The concept of social objects is pretty widely used in social interaction design, but we’re missing a solid definition of what social objects are. Or, whether they really even exist.

The most common use of the term “social object” refers to shared online resources around which interactions develop and coalesce. Examples could include gifts on Facebook, videos, or what have you. The object sort of serves as a shared object, a focus of attention, an actual digital object, and so on. And the object plays a role in governing or informing interactions; we know what objects mean and what to do with them (give them, comment on them, play them, etc.)

But the definition of social object is a bit too fuzzy for me, and for a couple reasons.

Thinking in objects

Firstly, as designers, the object plays into our interest in having an object language — things to design and design for. We are biased to think in terms of objects; objects belong to the world of interface design. So there is a possibility that where there is actually other stuff going on, we focus on the object out of our own interest. (By analogy, consider the anthropologist who focuses her attention on these social objects: a ceremonial mask, money, a wedding ring, a football. How much of the rituals, pastimes, social and cultural practices belong to the object and are explained by object properties? Not much….)

Secondly, objects are easily confused with their properties, attributes, qualities, uses, and so on. This is just how language works. We name a thing and give it attributes, and having done so we have a stable concept. Plato’s ideal chair, vs all real chairs. Concepts then substitute for the real thing. It’s possible that we’re actually talking about the concept of social objects, and not social objects as used.

Which is a more accurate description of gifting on Facebook: the relationship between two friends and the practice of giving gifts on birthdays, or the graphic of the beer mug? The more accurate description of user interaction would be that which explains the practice of gift giving, the symbolic act of presenting a gift, the Facebook tradition of recognizing birthdays, and the social space in which gifts are seen by others such that birthdays create a cause for a stretch of social interaction.

Shared cultural resources

We know that social objects are a shared cultural resource — their meanings are culturally context-specific. We know that many social practices involve social objects. We know that in the digital domain, social objects are unique in that there is no original object but many copies; that an object can appear in many places at once.

For example, I give you a beer mug and it is on your wall but in my stream also — same object, but not really, since one is the one I gave you and yours is the one you received. We’re really talking about a representation, not an object. In other words, the object represents the act.

Representating acts

If the social object is sometimes the representation of an act, then perhaps the focus should be on the act, and on interaction practices, less on the object. The act of recognizing a friend’s birthday by gifting a graphic beer mug is a better explanation of the user activity. The object is merely a representational vehicle by which the activity is sedimented into a mediated, visible, socially recognizable form.

Social objects, then, might be better understood as common forms. Forms in which many kinds of graphics, rich media, even textual forms (for a tweet is a social object as soon as it is retweeted) permit diverse kinds of social interaction. The object, in other words, is not an object, but a form.

If social objects are a form of representation, we can expand our understanding of what they mean. If a form has visual content, it is an image. If it has linguistic content, it is a text or an utterance. If it is a video, it is televisual.

If it is a gift using a graphic, such as the beer mug, then it is both a symbolic token (as described by traditions of gift giving — the gift is an object with meaning inherited from the tradition of gift exchange, and specified by meanings belonging to the object: price, ownership, status, utility, etc) and an image. The beer mug graphic indicates “a drink” (this is basic theory of representation stuff: the image is a beer mug); the act of giving it refers to “get you a drink for your birthday”. The interaction, in other words, is a symbolically-mediated one, referencing a content (get you a beer) and a cultural practice (on your birthday).

It’s all about interactions

We can now see that the interaction situates and contextualizes the object. Not the other way around. The object doesn’t tell us what’s going on, nor does it define uses and interactions. Those belong to practices — namely, practices in which objects are used.

There’s another reason that the object should take a subordinate role to the interaction. Tweets are social objects. Tweets are utterances that take a form, and which, given that twitter is a distribution platform, can be circulated, referenced, recontextualized (posted to streams, blogs, surfaced in search, etc), and so on. We miss out on the significance of the “commodity” form of mediated talk if we think in terms of objects. Because we think of objects as things.

But clearly, anything that can be mediated and used as a shared resource can be a social object. And this includes tweets, things, and much more. So if the world of social objecst includes linguistic statements, gestural tokens (emoticons), signs, numbers (is a follower number a social object? it certainly is the object of a lot of social activity!), images, graphics, avatars, and on and on. We would have to admit that not only is the idea of social objects so broad as to be almost meaningless; but that it’s lost any critical or explanatory power. A concept too big to give us any guidance.

So that’s where I am on social objects. We need a better description. Personally, I think we can borrow from linguistics, semiotics, and anthropology. I would argue that the interaction domain has primary importance, and that the subdomain is symbolically-mediated interaction.

Within this, then, types include:

• linguistic statements;
• symbolic tokens;
• currencies;
• representational objects;
• images;
• gestural signs;
• signs;
• numbers;
• rich media (video, etc — stuff playable online);
• bookmarks;
• avatars;
• etc

Using the disciplines I just mentioned, we would be able to use:

• linguistics for linguistic statements;
• semiotics for signs;
• representational theory for representations (looks like something) and images (is of something);
• cultural anthro for exchange practices and their token objects;
• media theory for numbers (stats, counts, etc);
• and so on.

The types are then unpacked in the contexts of their use, in their contribution to interactions, in their meanings, and as expressions of intent and guidelines for interpretation. And, most importantly, we would be able to account for the enormously innovative and unique ways in which symbolically-mediated interactions can refer to all manner of meaningful activities online, from social games to Second Life (which is, kind of, a total social object world!), from gifting to retweeting, and so on. It’s a bigger project, but the online world is incredibly rich. And I’m convinced that we might misinterpret what’s going on around it if we allow ourselves to think of objects as objects.

In our last article on Johnny Holland we talked about the ‘interpret’ stage of the Social Life of Visualization. This was where a visualization can be tweaked so that the meaning of the data can be seen in a different way and annotated on so that the individual insights that users create can be displayed. The final stage in the shared storytelling process that will be explored in this article is where the tweaking and annotations made to the visualization are captured so the insights can be communicated to others in the community.

We’ll be looking at the rationale for including capture as part of our design framework such as its role in knowledge management and promoting a sense of community engagement. We’ll also look at some of the implications for designing it in the way we have, including the limitation of not being able to get an overall sense of the knowledge captured very readily.

What is the purpose of capturing?

The purpose of this stage is that when users are able to interact within the parameters of a pre-existing visualisation, they need to be able to store ‘snapshots’ of the visualisation to be able to save their work and communicate their understanding of a specific visualisation configuration. Through this process the visualization shifts from being an individual pursuit (where a user visualises their own data) to a communal process of looking for inisight and sharing knowledge (where many users can work on a visualization together).

The capture process is an important part of the design framework because it allows users to become citizens of the community surrounding the visualization by making contributions to knowledge. Through this it facilitates knowledge management by storing the insights that users have made within data visualizations for later retrieval. Knowledge management is not a new concept, considering that software vendors like Microsoft, SAP and IBM have been producing technology that enables it for more than a decade. However in that time social software has emerged which has precipitated two significant changes in the field.

How is knowledge management developing?

The first of these is that read/write social platforms like blogs, wikis and other social platforms have made it increasingly easy for users to create content, leading to a significant increase in the amount of knowledge generated, and therefore the amount that needs to be managed.
In 2003, the last time a significant report on the amount of knowledge contained on the Internet was conducted, it was found that:

• the World Wide Web contained about 17 terabytes of information on its surface
• instant messaging generated five billion messages a day (or 750 gigabytes)
• email generated about 400,000 terabytes of new information each year worldwide
• and the entire Internet generated 532,897 terabytes in electronic flows of new information in 2002.

