Understanding how users think of personal information, and how they mentally categorise or classify the objects they work with, should inform the design of personal information management (PIM) or personal retrieval systems. However, most investigations of this topic predate widespread multimedia, websites, and social media|objects that a contemporary PIM or retrieval system should work with. We describe a pilot study that has used a variant of card sorts to elicit categories for personal information such as les, email, tweets, and websites. Our early results suggest that there are common categorisations which are not yet supported by PIM software, but which might reward further work. Our results also suggest that|with some caveats| card sorts are useful for understanding users' categories.
Tools for personal information management (PIM) and
search support the archival, retrieval, and management of
\personal" data: the les, email, photos, videos, and other
digital objects a person creates or uses [
We are interested in how users think of the wide range of digital objects they interact with|the objects conventionally considered by PIM tools, objects less commonly considered such as websites and applications, and newer objects such as messages from social media. There are three linked questions: 1. What properties do users think \personal" digital objects have? That is, in which ways do users think of the objects they use? Presented at EuroHCIR2012. Copyright © 2012 for the individual papers by the papers’ authors. Copying permitted only for private and academic purposes. This volume is published and copyrighted by its editors. 2. Can we expose these properties in a PIM or search tool? Can the properties of an object be determined algorithmically? How should the properties be presented? 3. Assuming we can expose some or all of these properties, would we expect that to make management or retrieval easier?
At present, we are considering the rst question. In particular, in this work, we have experimented with card sorts to elicit users' own descriptions of personal information. 2.
Past work has investigated the properties users assign to les, and elicited categorisation schemes. This work has not however considered as wide a range of object types as we do here; we may expect that with di erent types, sources, and quantities we would see di erent categorisations. Existing PIM and le management systems also support, or impose, particular faceting schemes. 2.1
There is a rich tradition, in information science and information behaviour, of studies that try to understand how people organise, classify, and think about their information| that is, how people understand their information independent of any particular software capabilities or restrictions.
Three studies of note are by Cole, Kwasnik and Case.
Cole [
Other research relating to our work has tried to learn about
how people think about digital information by investigating
how they behave with information in practice. For example,
people have been shown to organise email messages and les
based on projects [
More recently, Goncalves and Jorge [
Our aim here is to add to and complement this previous work by using a technique that can deal with rich variety of information objects we interact with today; be tool agnostic; and allow insight into how di erent documents can be associated in di erent ways. We would also like to understand whether this has any impact on the design of PIM tools. 2.2
Tools for desktop search typically expose not just
lesystem attributes such as name, size, and timestamp, but also
extracted metadata. For example, Phlat [
More elaborate PIM tools have exposed other attributes to
support di erent interactions. Some, such as Lifestreams [
These systems o er variety of projections, across a number of media and storage types, but it is hard to know whether these match the way people naturally think of their objects. Alternative presentations that were natural for users, and easy to implement, would be worth further thought.
In this work, we have experimented with repeated singlecriterion card sorts to elicit users' mental categorisations. Cards represented digital objects on each participant's computer. 3.1
Repeated single-criterion card sorts|or just \card sorts"|
are a common technique for eliciting users' categorisations
(see e.g. Rugg and McGeorge [
In a typical exercise, each participant is given a number of cards, each representing an object or a concept. They are asked to partition these cards according to any criteria they like; the criteria used for the sort, the categories (piles or sets), and the cards in each category are recorded. This is repeated several times, with participants suggesting a di erent criteria each time. For example, given cards labelled as follows: 1. pig; 2. chicken; 3. snake; 4. horse; 5. spider a participant may sort cards according to the criterion \raised on a farm", with cards 1, 2, and 4 in category \yes" and cards 3 and 5 in category \no". A second sort, according to the criterion \where eaten", might have cards 1 and 2 in category \almost everywhere", card 3 in category \Asia", card 4 in category \Asia and Europe" and card 5 in category \don't know".
Records of the sorts may then be analysed with qualitative or quantitative methods. 3.2
For this early experiment, we recruited a convenience sample of ten participants from two institutions. All were heavy computer users.
As preparation for the experiment participants were asked to select several information objects they had seen, used or created in the recent past. An \information object" was de ned by giving as examples computer les, emails, websites, tweets or Facebook updates, documents or articles read, photographs or images, videos, music, and computer applications. However, participants were not restricted to these objects and could choose anything they wanted using these as a guideline. We encouraged participants to label 10 to 15 cards, which we believe balances the need for broad coverage with practical limits on participants' time.
While choosing objects, participants were asked to create index cards with the name of each (or some other reminder of its identity or contents).
With cards made, each participant was introduced to sorts using a set of cards with pictures of buildings; they were taken through some example sorts which included criteria clear from the pictures themselves (colour, material), criteria which were not immediately clear (insulation), criteria which were subjective (good place for a party). \Can't tell" or \don't know" categories were included in these examples.
The participants' own cards were then used for repeated sorts. Participants were asked to make piles according to a criterion of their choosing, and we noted the criterion (sometimes this was implicit), the categories used, and the cards in each category.
After collecting individual classi cations from all
participants, the full dataset, i.e. the criteria used to associate
information objects, was analysed qualitatively using an
a nity diagramming technique. This is a group-based
process, which allows the discovery and validation of patterns in
the data [
The results from this pilot are promising. Card sorts elicited a variety of criteria; some of these di er from those seen before, and many are not well supported by PIM or retrieval tools. document lifecycle events object's form object's a ective qualities object's cost and value object static/dynamic distinction people and community properties of associated tasks topics covered work/leisure distinction (three other object-related groups) Participants
Our ten participants provided 64 sorts, a median 5.5 sorts each ( rst/third quartile 5.0/8.0 sorts each). At the leaves of our hierarchy, there was in general little overlap: 13 of 25 criteria were used by only one participant. However, 12 were used by two or more participants, 8 by three or more, and one criteria (discussed below) was used by every participant for at least one sort. Figure 1 summarises how many participants were represented in each top-level group, that is each group at the top of our hierarchical coding.
