
connections(maximum)Numberofmonitored
hosts

100

3025

Table 2: An example of log file.
Time

Host
IP

Search Criteria/
Connection
Status

9:16:50

a.b.c.d***connected***

9:22:11a.b.c.dCountry

74FF168526EFE5AF
FF144527D2ED9500

9:26:09a.b.c.dim gonna take themountain

09964821B6CFDF1C
FF808D7794838600

9:26:30

a.b.c.dcowboys like us

9:28:07a.b.c.dperfect

9:30:04

a.b.c.dsenorita

9:35:54

a.b.c.d***disconnected****Host IP is hided for privacy reasons.

GUID

0B4E54F0197DC63B
FF73379CAB3C0700
AA29CAEE72FF1FCB
FFE91CBE8BAA4500
76CE25D54178C320F
F6365EA9A0DBD00

4 Results
4.1 Analysis on queries in terms of search topics
The queries are categorized into five categories:
Computer, Education, Entertainment, Sexual andInexplicit. Each query is manually entered in the
Google web search engine [11] to verify its
belonging category. The categorization process isbased on the returned results from the search engine.
For example, if the majority of returned results is
related to entertainment, the query will be
categorized into the topic named Entertainment.
Moreover, the file types of desired contents, such as
Audio and Video, help us categorize the queries. For
example, if the query is specified with the file type
named Audio and the search criterion is a singer’s
name, the query is classified to be of the topic
named Entertainment. The distribution of queries isshown in Table 3. It is not surprising that the mostpopular category is Entertainment, which accounts
for about 53% of queries in the sample, since P2P
network is mainly used for sharing multimedia files
for entertainment purpose. This figure also ratifies
our claim that the P2P network is a suitable platform
to capture the information seeking behavior of users
searching for multimedia contents. About 12% ofqueries are classified as Inexplicit because someusers submit queries of one to two terms, such as“nice” and “down”, which provide no indication ofwhat they search for.
Table 3: Distribution of number of queries to topics.
Topic

Number of Queries%

Computer

488

10.59%

Education

51

1.11%

Entertainment

2442

52.98%
23.04%

Sexual1062

Inexplicit

566

12.28%

Total

4609

100.00%

4.2 Analysis on queries in terms of session
Among 3219 connection sessions from 3025
servents, 661 were search sessions which included
one or more than one query. The mean number ofqueries per session for the Gnutella P2P network is
6.97. Figure 4 shows the distribution for number ofqueries per session. It is shown that most sessions
tended to have a small number of queries persession.
4.3 Analysis on queries in terms of topic change
There were 256 topic changes in 661 searchsessions, whereas the mean number of topics persession is 1.3 and the mean number of topic changes
per session is 0.4. It means that in a search session,an average user searched for files on one topic onlyand seldom switched to other topics. Figure 5 shows
the distribution for number of queries per topic. As it
is observed, usually 1 to 5 consecutive queries werespent on a particular topic. On the other hand, 14.5%
of users submitted more than 30 consecutive queries

on a single topic. The mean number of queries per
topic is 17.8 queries, which means that users
searched for another topic every 17.8 queries. It
should be noted that this figure is deviated by thesignificant amount of users who submitted morethan 30 successive queries on a single topic.

Number of queries per session
30.00%

Numer of sessions (%)

25.00%
20.00%
15.00%
10.00%
5.00%
0.00%
1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30+

Number of queries/session

Figure 4: Histogram for number of queries per session.
Number of queries per topic
35.00%

Frequency (%)

30.00%
25.00%
20.00%
15.00%
10.00%
5.00%
0.00%
1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 2930+

Number of queries/topic

Figure 5: Histogram for number of queries per topic.

4.4 Analysis on queries in terms of file typespecification
In our experimental environment, users canvoluntarily specify the file types of their desired
contents when they submit queries, a feature aiming
to improve the accuracy of searching. The file types
include Audio, Documents, Images, Software
Programs, Video and Any types. Among 4609
queries, only 1367 queries are specified with filetypes, while about 70% of queries are not specified.
Among those file types, only Audio and Video areused by users to specify their queries. Table 4 shows
the distribution of queries specified with file types
among different topics. About 46% of users tend to
specify the file types, Audio or Video, when theylook for multimedia files for entertainment purposes.