In 2007, 281 exabytes (i.e. 281,000,000 terabytes) of information was created.

The second change is that the structure of knowledge that needs to be managed is changing radically, given the free form nature of knowledge generation that social spaces like blogs, wikis and social visualization spaces encourage. Consequently new ways of approaching knowledge management are needed, as opposed to simply tagging documents that are contained within a content management system and performing searches based on those tags.

The way in which knowledge is generated is also changing across a number of dimensions.

The first of these is the workplace. No longer is it always a single place for face-to-face interaction but rather, it can sometimes be an anytime, anyplace network of electronically connected spaces. This paradigm is known as the distributed workplace and is emerging as an alternative to the classic co-located scenario. This changes the way knowledge is generated within an organization, because it becomes more asynchronous rather than synchronous.

The second dimension is the approach. As the technologies have emerged to enable it, knowledge generation has taken on a communal approach known as collective intelligence. This is the belief that pooling everyone’s knowledge on a subject together creates a greater depth of information than if one authoritative figure had worked on it alone. Consequently everyone’s contributions create units of knowledge within themselves that it is also important to capture.

Why is capturing knowledge from a data visualization important?

In specifically tying knowledge management back to the shared storytelling process, being able to see what another person saw is an important way of understanding what previous users working on the data visualisation were trying to communicate. The particular way this process is facilitated through the design is also important. The proposed interface allows snapshots to be collected along with discussion, and is a good way to illustrate the evolution of understanding around a dataset. This method allows other users to see individual contributions to see visualizations. It avoids the chaos that might exist if every user’s contributions could be viewed at the same time. Instead it allows a user to use another user’s work as a further exploration and extrapolation of the dataset.

Example of a visualization state in Many Eyes captured and attached to a user comment.

What else is important about the capture process?

The capture process ties other processes within the Social Life of Visualization together because while comments and annotations allow knowledge to be exchanged and to an extent captured, the nature of a visualization means that without seeing what the original user was seeing while they made those comments or annotations, a great deal of the insight that could come out of the process would be lost.

So capturing is the ultimate degree of sharing within the framework, because it shares the community based work that is taking place around the dataset in a visual way. It creates a trail from the initial visualization that really establishes the visualization’s role as a social object within the community by giving it a rich history.

How can an interface be designed to support this?

To specifically design an interface around the capture process, when a user is commenting on a data visualisation they should have the ability to attach a ‘snapshot’ of how they have configured or reconfigured the visualisation at that exact moment in time. They should then be given the option of attaching a text-based comment to the visualization state that they have created.

From here, another user should be able to select a comment that a previous user has made and the interface should work to reconfigure the visualisation to reflect the ‘snapshot’. From here a user should be able to recognise the contribution that the previous user has made to knowledge around the visualization. They should then be able to make a further contribution based on the work of the previous user.

However the real limitation of snapshots is that they do not provide a good overview of the insight that a community has extracted from a visualisation. It is necessary to look through each snapshot and comment to get a sense of what has transpired, when it would also be useful to get a sense of the collective contribution that the community has made through exploration of the dataset.

Conclusion

This concludes our article on the capture process, where we have particularly paid attention to the importance of knowledge management, because the process aims to preserve ideas and insights generated in other parts of the design framework. It also concludes our series on The Social Life of Visualization. For interaction designers, we feel that this is a change in approach towards visualization; no longer is it about making the most visually appealing and sophisticated representations. Instead this creativity should be constrained to giving back people control over the manipulation and control of their data, and providing a good experience along the way.

We have created detailed interaction design patterns for all the phases that we have discussed in this series of four articles. You are welcome to use them to help your own work. You can find out about the interaction design patterns that we have proposed in more detail at our website.

Background

In 2008 the Australasian CRC for Interaction Design (ACID) was approached by Deloitte Digital for their expertise in data visualization which was being developed through the Loupe Project. Deloitte Digital was preparing its accounting firm in Australia for the introduction of XBRL (eXstensible Business Reporting Language) which would see a significant change in the way business reporting was conducted. Rather that sending multiple reports to different agencies, XBRL would produce one set of data that agencies could draw upon for their own purposes when needed. As part of this change, Deloitte has released an online accounting platform called Accounts IQ which will change the relationship between accountant and client to become an ongoing conversation online. This process needs visualization to make complex business data more easy to understand for the client, and an interface to make this conversation process a good user experience. The Social Life of Visualization is the outcome of our research into this solution for Deloitte.

In our previous article on Johnny we outlined the second stage of The Social Life of Visualization, which was the capture stage. If you missed reading it, it dealt with creating an interface that allowed a user to upload a piece of data, create a visualization that expressed an idea about the underlying dataset, and provide the visualization with an identity so that it can exist within an object-centred social network. This allows other people to join in discussions around it. In this article we outline the philosophies and design implications of the interpretation phases such as the notion of sensemaking. We also outline how people can use a data visualization as an interface to explore and make realizations about their data using interactive techniques like sliders and annotations as they go.

The Interpretation Phase

This next stage in the shared storytelling process is being able to interpret the data visualization. The purpose of this stage of the proposed interface design is two fold; users need a way of shifting and reformatting a data visualization so that they can make sense of the whole data set by understanding how it responds to dynamic changes. Users also need to comment on, or draw attention to specific elements of a visualization without compromising legibility of that visualization.

The point of interpretation is that users within a visualization environment can alter a data visualization so that it conforms to their understanding of the data; and thus allows them to have opportunities and tools for making their own sense of the data and consequently make contributions to the shared story.

Gapminder allows a user to dynamically tweak a dataset through the interface of a visualization

Underpinning this process are specific ideas about knowledge management and sensemaking, and how these relate to one another. This process is specifically about providing an interface that enables users to see structure in the data visualization they are working on by ‘tweaking’ it. This is a particular example of information use that defines one of the behaviours of sensemaking – what people do to make sense of the information in their world.

Sensemaking

Sensemaking can be described as a process of creating situational awareness and understanding in situations of high complexity and uncertainty in order to make decisions.

Sensemaking arises when we change our place in the world or when the world changes around us. It arises when new problems, opportunities, or tasks present themselves, or when old ones resurface. It involves finding the important structure in a seemingly unstructured situation. It is an activity with cognitive and social dimensions, and has informational, communicational, and computational aspects.

So an important aspect of the interpretation process is implementing an interface that allows users to take part in sensemaking activities.

Tweaking

In the first part of the interpretation phase, users should be able to tweak the visualization parameters, such as when there is a variable that can be changed to something else (eg. When the value of profit margin can be changed to the value of unit cost). Either that or it can be offered to the user when one or more of the visualization parameters is ordered either ascendingly or descendingly (eg. Time, scale, amount, location). Essentially what occurs through the interpretation process is that rather than the visualization becoming a snapshot of the interface, it becomes an interface that allows the dataset to be explored by the user in an interactive and playful manner. This should encourage them to make greater sense of the dataset and uncover insights.

The ability for users to be able to tweak a parameter value and see how it affects a data visualization helps communicate the relationship that the parameter has to the whole visual analysis. This approach can help people see trends and make sense of complex datasets more quickly than with static visualizations.

Visualization to Interface

In order to specifically turn the visualization into an interface, controls should be built into the data visualization interface that enable users to perform actions such as resorting the date, excluding certain parts of the data, or changing a variable that reflects the outcome of the data. This implementation can be achieved through the use of interface objects such as drop down menus, radio buttons, check boxes and sliders.