The single most common criterion was a distinction between objects used for work and objects used for leisure|all our participants used this criteria, and typically early on. Following this there were four common groups: to do with the object itself, especially the form (data type and other surface features), which 7 participants used at least once; the topics an object is connected with (5 participants); the properties of tasks associated with an object (4); and criteria describing people and community (4). A striking nding is the diversity in the criteria derived by the participants. Although we were able to group the criteria into 13 distinct high-level cateorgies, only two of these, work/leisure and form, were named by more than half the population.
After our initial grouping, which was based on labels proffered by participants and not on any statistics of the sorts themselves, there were no clear correlations between criteria| that is, \work" did not look the same as \important" or \Word les" as \makes me angry".
On our analysis, ve of Kwasnik's seven groups were represented in our data: situation, document, time, value, and cognitive state. However, they were very unevenly distributed: criteria we classi ed as \situation" were used by all ten participants, for one or two sorts each; \document" was used by nine participants, for a median 3.5 sorts each; while at the other end of the scale, \time" was used by only three participants and \cognitive state" was used by two participants, once each in each case. 4.2
In many previous studies two groups of criteria|form and topic|were found to be central, and our data reinforces this. There are also, however, notable contrasts.
Work/leisure. To the best of our knowledge, previous studies of classi cation behaviour have not found a work/leisure distinction. However, every participant in our sample used this criteria. This may be because we most of the objects in our study were digital, not paper documents|it is very easy to mingle work- and leisure-related objects online|but it is clearly important and is not explicitly supported by PIM tools.
One participant reported that he used two top-level folders in his le system, and two email accounts, to keep work and leisure information separate. No other participants reported as clear a distinction, however. It should be possible in a PIM/search tool to tag les, or e.g. learn a classi er, to help maintain this distinction. Distinguishing work from leisure contexts might also allow di erent technologies to be used in each case.
People. Our participants did associate their objects in terms of speci c people, but not in the way we might have expected. Rather than linking objects to particular, speci ed people, our dimensions relate to relationships with the community: \popular with many people", for example, \things I will/won't talk about", or \involvement of other people". Unfortunately it is not clear how a PIM or search tool could support this sort of classi cation.
Task, time, and workflow. Users in our study did not group
objects by particular tasks|objects related to task A, to
task B, etc|but four users did group objects according to
whether an object had an associated task, and by properties
of that task (state, importance, and cost or di culty). This
could be used to extend the work of Jones and his colleagues
[
Time was mentioned by four participants. However rather than categorise objects according to time of use (or receipt), as supported by a number of tools, three participants derived criteria from the lifecycle of an object. Criteria such as \when I need to act on this" or \when this is important" will change over time for each object. This is related to Cole's \level of information" dimension. Only one participant used objects' importance to an event, at a particular time, as a criteria. Tools which support an explicit notion of document or task lifecycle, or approximate this e.g. by recording patterns of use, would presumably suit these participants. 5.
The results above suggest that card sorts, in this variant, are useful for eliciting criteria: it does seem possible to gain some insight into how users think of personal objects, and how we might support this. This pilot has, however, highlighted some limitations.
The objects represented by each participant's cards were familiar|that is, they tended to choose objects they had used recently or frequently. They were also selected for sharing, since although we did not record the card titles we did see them. We cannot be sure that the chosen objects represent the sorts of things users may search for in a PIM system, and of course they are not representative of unfamiliar objects. We could instead choose objects from a participant's computer, for example by choosing randomly from the le system and labelling cards with le icons and names, and similar. There is a tradeo , however: if participants did not recognise these objects, the only possible criteria would be le icons and names, and we would learn little. By allowing users to choose their own objects the cards hopefully acted as prompts for other, richer, associations.
On a related point, some media types, such as video or audio, are di cult to represent on cards. It is not clear what this means for eliciting criteria. We are possibly unlikely to get criteria such as \out of focus" (for photos) or \scratchy bit in the middle" (for audio), but participants' familiarity with the objects may mitigate this to some extent.
There are of course properties that are not captured by
this method: links between documents, for example (except
implicit links of the type \sorted into the same pile"). It is
also possible that our presence, and the apparatus we used,
made it hard for participants to think naturally. They may
have been inspired to create other categories if prompted
(as in Goncalves and Jorge [
We also note that the objects chosen varied greatly from participant to participant, and this may have played a role in the criteria that were chosen|although we did see some overlap, possibly there would have been more if the objects were more similar. We are considering constraining participants more in future, for example by prompting them to make a certain number of cards for each media or perhaps having subpopulations sort a shared set of cards, e.g. emails, web pages etc. they have all seen or received.
We hope to extend this work by scaling to a larger group of participants, but we will consider some methological changes: constraining the objects chosen, for example, or careful prompts to elicit more classi cations. Nonetheless card sorts, in the variant here, have proved useful for starting to understand how users think of \personal" digital objects from a wide range of sources and media. Some classi cations were both common and expected, but we did observe interesting di erences both with the criteria found in earlier studies and with the criteria exposed in PIM and search tools.
We would like to thank our participants for their time.