Table 4: Distribution of queries specified with file types
among different topics
Specified withfile types

Unspecified

Computer

0 (0%)

488 (100%)

Education

0 (0%)

51 (100%)

51

Entertainment

1127 (46%)

1315 (54%)

2442

Sexual213 (20%)849 (80%)1062

Inexplicit

27 (5%)

539 (95%)

566

Topic

Total
queries488

4.5 Analysis on queries in terms of metadata
searchTo search for contents in P2P networks, users can
include metadata in their queries to narrow the scopeof searching. In our experiment, for example, users
can specific their queries with Title, Artist, Album

and Genre to locate audio contents, while they canspecific their queries with Title, Type, Year andDirector to locate video contents. Among 994
queries searching for audio contents, 82.09% ofqueries include 1 metadata field, 17.3% of queries
include 2 metadata fields, while 0.6% of queries
include 3 metadata fields. Among those 373 queries
searching for video contents, 91.96% of queries
include 1 metadata field, 7.51% queries include 2
metadata fields and 0.54% of queries include 3
metadata fields. For both audio and video queries,
none of them includes all 4 metadata fields. Table 5summarizes the distribution of queries with different
number of metadata fields. As it is observed, users
are likely to include 1 metadata field when they
perform metadata search for multimedia contents.
They seldom specify their queries with more than
one metadata field. Table 6 presents the distributionof queries including different metadata fields. Title
and Artist are the popular fields when users perform
metadata search for audio contents, while Title is themost popular field for metadata search for video
contents. These figures suggest that those metadatafields are not of equal interest among users. Some
metadata fields are used more often than others are.

Metadata
field
Type

Title

Type

Year

Director

Video

94.64%

11.26%

1.07%

1.07%

4.6 Analysis on queries in terms of duration ofsessions
The mean duration of the 661 search sessions is3087.9 seconds, whereas the standard deviation is3707.1 seconds. This shows a significant variation in
the duration of search sessions in P2P network. Thismay be due to the dynamic nature of P2P network,in which servents frequently disconnect from theP2P network. The distribution of session durations isshown in Figure 6. A major proportion of sessions
were under 2000 seconds. Only 2.28% of sessions
lasted for more than 12000 seconds. Table 7 shows
the summary of our data collected in the Gnutella
P2P network.
Table 7: Summary of data collected in the Gnutella P2P
network.
Total number of search sessions
Mean of duration of search session (sec)S.D of duration of search session (sec)
Total number of queries
Mean of queries per session
Total number of topic change
Mean of topics per session
Mean of topic changes per session
Mean of queries per topic

Table 5: Distribution of queries with different number offields of metadata
Numberoffields

1

2

3

661
3087.93707.1
4609
6.97
256
1.3
0.4
17.8

4

5 Conclusions and Future Work

Type
Audio

82.09%

17.30%

0.60%

0.00%

Video

91.96%

7.51%

0.54%

0.00%

In this paper, we introduced a methodology tocapture the information seeking behavior of users
searching for multimedia contents, and preliminary
results were presented. The contributions of thispaper are (1) to help understand users’ searching
behavior in P2P networks at the level of individuals,
while previous works could only capture theTable 6: Distribution of queries including different metadatafieldsMetadata
field
Type

Title

Artist

Album

Genre

Audio

47.89%

57.24%

10.36%

2.62%

Distribution of session durations in seconds

20.00%

15.00%

10.00%

5.00%

Duration/session (seconds)

Figure 6: Histogram for distribution of session duration.

12000+

11500

11000

10500

10000

9500

9000

8500

8000

7500

7000

6500

6000

5500

5000

4500

4000

3500

3000

2500

2000

1500

1000

0.00%

500

Number of session (%)

25.00%

behavior of general public as a whole; and (2) tostudy, through experiments, the information seeking
behavior of real users in Gnutella P2P networks, inwhich multimedia files were extensively shared. The
findings make contributions to the design ofmultimedia DLs.
DLs can incorporate some features toaccommodate users’ information seeking behavior.
For example, when the DL receives queries aboutentertainment, the system should be able to predict
what the users are looking for, since users seldomchange their searched topic within a single searchsession, as observed from our preliminary results.
The DL can refer users’ queries and its predictionsof users’ requests to other DLs in advance, if it does
not contain the requested contents, to improve its
response time. Moreover, since not all metadatafields are not considered useful, metadata fields
should be carefully chosen to tailor users’ searchingneeds. The understanding of users’ information
seeking behavior also helps address individual needs
and provide users with related contents of theirinterests, in order to facilitate push of information in
DLs. Finally, organizing data according to users’
searching behavior may be a way to shorten
information retrieval time, which is a crucial
performance indicator in DLs.
Future works of our study include the analysis ofchanges in users’ information seeking behavior overa certain period of time and how users behave when
they engage in multitasking searching.

[4]

[5]

[6]

[7]
[8]

[9]
[10]

[11]

Acknowledgments[12]

The work described in this paper was partially
supported by grants from the Research Grants
Council of the Hong Kong Special Administrative
Region, China (Project No. HKUST6256/03E).

[13]

References
[1]

[2]

[3]

A. Aoki, K. Hayashi, K. Kishida, K.
Nakakoji, Y. Nishinaka, B. Reeves, A.
Takasbima, and Y. Yamamoto, "A casestudy of the evolution of Jun: an objectoriented open-source 3D multimedia
library," presented at 23rd International
Conference on Software Engineering, 2001.
C. L. Chee, S. S. Erdogan, C. W. Ngo, andC. K. Wong, "A high speed distributed filesystem for multimedia communications,"
presented at 1994 IEEE Region 10's Ninth
Annual International Conference, 1994.
Clip2, The Gnutella Protocol Specification
v0.4,
http://www9.limewire.com/developer/gnute
lla_protocol_0.4.pdf, accessed on 23
October 2003.