The only usability issues that exist in implementation of a data visualization as an interface are clearly communicating which parameter is selected, and what visualization element this affects.

Communicating Insights

Once this has been achieved, users need to comment on, draw attention to, or in other words annotate specific elements of a visualization without compromising legibility of that visualization. This ability has been developed out of research into how people collaborate; and into collective intelligence principles that drive the social web. This ability that is built into the interface works on collaboration and collective intelligence principles. Collective intelligence assumes that everyone knows something about the subject they’re contributing to, and that combining all this knowledge together creates an object that contains a better overall presentation of the subject matter than any one person could hope to come up with. However it is a chaotic process due to the differences of opinion that people may have about a subject.

Example of different users' annotations of a visualization in Many Eyes

How can an interface be designed to support this behaviour?

Consequently to promote the annotation process and guard against the chaos that is a byproduct, this works by creating tools within a collaborative visualization interface that give everyone a chance to contribute something to the original visualization, but at the same time try to avoid the chaos that may ensue. This is achieved by preventing users from drawing freehand over the visualization to make their contributions to the process, but instead provides a type of marker that is in keeping with the visualization that was chosen. This once again aids people’s sensemaking processes by providing a common visual language for people to use to work on the visualization, making the transfer of knowledge from person to person easier as well.

The reason for allowing this process to exist within the interface is to promote discussion of visualization details and sub-elements. This can be achieved by giving users a set of drawing, arrow and box tools as can be found in some desktop software, which provides users with a single method of annotating a visualization that is in keeping with the visualization approach used (eg. Such as using highlight bars in a bar chart, or showing the height of ranges in a flow graph). The only issue with this design choice is that non-disruptive annotations limit the types of insight users can show in a visualization, whereas drawing tools might have allowed users to show other patterns and insights in the data.

Conclusion

This part of our series has discussed why its worthwhile to allow users to explore and re-interpret a visualization, and how setting it up as an interface to a dataset allows them to achieve this. We’ve also explained how you can go about designing an interface to support this type of behaviour. In our next article on Johnny Holland we’ll discuss the final stage of the shared storytelling process which we’ve called capture, and is about creating an interface that supports the preservation of insights into the visualization by individual users and allows these to be communicated back to others within the community.

Background

In 2008 the Australasian CRC for Interaction Design (ACID) was approached by Deloitte Digital for their expertise in data visualization which was being developed through the Loupe Project. Deloitte Digital was preparing its accounting firm in Australia for the introduction of XBRL (eXstensible Business Reporting Language) which would see a significant change in the way business reporting was conducted. Rather that sending multiple reports to different agencies, XBRL would produce one set of data that agencies could draw upon for their own purposes when needed. As part of this change, Deloitte has released an online accounting platform called Accounts IQ which will change the relationship between accountant and client to become an ongoing conversation online. This process needs visualization to make complex business data more easy to understand for the client, and an interface to make this conversation process a good user experience. The Social Life of Visualization is the outcome of our research into this solution for Deloitte.

In our last article on Johnny Holland we provided an overview of what a ‘social life of a visualization’ might look like. Based on a person-centered social network, it showed how the identity of the visualization was important, and how having this allowed the underlying data to retain its integrity and facilitated the process of people interacting around it. Its implementation created a shared storytelling experience around visualization, and we broke this up into three phases; create, interpret and capture.  In this second article, we’ll delve more deeply into the creation phase of the ‘social life of visualization’; including its rationale and the design challenges that it represents.

The creation phase – choosing the right tool

In the creation stage of the shared storytelling experience, the initial dataset is presented as a visualization.  The problem that needs to be overcome is that people aren’t generally well versed in presenting information visually. So the purpose of this process is to help people to decide  how to visualize their data and communicate the meaning of their data to the online community without resorting to text.

While visualisation can be an ideal medium for people to tell stories about their data, the problem is that they don’t necessarily know the best visualisation technique (and by this we mean box plot, bar chart, scatter plot etc.) to use that adequately communicates what their data is about. While this is a problem that exists for the individual person when they are trying to gain some individual insight into their data, it is especially problematic when data visualization is being introduced into a social network. This is because other people need to be able to interact with the visualization, continue the shared storytelling process and add more knowledge to what was contained in the initial visualization.

So an integral part of building any interface that supports a social network for data visualization has to be including a tool that helps people to better understand the techniques they should use to visualize data. Enabling this allows them to focus on the type of story they want to tell through the data visualization, rather than becoming preoccupied with how to tell the story. So the intention of the first part of the creation process is to helps people to visualize their data so that other people within a social network can understand its intention and interact with it accordingly.

Guidelines

The theory behind this comes from the work of several important figures in the field of data visualization and visual thinking:  Tableau Software CEO Christian Chabot, ‘Back of the Napkin’ author Dan Roam, and noted visualization expert Stephen Few.

In his keynote address at InfoVis 2008, Chabot presented five flawed principles of data visualization.  Some of these are considerations for the way this particular aspect of the interface should be designed, or in particular, what it has to achieve to help users. The first of these flawed principles is that people adopt visual analytics primarily to help them see and understand new visual paradigms. The answer to this is that most people’s needs can be solved with tried and tested visualizations such as bar charts, line graphs and scatterplots.

Dan Roam’s work in The Back of the Napkin introduces a simple and straightforward methodology for visual thinking and problem solving. So some of what he talks about provides the basis for the reasons why a person might choose a particular visualization approach. Roam’s approach is to begin by thinking about what sort of question needs to be asked of the data. These ‘problems’ are clumped into those that involve:

• who and what
• how much

when

• where
• how
• why

There is then a corresponding ‘showing technique’ that equates to each of these problems, making a matrix (see below).

Dan Roam's visual thinking matrix from Back of the Napkin

There are only six of these techniques, one for each type of problem, and as Roam points out in the book, that is all that is needed. All visualization techniques are derived from the portrait, chart, map, timeline, flowchart and multiple-variable plot visualization approaches that he uses. His take is that other visualisation techniques are great but they are not necessary in this type of application.

This fits in with Christian Chabot’s second flawed principle of data visualisation; that people adopt visual analytics primarily to help them see and understand massive data. The truth is that people want to better understand small datasets more readily than large ones, and so complex visualization techniques are unnecessary for this. His fourth flawed principle is that people adopt visual analytics primarily to help them see and understand hidden insights. However the real reason that people employ visualisation techniques on their data is to save time.

Complicating this need of people is that according to Stephen Few is that they are still struggling to achieve simple tasks because existing visualisation tools complicate the task of making sense of data and effectively presenting it to others.

Goals

However the key to the creation process is to help people determine their analysis or communication goals and then suggest a visualization approach that maps most closely onto their stated objectives and is appropriate for their dataset.This is instead of forcing people to concentrate on learning the merits of different visualization approaches, and rather it helps people to focus on what they already know about their data and the context they want to present it in. This can be achieved by attempting to determine the communication or analysis goals the person has for their data visualization, including; who they will be sharing the visualization with, what kind of data they will be visualizing, and what outcomes they want the visualization to create.
Based on these factors, it is proposed that the interface would suggest a visualization approach for the data, explaining to the person why that approach is best suited to their goals. Along with this a range of other visualization approaches should be presented to the person, stressing their individual strengths and weaknesses.

Through this process, the person is required to have a good understanding of the original data to be able to choose an appropriate visualization approach that communicates the dataset in the visual medium.

How can a visualization become ‘social’?

