1. The Public Library Catalogue as a Social
Space
Louise Spiteri. School of Information Management.
Dalhousie University
Laurel Tarulli. Halifax Public Libraries

2. Introduction
Today’s library catalogues



Important and fundamental medium between users and their
information needs
Competing against powerful alternatives for information
discovery that allow user-contributed metadata (e.g., tagging,
ratings, and reviews) and user interaction with each other.
These alternatives raise user expectations of library
catalogues, where user-centred design and usability are seen
as more important than information organization.

3. Social discovery systems
Vendors are providing social discovery systems for use by public
and academic libraries, with enhanced features such as:

Predictive searching (or, “Did you mean …?)

User-contributed content such as tags, reviews, and ratings

Faceted navigation of results

RSS feeds of stored searches, results, new postings, and so forth

Sophisticated ranking algorithms based on variables such as item count,
popularity, field weighting, and so forth

4. Research problem
There have been no comprehensive studies to evaluate the use of social
discovery systems in public libraries in Canada.
The actual value of social features of these social discovery systems, such as
tags, reviews, and ratings to the end user has not been examined:


Why would users post tags, ratings, and reviews in a public library
catalogue?
These systems are costly to implement and to maintain: If we provide
users with the ability to contribute content to catalogue records, will they
actually do so?

5. Participants
Two principal social discovery systems used in Canada:
AquaBrowser & BiblioCommons
Halifax & Edmonton public libraries

Due to the nature of the funding project and time restrictions,
this part of the study was deliberately limited in scope,
especially since permission is needed to access server logs.

6. Research questions
How do public library users interact with social discovery
systems?
How does usage between the two social discovery systems
compare?
Does the use of social discovery systems change over time?

7. Methodology
Transaction logs of the social discovery systems used by Halifax
and Edmonton were compiled from June-August, 2010. Data
gathered included:
•
Type of search used
•
Sort features
•
User-generated content
•
Tags
•
Reviews
•
Ratings
•
Lists
•
Comments

8. Tracking user-contributed metadata
A set of 50 monograph records was examined (weekly) in both
systems to track changes to tags, reviews, and ratings assigned by
the clients.

10 Adult fiction

10 Adult non-fiction

10 Children's fiction

10 Children's non-fiction

10 Graphic novels

9. Limitations of transaction log analysis



The nature of the data gathered differs by vendor, so one cannot
compare results easily between the two systems.
Log analysis shows only which features and used and how
frequently. In the case of user-generated metadata, we cannot
determine specifically how or why these metadata are used.
Log analysis does not tell us why clients use these features and,
perhaps more importantly, why they do not. The dearth of
“active” use of social features suggests that further studies are
necessary to determine motivations for use.

10. Findings: Search types
Table 1: BiblioCommons: Average Search Type
50.00%
45.00%
40.00%
35.00%
30.00%
25.00%
20.00%
15.00%
10.00%
5.00%
0.00%
Item show
Smart search
Keyword
Author
Advanced
Title
Subject
Tag
List

11. Findings: Search types
70.00%
60.00%
50.00%
40.00%
30.00%
20.00%
10.00%
0.00%
Table 2: AquaBrowser: Average Search Type

12. HPL advanced search page
• What is noticeably missing is a
field to search by tag. Even
though tags are now part of
the metadata record, you can't
search by this field.
• When I search for “robin
hood”, for example, I don't get
a tag cloud; the only way I can
search by tags at this point is
via the “refine search” feature.

13. HPL “Cloud”
•Does “thesaurus term” refer to LCSH?
•How is “wood” a spelling variation of
“hood?” - it's a lexical variant, not a
semantic variant.
•What is mean by “translation?” These
are actually what appear to be
equivalent terms. “Translation” is not
the correct description of this
relationship.
•Bottom line: This is not a tag cloud.

14. EPL basic search

15. Findings: Faceted navigation
Tb 3Au B we:Aea e r q e c F c t d aig t n
a le : q a r s r v r g Fe u n y ae Nv a
o
e
io
7. 0
0 %
0
6. 0
0 %
0
5. 0
0 %
0
4. 0
0 %
0
3. 0
0 %
0
2. 0
0 %
0
1. 0
0 %
0
00
. %
0

16. HPL: Refine search options

17. EPL: Refine search options

18. User-generated content
Options for user-generated content differs significantly between the
two systems.


In AquaBrowser, clients can add: Lists, Ratings, Reviews, and Tags.
In BiblioCommons, clients can add: Age suitability; Comments; Content
notes; I own this; Lists; Private notes; Quotations; Ratings; Similar titles;
Summaries; Tags. Clients can also communicate with each other via an
internal messaging system.

19. Findings: User-generated content
Table 4: BiblioCommons: Average Frequency of User-Generated Content
35.00%
30.00%
25.00%
20.00%
15.00%
10.00%
5.00%
0.00%

20. HPL: Display of options to add content

21. HPL: Tagged record

22. EPL: Display of options to add content

23. EPL: Tagged record

24. EPL: Tagged record, cont.

25. EPL: “Add more” features
• Private notes: You can add a private note to any title in your
collection
• Quotations: The quotations you contribute will be visible to other
library members when they look at this title’s details page.
• Similar titles: Recommend titles that have something in common
— content, tone, style or plot — to the one you are viewing.
• Video: Add a video (your own or others via the Web, e.g., film
trailer) related to the item.

26. Findings: Percentage of observed records with
user-generated content
80.00%
70.00%
60.00%
50.00%
40.00%
AquaBroswer
30.00%
BiblioCommons
20.00%
10.00%
0.00%
Ratings
Reviews
Tags

27. User-generated content in the 50 selected records:
HPL: Only 6 records (12%) were assigned user tags. One record was
assigned 2, while the other 5 were each assigned one tag. There is
no tag growth over the 4 months. No ratings or reviews were
assigned to any of the records.
EPL: Tags: Assigned to only 3 records (6%) - no changes
Comments: Assigned to 10 records (20%) - no changes
Ratings: Assigned to 32 records (72%)

28. Conclusions: User content and search
User-generated content does not feature prominently in the search
types.


Directory-style browsing of records or predetermined pathways dominates
search type in BiblioCommons. The basic search page features drop-down
menus for fields such as author, title, genre, subject, and tag.
The single basic search box (no drop-down menu) dominates search type in
AquaBrowser. No specific search option for tags or any user-generated
content is provided.

29. Conclusions: User-generated content
User-generated content is not used extensively or significantly in the
two social discovery systems observed.
List creation predominates user-generated content. Ratings, reviews,
and tags rank significantly lower. Other than list creation, there is
very little evidence of user-generated content of the 50 records
tracked over 4 months.

30. Conclusions: Faceted navigation
Even though both systems provide 13-14 facets by which to refine
search results, format is the predominant facet used to refine
searches; the remaining facets are significantly underrepresented.
User-generated content does not feature prominently in the facets
provided by either system. It would be useful to allow clients to
refine their searches by ratings, e.g., to select DVDs that have a 4star rating.

31. Future research
Distribute surveys and conduct focus groups across Canadian public
libraries to examine:




Which social features (e.g. tags, ratings or reviews) are used by others;
How social features are used by users (e.g. to look for items or to contribute
content to catalogue records);
Users’ motivations for using (or not) social features;
Users’ perceptions of, and satisfaction with, the benefits of the social
features in social discovery systems.

32. Acknowledgments

Funding for this research study is provided by the OCLC/ALISE
Library and Information Science Research Grant Program.