[14]

[15]

[16]

M. D. Cooper, "Usage patterns of a Webbased library catalog," Journal of theAmerican Society for Information Science& Technology, vol. 52, pp. 137-148, 2001.
V. Cothey, "A longitudinal study of World
Wide Web users' information-searching
behavior," Journal of the American Society
for Information Science & Technology, vol.
53, pp. 67-78, 2002.
A. P. de Vries, B. Eberman, and D. E.
Kovalcin, "The design and implementationof an infrastructure for multimedia digital
libraries," presented at International
Database Engineering and Applications
Symposium (IDEAS'98), 1998.
eDonkey, eDonkey2000,
http://www.edonkey2000.com/, accessedon 23 October 2003.
M. Fingerhut, "The IRCAM multimedia
library: a digital music library," presented
at IEEE Forum on Research andTechnology Advances in Digital Libraries,
1999.
Gnutella, Gnutella, http://rfcgnutella.sourceforge.net/research/index.htm
l, accessed on 23 October 2003.
Gnutella, The Gnutella Protocol
Specification v0.6, http://rfcgnutella.sourceforge.net/developer/testing/i
ndex.html, accessed on 23 October 2003.
Google, Google, http://www.google.com/,
accessed on 23 October 2003.
C. Holscher and G. Strube, "Web searchbehavior of Internet experts and newbies,"
Elsevier. Computer Networks: theInternational Journal of Distributed
Informatique, vol. 33, pp. 337-346, 2000.
S. H. Kwok, S. M. Lui, R. Cheung, S.
Chan, and C. C. Yang, "Searching Behavior
in Peer-to-Peer Communities," presented atProceedings of the International Conferenceon Information Technology: Computers andCommunications (ITCC.03), 2003.
S. H. Kwok and C. C. Yang, "Searching thePeer-to-Peer Networks: The Community
and Their Queries," Journal of theAmerican Society for Information Scienceand Technology (JASIST), 2004
forthcoming.
A. W. Lazonder, H. J. A. Biemans, and I.
Wopereis, "Differences between novice andexperienced users in searching informationon the World Wide Web," Journal of theAmerican Society for Information Science,
vol. 51, pp. 576-581, 2000.
Limewire, Limewire.org,
http://www.limewire.org/, accessed on 23
October 2003.

[17]

[18]

[19]

[20]

[21]

[22]

[23]

[24]

[25]

[26]

[27]

C.-C. Liu and A. L. P. Chen, "3D-List: adata structure for efficient video query
processing," IEEE Transactions on
Knowledge & Data Engineering, vol. 14,
pp. 106-122, 2002.
E. P. Markatos, "Tracing a large-scale peer
to peer system: an hour in the life ofGnutella," Proceedings CCGRID 2002. 2ndIEEE/ACM International Symposium on
Cluster Computing and the Grid. IEEE
Comput. Soc. 2002, pp. 65-74, 2002.
J. Martinez, "The design of an extensible
multimedia library for an OODBMS,"
presented at Seventh International
Workshop on Database and Expert Systems
Applications, 1996.
N. Minar, Distributed Systems Topologies:
Part 2,
http://www.openp2p.com/pub/a/p2p/2002/0
1/08/p2p_topologies_pt2.html, accessed on
23 October 2003.
A. Spink, "A user-centered approach toevaluating human interaction with Web
search engines: an exploratory study,"
Information Processing & Management,
vol. 38, pp. 401-426, 2002.
A. Spink and H. Cenk Ozmultu,"Characteristics of question format Web
queries: an exploratory study," Information
Processing & Management, vol. 38, pp.
453-471, 2002.
A. Spink, H. Cenk Ozmutlu, and S.
Ozmutlu, "Multitasking information
seeking and searching processes," Journalof the American Society for Information
Science & Technology, vol. 53, pp. 639652, 2002.
A. Spink, A. Goodrum, and A. R. Hurson,"Multimedia Web queries: implications fordesign," presented at International
Conference on Information Technology:
Coding and Computing, 2001.
A. Spink, B. J. Jensen, and H. C. Ozmultu,"Use of query reformulation and relevance
feedback by Excite users," Internet
Research-Electronic Networking
Applications & Policy, vol. 10, pp. 317328, 2000.
A. Spink, H. C. Ozmutlu, and S. Ozmutlu,"A Study of Multitasking Web Search,"presented at International Conference on
Information Technology: Computers andCommunications (ITCC'03), 2003.
A. Spink, S. Ozmutlu, H. C. Ozmutlu, andB. J. Jansen, "U.S. versus European Web
searching trends," ACM SIGIR Forum(ACM Special Interest Group on[28]

Information Retrieval), vol. 36, pp. 32-38,
2002.
T. D. Wilson, "Human information
behavior," Informing Science, vol. 3, pp.
49-55, 2000.