As we discussed in our previous article, setting up the social space as an object-centered social network (e.g. Flickr) establishes the visualization as the object that interactions occur around. So while this explains why interaction will occur, it doesn’t necessarily encourage it. On the other hand, giving a visualization an identity makes it recognizable and approachable within the social space, and consequently does promote interactions.

Firstly, to understand the importance of identity to an object, consider its importance to a person within a social network; it is a way of uniquely identifying that person within the social space. It is also the most basic requirement of any social space. However social spaces aren’t always built around people. To refresh the idea of object centered sociality that we’ve discussed previously, it is an alternative to the idea that a social network is a map of relationships, and instead says that people within a social space are connected through the existence of an object. The object centered sociality theory suggests that when it becomes easy to create a digital instance of an object, the online services for networking on, through and around the object will emerge too.

How can users make their visualisations social?

Therefore just as people within social networks have identities so they can be uniquely identified, objects need identities as well. Social spaces have ways of creating these for people. One of these ways is through the creation of a profile that allows the person to provide specific information about themselves that would give other people on the system some idea about the identity of that particular person. Often used in conjunction with a profile is a profile picture or avatar that provides the particular person with a visual identity within the system. This gives other people on the system extra information about the particular person that can only be conveyed in visual form.

Identity of a dataset in Swivel

When visualizations are objects within a social system, they can have identities attached to them as well. A visualization’s identity is created by the title given to it by its creator, its description and the content of original data set as well. However, a visualization can also have visual imagery or an avatar attached to it, in order to more clearly communicate its identity within the social space.

This serves an important purpose for those people within the space that did not create the visualization, in that it adds an extra layer of identity to an object that they have no pre-existing familiarity with. Specifically it helps them to make sense of the visualization, which aids any collaboration which may occur around the object. For the creator of the visualization, it helps them to make sense of what they have created by thinking about its identity and what sort of iconography they might attach to it. An avatar also contextualizes the visualization’s place within a social space. In turn, this objectifies it and allows it to exist on its own within the social environment. It also reduces the cognitive load on other people, and allows the inherent meaning in the visualization to be communicated and consequently transferred to the community with greater ease.

The process can be achieved by integrating with the search APIs of person generated content communities to access images and media that relate to the content of the visualization. The only issue that arises from this part of the creation process is that assigning absolute meaning to media can be tricky, and often fails to communicate effectively across different cultures. People can ‘read’ images and media very differently.

Conclusion

Ultimately the creation phase is the most important in ‘the social life of visualization’ because it is where an individual idea or question about a dataset can be transformed into an object for social interaction. So this process needs to help people who aren’t visual thinkers to make that jump and set up the visualization as an object so that interactions can occur around it. It leads into the next stage which is interpret, where the interface should act so that people can drag further insight out of the data. We’ll talk about this in a lot more detail in the next article.

Background
In 2008 the Australasian CRC for Interaction Design (ACID) was approached by Deloitte Digital for their expertise in data visualization which was being developed through the Loupe Project. Deloitte Digital was preparing its accounting firm in Australia for the introduction of XBRL (eXstensible Business Reporting Language) which would see a significant change in the way business reporting was conducted. Rather that sending multiple reports to different agencies, XBRL would produce one set of data that agencies could draw upon for their own purposes when needed. As part of this change, Deloitte has released an online accounting platform called Accounts IQ which will change the relationship between accountant and client to become an ongoing conversation online. This process needs visualization to make complex business data more easy to understand for the client, and an interface to make this conversation process a good user experience. The Social Life of Visualization is the outcome of our research into this solution for Deloitte.

Recently I came across an article in the Wall Street Journal about online ratings. The article, which surveys a number of online properties, cites the tendency to 4.3: On the Internet, Everyone’s a Critic But They’re Not Very Critical. The article’s authors pretty much capture what many of us get intuitively about why online ratings really don’t work, but I thought I’d break this down from a social interaction design perspective to get at some of the causes of this. First and foremost is the fact that most online systems built to capture user tastes, preferences, and interests engender bias. And online media amplify bias, for a number of reasons.

This bias originates with the user’s intention, which goes unknown and is not captured in the rating system itself. The reasons a user may have for rating something can be many: a mood, attitude, a personal interest, a habit of use, interest in getting attention, building a profile, promoting a product, and so on.

Amplified distortions

Social media, because they provide indirect visibility in front of a mediated public, amplify any distortion baked into the selection itself (a selection being the act of rating something). This amplification is explained in part by the de-coupling of selective acts (rating) from consequences and outcomes.

Selections are de-coupled from personal consequences, which excuses a certain lack of accountability and responsibility. Selections are de-coupled from their context of use, which range from personal utility to social promotion. And selections are de-coupled from social implications, which removes the user from his or her contribution to a social outcome (eg, highly-rated items look popular).

Consider the reasons a user may have for making a selection (rating something). They include:

• personal recollection (like favoriting)
• to inform a recommendation engine (so that it can make better personal recommendations)
• because the item is a favorite (sharing favorites)
• because the social system has no accountability
• because it always creates the possibility of recognition for the user
• because it promotes the item
• because it’s nice (socially; possibly karmic)
• because it’s a gesture about how the user felt

Social selections are thus encumbered by ambiguity: of intent, of meaning, of relevance, and of use.

Can these be addressed and resolved by better system design? Or can they only be resolved by social means?

Considerations

It might be possible to couple ratings with outcomes. This would involve new sets of selections and activities made available to other users and used to create consequences. Users would then consider these consequences when making a rating selection.

Contexts of use could be distinguished, so that users rate with greater purpose. This would involve creating new views of rated content, such as “rate your favorite item this wk,” “rate your favorite genre,” “rate your personal favorite,” “rate which you think is the best,” and so on. Each of these distinctions, if followed by users (!) would specify the selection by means of a different social purpose.

Reduce ambiguity

It might be possible to reduce ambiguity by means of some cross-referencing achieved by algorithms and relationships set up in the data structure. Without detailing these, they would probably include means by which to distinguish:

• the bias of the user him or herself, measured in terms of personal tastes
• the domain expertise of the user, as demonstrated by ratings provided by the user on other items and in which categories/genres/domains
• the social communication and signaling style of the user, which would reveal some of his/her relation to the social space
• use by other users and the public, as a measure of relevance

Cross references could then be applied when aggregating ratings, used to filter and sort the ratings sourced for averaged results. Theoretically, the system would be able to identify experts, promoters, favoriters, and others by their practices.

Social solutions

Social solutions might be created to supply distinctions among the different kinds of social capital involved in ratings. Such as:

• the user’s expertise (domain knowledge)
• trust capital, or the user’s standing within his/her social graph
• credibility capital, or the user’s believability, as measured in loyalty perhaps
• reputation capital, or the tendency of the user’s ratings to be referred to and cited beyond his/her immediate social graph

Finally, ratings systems can diversify possibilities for making selections, and separate communication from ratings selections so that ratings are used less for visibility and attention-seeking reasons (eg users who rate a lot).

There are too many kinds of socially-themed activities and practices in which ratings play a part for me to delve into this here. But each theme could be examined for the social benefits of ratings, for how they attribute value to the user, add value to content, and distinguish social content items to result in shared social and cultural resources. Those distinctions could be used to isolate different rating and qualification systems so that they are tighter and less biased.

In 2009 we are in the midst of an interesting era for data visualization, particularly as it becomes coupled with the social web. Increasing processing speed, bandwidth and storage capacity are making it relatively simple to render and access visual representations of data. Developers have released libraries of code so we can easily create our own visualizations; and access to all kinds of data is becoming incredibly standardized, particularly through the use of APIs. So as visualization becomes much more straightforward to integrate into online environments, it makes sense to rethink how it can best be used in this setting.

You will have possibly already come across social networks about visualizations if you’ve ever used IBM’s Many Eyes or Swivel. Now is a great time to expand on the work of these pioneers in the field, considering that there is a great need for data visualization as a way of addressing the problems of information overload and the technology to support it is now falling into place.

Support storytelling

After some extensive research into the area, our take on visualization within a social space is that it should support a shared storytelling process around a data set. By calling it a shared storytelling process, we mean that the process of data visualization starts with an individual with an idea (or intent to tell a story) and ends with a community who share the story and adapt it. An individual who wants to tell a story needs to know how to do it. But then they need to be able to tell the community about it in a way which lets everyone understand what is going on. Communities like to be able to pass stories around to be able to entertain and educate, but without forgetting what the story was originally about.

…our take on visualization within a social space is that it should support a shared storytelling process around a data set.

visualization

This shared storytelling process can be supported by designing an appropriate web-based interface. In this article we’ll be talking about some ‘big picture’ ideas that have become design implications in our process of conceptualizing this interface. In subsequent articles we’ll be delving into these ideas in more detail and presenting excerpts from the interaction design patterns we have collected to construct the experience of using visualization within a social environment.

Rather than a medium for storytelling, visualization has traditionally been a tool for the analysis of data, and the aforementioned Many Eyes and Swivel have extended its use to the social web in different ways: Many Eyes has focused its attention on supporting the analysis of data through the visualization in a collaborative environment. The site achieves this by using an interface that supports transforming the data to change the look of the visualization, annotation that keeps to the style being used and the ability to save any view being worked on and add a comment to it.

Rather than a medium for storytelling, data visualization has traditionally been a tool for the analysis of data

bringing it to life with Flickr photos

Swivel takes a different tact and prioritizes the idea of using visual communication to express the meaning of the data. It achieves this by using the assigned title of the data set to query Flickr and this produces a set of images that should visually define what the data set is about. This potentially helps to create a wider understanding of what the data is, and how it might interest people within the Swivel community who are looking to contribute. But Swivel doesn’t support much community based analysis of data. Visualizations are presented in a very standard manner (using bar charts, pie charts and scatter plots) and can be commented on but not annotated or manipulated.

Support an holistic process

What we’re looking to do is extend some of the functionality of these sites to support a more holistic process that better supports social activities like collective intelligence and sensemaking as a visualisation is created and reiterated.  We’ve conceptualised this as a three stage process that begins with creation, extends to interpretation and ends with capturing and reintegrating these interpretations back into the conversation.

• The creation process is an individual process;
• Interpretation is one that belongs to the community;
• Capturing saves the process for posterity and allows it to iterate.

For now we’re going to talk about the theory of object centered sociality and how it holds these three different processes together. In subsequent articles we’ll talk more about the importance of the individual processes and how interaction designers can use them when they want to get the most out visualization in a social web setting.

The basis of capturing this shared storytelling experience through visualization is creating an object-centered social network in a similar style to Flickr, where interactions occur around the photos that users share (as opposed to an ego-centric one such as Twitter where users ‘follow’ other users and receive their updates) that enables it. To do this the visualization needs to become a social object within the network. This essentially means that the interface elements used to represent a visualization will afford discussion, or contain things for people to talk about. We can create a visualization as a social object by giving it an identity and making it interactive. We’ll talk about why identity is important in a moment, and come back to interactivity after that.

The basis of capturing this shared storytelling experience through visualization is creating an object that affords discussion

Social begins

Before identity comes into play, the visualisation needs to be created. This is more imperative within a social network because the creator of the visualization needs to understand their own data, and other people need to understand it as well. So we need to understand how data is best visualised. This can be difficult to understand at the best of times, but focusing on the communication goals and intended audience is often the best way to start. This prevents users from needing intricate knowledge of various visualization techniques such as the different properties of a box plot and a scattergraph. Foregrounding these aspects of the process we refer to as MAPPING in the diagram above helps people to focus on the answer they are seeking from the data rather than being bogged down in the process of presenting the visualization correctly.  Once this is achieved then social processes around the visualisation can begin.

But objects can’t exist on their own within the social network and need to have identities built around them. Identity of an object can be implemented just like identity of a person within an ego-centred social network. You can see in the image above that the visualisation on Swivel has an image, a source, categories and it could have tags. These all work together to provide an sense of identity and give people within the social network some chance of being able to work out what the visualisation is about. Along with identity comes reputation and history. As different users interact with the visualisation and make contributions to it, its identity remains unchanged. Adding an avatar provides visual clues about the identity of the visualisation that may not be communicated through the text associated with it.

Back to interactivity

Now we come back to interactivity. In order for social processes within the network to have any real value, then the visualisation should be one that can be manipulated. This allows for further insights to be drawn out of the data that may have been missed when the original user chose to visualise it. So it is not enough to be able to leave comments on the visualisation, which is the standard way that content is treated within most social networks. Users need to be able to tweak a visualization, from switching axes on a line graph, to filtering or bringing in new data, or even changing the type of visualization used.

Capture the process

This process also needs to be captured. Users should be able to highlight parts of the visualization that represent interesting insights into the underlying data set. They should then be able to store these as an attachment on the original visualization that doesn’t in any way detract from what the original visualization represented. In this way the shared storytelling process we talked about earlier is at play. If the visualization is the story, then as it is passed from person to person they choose to promote different parts of the story and neglect others.

Capture insights

Because the visualization has an identity, its original form is recognizable through this process and the subsequent retellings of it become versions of this original that make up a bigger narrative about the issue, or in this case the original data set. This process would probably occur without the interface in place to support it – everyone sees things differently, and everyone expresses the way they see things differently. But with an interface in place to support the processes of an object-centered social network, we can capture the insight that the storytelling process brings to visualization and store it as an artifact that creates greater knowledge about the original subject.

The power of data

Considering that sites like data.gov are launching soon, and technology guru Tim O’Reilly has been saying for a number of years that data will be the ‘Intel inside’ for the web operating system the amount of data that is available for people to manipulate is going to increase exponentially. Visualization has always been a good way of gaining insight into data, and creating a storytelling experience around it feeds into human needs to ask questions and tell stories. So for interaction designers, implementing this type of experience within the data-driven websites of the future has a number of benefits. One of these is that the particular website becomes more engaging for users, and drives take-up. Another that springs to mind is that by making this an interesting and engaging experience, much insight can be drawn out of the data which can be of benefit to the community (in a similar way to Wikipedia in its collation of vast amounts of information) and to the owners of the data, who may discover things about their data they’d never thought of before. So watch out for more articles from us in the future, as we go into further detail about how to implement this experience.

Background

In 2008 the Australasian CRC for Interaction Design (ACID) partnered with Deloitte Digital to research applications of data visualization, through the Loupe Project. Deloitte Digital was preparing its accounting firm in Australia for the introduction of XBRL (eXstensible Business Reporting Language) which would see a significant change in the way business reporting was conducted. Rather than sending multiple reports to different agencies, XBRL would produce one set of data that agencies could draw upon for their own purposes as needed. As part of this change, Deloitte has released an online accounting platform and aims to change the relationship between accountant and client to become an ongoing conversation online. This process needs visualization to make complex business data more easy to understand for the client, and an interface to make this process a better user experience. The Social Life of Visualization is one outcome of our research into this solution with Deloitte.

This post is about social interaction design. I’ve been gestating around the concept of “frames” for the past couple weeks. Frames of meaning, frames of experience, and frames as a concept for a user-centric description of social interactions.

I like frames because they can accommodate our need for a visual metaphor, a temporal metaphor, and a metaphor for meaning. Metaphors are generally a bad idea in theory, in that they communicate (descriptively) but do not explain. But structural and visual metaphors, spatial metaphors, and value/utility metaphors don’t work for me (or for social interactions, IMHO).

• Concepts based on containers can lead us to think in and with boxes — good for presentation but inadequate to the actions that occur around them;
• Concepts based on place, space, and location can lead us to think in terms of structure and stability — good for a sense of design control but inadequate to the durations, episodes, and temporal experience of social interactions;
• Concepts based on structure, which can include containers as well as spaces, lead us to think architecturally — good for building and designing, but inadequate to the system dynamics of social media;
• Concepts based on value and utility can lead us to anticipate user needs and objectives — good for designing for success and usability but inadequate to the psychological dimensions of interactions, communication, and human relationships;
• Concepts based on writing, posting, and messaging can lead us to think in terms of communication — good for the medium’s shift from information to communication, but inadequate to the speech, performance, and social interaction dynamics of social media;
• Concepts based on conversation can lead us to think about the emerging flow- and talk-based trend away from pages and publishing to talk and relationships — but inadequate to the fragmentation and disaggregation of the “conversation” space.

I’m borrowing from Erving Goffman‘s Frame Analysis, a remarkable study of social encounters, and a work rich in concepts of social interactionism. Notably “keying” and “footing,” both of which cover the nuanced means by which we can reference social convention and indicate personal disposition and meaning to coordinate interactions.

Of course Goffman was a master of face to face interactions, and his observations and explanations hold for social media only to limited and clipped degree.

The challenge for social interaction design today, and my interest in the use of frames, is that it seems as if the conversational trend in social media may be running away from us. Namely, that both forms of online talk, and the proliferation of system messages and activity updates increasingly interconnected (think Facebook connect), have resulted two significantly (unintended) consequences.

1. The interconnectedness of separate social media sites, services, desinations, and applications increases the number of arbitrary connections. Arbitrariness is increased when two separate nodes are coupled, when a connection is established, a message distributed, fed, published (etc) to a new context. What was contextually relevant in its original context (eg favoriting a video on Youtube) is more arbitrary when it appears on Friendfeed. Connectedness may serve the Friendfeed account holder, or his/her Friendfeed followers also. But the message itself is more arbitrary, or its meaning as an action is more arbitrary in distributed contexts than it is in its original context (where favoriting videos serves to rank videos).
2. The proliferation of talk in social media, or shift from the page to flow, stream, and conversation, increases the ambiguity of communicative intent. Again, interconnectedness means that messages are viewed, fed, delivered, or otherwise included in a greater number of contexts. Facebook status updates in Seesmic, with the ability to comment from outside of Facebook. Aggregation of updates in Friendfeed, widget distribution of tweets, disaggregated listening on last.fm et al to blogs, Facebook, etc. The interconnectedness of communication platforms raises the degree of ambiguity in message and action intent: in what’s being said, why, to whom, and even about what.

• The arbitrariness of connection can create discovery and serendipity, but also confuse and destabilize the very practice of communication itself. Where does one comment back? Where else will a comment appear? What value is captured by which other site or service if I share, rate, digg, forward, retweet etc an action, a system message, or a user’s message?
• The ambiguity of intent can lead to a greater number of possible responses and reactions to an action, but increases the likelihood of misunderstanding, misinterpretation, our failure (from the perspective of communication).

Social media are social systems. Conceptually and theoretically, social systems have structure but also have actions and communication: thus a better model than architectures, places, utilities, and communication alone. Social media are reproduced constantly out of the information and communication they produce, and which they made available. In addition to making information visible and available, they permit actions that in turn create more communication and enable more actions. Some of these are system messages (user has done X, Y, or Z); some are human communication (status updates, tweets, comments, posts… ). Systems in other words report on their own use as well as facilitate use: and so they continue, ever producing and reproducing information and communication in the form of news that’s meaningful within the social system, actions which select information, and views of those actions which filter, sort, rank, and otherwise apply social evaluations.

As systems interconnect, sharing system messages and distributing user communications among one another, noise levels increase, connections increase, actions (possible and required) increase, and so on.

If social media become too interconnected and if they produce more activity and communication than each can filter/sort and allow users to manage, might they implode or collapse in on their own excess of activity and communication? This is strictly a system question — not a personal concern (yet).

And here’s where frames re-enter the picture. User centric design ought to be oriented to the framing of experience, and in social media particularly, common and shared frames of experience. Also common frames of reference, frames of communication, recognizable frames of action (games, rituals, pastimes etc), and temporal frames (routines and episodes).

Are we losing our frames? In terms of the user experience, is his or her experience running away from us? Can we no longer anticipate the user’s experience, due in part to the level of interconnectedness among social media? Can we no longer assess the user’s experience, due in part to the increased ambiguity surrounding his or her use of (our) applications and services? Can we no longer manage the user experience, insofar as there is now a high level of arbitrariness in the information selected, actions acted, communications created and sent, among users of social media?

If the user experience escapes us, if it is not possible to anticipate uses, to design and forward use cases, to define and order user interests, goals, and use benefits — what can we know of how social media will be used? Not knowing how they will be used, how can we anticipate consequences well enough to design for them?

This is where I am at the moment on this. Frames are still, I think, offer a strong conceptual “framework” for social interaction design. But it is possible that, as personas do more for the designer than they do incapturingtruths aboutthe user, frames will offer more to the designer than they will capture truths of social interactions.

There is one possible solution, but I can only suggest it for the moment. If workable, it strikes me it may change the design paradigm (conceptually at least). It’s a double accounting system. Akin and reminiscent of the double-entry book-keeping that revolutionized finance hundreds of years ago. I sense, and I’ve not yet worked this out, I sense that our action system is unilateral. One-sided. As communication is doubly contingent (two subjects, not one, thus two interpretations of meaning to be coordinated through inter-action), the correct framework for social interaction design probably needs to be a double accounting model. Action intended by user : action perceived and interpreted by user. One might then proceed with all design framing by accounting for user actions (by self) as well as views of user actions(by others). Each “side” has actions and an action system (grammar, language, etc). Or if one prefers, action : response.

I have to consider this further. System complexity may simply overwhelm the possibility of a durable design theoretical framework for social media. Or I may simply be lazy.

Here in Drupal7 User Experience Project land we’ve been moving from ‘iteration zero’ to the actual production iterations. In iteration zero we’ve been doing a lot of our strategic thinking and documenting, but now it is time to start producing output that the developers who are working with us on this project can turn into something that will be contributed to the Drupal7 Project.

There is a real cadence to the project, and although there is no time in the schedule for us to take a breather, between you and I, it has been impossible for us not to do so (just a little), before heading back into the fray. I’ve noticed this effect a few times in agile projects and I think that I’m going to try to encourage project managers to allow for a little breather at this point in future projects I work on.

I thought I’d take a moment to share with you some of the other shifts that start to happen as we move from Iteration Zero into the Production Iterations.

Communication Framework: From Abstract to Concrete

As I’ve mentioned in the past, a big part of the time we spend on this project is spent either communicating with the community about the work we’re doing, our process and our ideas, or trying to work out a better way to communicate with the community.

One of the biggest challenges of the Iteration Zero stage in the project is that it is, by and large, a series of quite abstract and strategic discussions.

It is really easy to forget that many people find abstract and strategic discussions really difficult. I think there are particular types of brains that embrace the abstract better than others, but experience in this project phase is also very helpful.

In Iteration Zero there is often a lot of writing and talking and not much making/showing – this can create a very challenging environment for project participants. It is pretty easy for people to have vastly different interpretations of the same concept and it can be difficult to make sure that everyone is on the same page with the higher level strategy for the design and product. I’ve experienced this recently not only with the Drupal project, but with a few other projects I’m involved in.

Abstract discussions can be difficult to grok due to their predominantly conceptual nature.

It is pretty easy for people to have vastly different interpretations of the same concept and it can be difficult to make sure that everyone is on the same page with the higher level strategy for the design and product.

I can’t begin to tell you how many times I have explained and re-explained the very same concept, each time thinking that it’s been communicated clearly, only to discover that we still have at least two very different ideas about how something is going to work. There are at least two reasons for this: firstly I have to take responsibility for communicating – if the message isn’t being received I have to re-evaluate either what or how I’m communicating. We also have a second and somewhat unique problem when communicating with the Drupal community and that is that they have a tremendously strong mental model of How Things Work In Drupal. Every time an idea is presented the community almost invariably tries to map it directly to their mental model of How Things Work In Drupal – this is natural and what we *do* with mental models, but when the concept we’re suggesting actually breaks the model, we can run into trouble. It just doesn’t compute! It becomes abstract, difficult to understand, as we have to try to find ways to make concepts more concrete.

Iteration Zero can be a stressful time as a result of this abstraction – people aren’t really certain that they know what you’re talking about, but you’re also asking them to make decisions that will be really significant in shaping the product they’ll be getting at the end of the project.

I think it’s pretty common for people to be fairly fraught towards the end of Iteration Zero.

Thank goodness it is also around this time that something excellent happens – things start to become a little more concrete. There are still a bunch of abstract concepts that need to be agreed on, but as designers we’re also starting to get our heads around exactly how things will fit together and we can start to communicate that.

This is around about the time that we had a fundamental overhaul of the way we’ve been communicating with the Drupal community and interested onlookers on this project.

Towards the end of Iteration Zero we were starting to get a little down about the some of the feedback we were getting on the D7UX project – people were saying that they didn’t want to get involved because it was too intimidating for people who didn’t have UX experience and expertise, that they didn’t think it would actually happen or be a success, that they felt that the discussion was too disjointed and widespread.

It was clear to us that we needed to change the way we were engaging with the community to help them help us. Essentially, we needed to change the structure of the conversation from it’s abstract Iteration Zero format to a more concrete format appropriate to the production iterations, and, we suspected, to a format that most of our participants would find much more comfortable.

Over the course of a day, we created a ‘Project Framework’ on the D7UX site by breaking down the project into it’s main component parts and providing a wireframe, description and outline of ‘what we’re thinking’ for each part. Threaded comments allow people to give their thoughts on each component as it evolves over time.

Allowing people to participate in a place that is most comfortable to them is a key part of our communications strategy. We wanted to continue with this strategy even as we move into this new phase of the project, but also to aggregate the discussions into one place and to facilitate this we created a system of tags for the project components and put together a series of Yahoo Pipes to pull tagged content together. We added a link to these pipes on each of the component pages in the framework.

It was a pretty big overhaul and quite a time consuming process, but almost immediately we noticed a significant difference in the way that people were communicating with us on the project – the interactions became much more focussed and productive and felt a whole lot more positive, and that trend seems to have continued. It also makes it much easier for us to be more conversational with the community in the project – thanks to the simple addition of threaded comments and also the aggregation of the main part of the conversation into one place.

Overall, we’re really pleased with the effect that changing the format of the conversation from abstract to concrete has had on the project to date and the effort involved has already been rewarded.

The Challenge of System Design with User Stories

Another major challenge that we’re butting up against at the moment is to try to make a system design fit into an agile environment.

I’m a big fan of agile methodologies and have had a long term interest in finding better ways for UX practitioners to engage in agile methods. Unfortunately, there is no denying that pushing a design project like this one into agile iterations is tricky.

The way that our user stories are being developed at the moment is that the project manager from the developer agency (Acquia) is writing user stories then pushing them over to us to check that they are right and for us to adjust and re-order as required. To date, we have mostly let them sit in a large spreadsheet whilst we focus on the design strategy (iteration zero) and try to ignore the need for user stories.

We’ve done quite a bit of work on developing an Audience Matrix that allows us to take quite sophisticated ‘views’ of the design from multiple audience perspectives, but to translate this into user stories is untenably complex. The alternative to date has been overly simplistic. We are struggling to find a way to make good use of our audience modeling work to date without breaking agile.

Another issue that we’re butting up against is the nature of system design and templating in an agile environment. There are sets of design elements or template components that would ideally be designed in components then re-used throughout the project – for this project examples of these would be the admin header, the overlay window and the edit-in-place interaction model. Describing these using user stories is incredibly clumsy and inappropriate.

Once these elements are built and we start looking at user pathways that make use of them for particular tasks and outcomes then user stories will come into their own, but it seems that in the same way that developers need a piece of time to set up their development environments and databases without requiring user stories being used, designers need some time to get the ‘design environment’ set up without requiring user stories.

Again, this is something that I’ve come across on a number of agile project I’ve worked on but I’ve not seen any allowance for this way of working in Agile UX project methodologies.

If you’ve had similar challenges and some ideas or solutions then I’ve love to hear from you!

Update on Crowdsourcing Usability Testing

In my last update I was telling you about the Crowdsourcing Usability effort we had launched. Since then we’ve seen that WordPress have launched a similar campaign and they managed to get coverage in the New York Times no less, so we will be watching their progress with interest. Exciting times!

Launching Microprojects

Want to dip your toe in Open Source Design? Help out with D7UX? Well, here’s a great way to give it a try – sign up to help out with one of our microprojects! You need to commit just 12 hours over 3 weeks, but you’ll get a feel for what it’s like to design with one of the most vibrant and clever communities you could ever come across. Be warned, it’s challenging but potentially very addictive!

Marshall McLuhan taught us that every medium uses a previous medium as its content. The same applies to social media. But in any social technology the progression of technology and design innovation is accompanied by the increasing complexity of the social practices it enables. This is as true of the stirrup (Mongol warriors, jousting, cattle-herding, equestrian games) as it is of television (reformatted radio plays, stand-up routines, comedy shows, soaps, reality tv) and, most recently, securitized investment vehicles (asset-backed, mortgage-backed, credit default swaps, derived credit swaps, synthetics, even cubed derivatives). At each stage in the “evolution” of the technology, social uses and practices enable corresponding cultural “progress” of increased complexity.

The history of social media began with Geocities, as a simple form of homesteading: establishing an address, or Here I Am. It moved through identity-based sites: or This is Me, such as Friendster. (This was in part why Friendster suffered through Fakesters — it was trading in the attributes of personal identity: trust, friendship, authenticity, etc.). Then came communities: early social networks based on discussions, such as Tribe, and This is What I Think. In the next phase the social network came to the fore: MySpace, Facebook, each of which thematize the social currency or value of social capital, based on more or less real relationships and testing the symbolic and “real” meaning of relatedness: This is Who Likes Me (and Who is Like Me). Today we are animating the face, and extending the face beyond the page.

At each stage of development, a socio-technical dynamic drives what will succeed or fail. This dynamic is like a governing principle of social technology design, and it articulates both design and use together: for each stage of social technology evolution, social practices complexify in increasing order of self-reference. With each stage, previous social practices become available (as references) to new practices. Established social customs and conventions, in other words, can either be practiced “directly” or become embedded and referenced in newer ones. Subsequent levels of social complexity make each new socio-technical practice possible.

we can suppose that the next round will involve presentation layer innovations and meta-level practices

Identity, for example, which is an important theme of dating sites. The early years of dating sites established conventions, meanings, playful and serious behaviors, etiquette, and so on. They saw the emergence of subcultures among online daters, from lurkers to stalkers, late night hook-ups to eharmony’s bureaucratization of romantic compatibility, etc. In its earliest days, dating sites suffered from the stigma that a) nobody’s real, truthful, or sincere on dating sites and b) they’re a means of last resort. But in the ten or so years that they have been around, it’s become understood that some 20% of a profile is inflated, but that everyone does this — and in part because we want to like ourselves, too. Gross lies and deceptions are less common now in online dating; it simply doesn’t pay to misrepresent oneself.

Online dating is now a theme or practice available to other social media. Indeed many, if not most Facebook apps, play in the flirtation and with the subtleties of symbolic and linguistic interaction that enable higher-order social games. Facebook social apps use practices already explored and sedimented out of several years of precipitating socio-technical discovery and experimentation. In this way social conventions can continually reference themselves, making an ever-growing landscape of technical features viable.

If we take status and activity feeds and their applications as another example, we can see this evolution at work and imagine where it goes next.

If it is likely that on-going innovation will kick off with the next level, or layer, up from today’s application interfaces and user practices, we can suppose that the next round will involve presentation layer innovations and meta-level practices. By meta I mean observing the “world” or activity produced through social media. If today’s twitter is for tweeting, meta Twitter would be for watching the world of Twitter.

This progression has already occurred in social networking sites (consider the number of facebook apps that report on facebook user activity, their demo or friend data, social rank, popularity, etc.). The next round of lifestreaming apps will mine usage to construct and represent the feed-based world of activity. Designers will design new and compelling ways to show this activity and make it interesting in the dimensions of social use that can be compelling: presence; activity; social rank; social networks; location; notifications; topics; and so on. Some of these are already off the ground: tag clouds and hashtags for topicality; counters for popularity and rank; twittervision for location; and so on.

With each iteration of social media sites, tools, and applications, designs leverage the familiar for the purpose of something new. We have tried to show here that it is possible to identify aspects of social interaction, social activities, the acts that comprise them, and social technology forms commonly used in social practices. Now, while a designer doesn’t need theory to practice design work any more than a musician needs music theory to play a tune, I hope to have demonstrated that a framework for social interaction design can help organize the field of social media design. One could write further on other social media tools and practices, and identify the modes of use implicated in design choices. And apply these insights to bigger goals, such as new business opportunities and uses for social media. But that would be a separate undertaking — and a separate primer!

Image by Elvire-r

We have covered just three forms of social action common to one kind of social media application. There are others more important to social networking, profile-based sites, mobile, and other kinds of social media. They include the form of self-presentation (profiles), the form of social networking (friends and friends of friends), the form of social gaming (apps, widgets, and games), to mention just three. But our goal here was to use lifestreaming applications as an introduction to applied social interaction design, and we chose to focus on temporality, audience presence, and communication.

Design choices made will shape whether or not questions are answered personally, by friends, by experts, or by anyone.

Now within each of these forms are design formats. These are more specific and particular than broad forms of mediated social action. We don’t have the time here to detail each, but we would be selling the framework short if we failed to at least mention some examples and explicate how a design language might be described for several of them.

We will take just some of the formats of the form of communication as examples. As noted, lifestreaming applications are fairly unstructured and open. But communication, and social talk in particular, can be very structured. According to Erving Goffman, forms of talk in face to face social encounters include such diverse examples as intimacies, pastimes, games, rituals, and ceremonies, in increasing degrees of structure and organization. Intimacies are highly personal and private, and are navigated by participants by means of personality and individual character, competence, and preference. Rituals, on the other hand, can be virtually devoid of personal qualities and will often rely heavily on role, position, authority, and so on. Lacking the context demanded of rituals, online social interactions by nature tend to emphasize higher degrees of personal handling. But some degree of ritualization does seem to have emerged around conventional online behaviors, especially greetings, acknowledgments, and other examples of interaction in which a preliminary acceptance or rejection sets up later communication.

Designers have of course been working with communication application design interfaces for years. And indeed, message envelope, priority, order and sorting, notifications and alerts, and other design conventions were established in email applications long before their adoption by social media. Similarly, groups, channels, and subscriptions share some number of design standards. And the design of chat, too, has influenced IM, and lifestreaming in turn (as have message boards and web-based discussion boards).

Users may be as or more interested in who is talking than in what she or he is saying

But there are ways to structure talk and communication that deal less specifically with message formatting and navigation and more with the structure of the kind of talk. Questions and answers, for example, are a structured kind of talk. Designers can use question and answer formats to facilitate and display these kinds of exchanges. The designer may also use conventions for the display questions and their answers, ratings, answer threading, answer categorizing, and more. There are some lifestreaming applications built around questions and answers, and there will certainly be more. In each, designers can choose how to route questions (publicly, to a social network, by design, or by text formatting), how to handle answers, how to make them searchable and browsable, how to tag or categorize them, qualify and rank them, verify, validate, accredit, relate them, and so on.

Design choices made will shape whether or not questions are answered personally, by friends, by experts, or by anyone. Those design choices will in turn shape whether or not questioning and answering is a social activity, a personal activity, or whether it serves the purpose of building new relationships, creating a knowledge base, surfacing experts, recommenders, and so on. Without going into the design white-boarding that might accompany these choices, we can see already that just one format of communication (QA) is richly textured and design-ready.

We could also take the example of symbolic exchanges, and of meta-communication in general. Here we deal not necessarily with what is said but with what is meant, and not in words but in hints, suggestions, solicitations, and other non-linguistic forms of symbolic interaction. Symbolic interaction on Twitter and on most lifestreaming apps is conducted with words. Twitter does not have an icon or smiley set available, as most IM and chat applications do. For this reason, the use of @name by convention solicits a written reciprocal acknowledgment. (Users can be nice, generous, or cold and unresponsive, or seem to be, depending.)

There is the example of what we call the message envelope: its urgency, priority, newness, subject, and addressing. Again, lifestreaming applications share the stripped-down design simplicity of flat messaging presentation. All messages look the same, even those that reply to or direct message a user. And there is no distinction between “normal” and “commercial” messages. These enhancements would be easy to imagine, regardless of whether we think they would be improvements to the application. These are important attributes of communication insofar as they tell about the message, conveying how important it is, time sensitive it is, and so on. Currently, lifestreaming apps simply show messages. But a meta-Twitter application that summarized twitter activity might be an interesting tool if it provided visual cues and notifications — sparing the user the need to read messages one by one.

In addition to structuring and organizing communication and messages, we can structure and organize the speakers or participants. Users may be as or more interested in who is talking than in what she or he is saying. Users can be grouped, by their relationships or by their affinity to topics and themes. And of course direct communication between members can be distinguished or separated from the rest of the stream of talk. The formats for these are emerging only now, and will evolve as social technologies proliferate and increasingly go mobile.

It would take significantly more time and space than we have here to outline and arrange the forms and formats of social action for social communication alone, let alone other forms and their related formats. And these would only address the interactions on social media. We are already seeing games and experiences that play with the meta-data and representation of online social interaction, and these would require their own frameworks of social action. For this reason we will stop here and conclude with a view to what comes next for social media.

We conclude this post in the next and final part.

Image by timparkinson