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Intelligent Content

Are UI Cards good for content?

A user interface format known as cards is gaining popularity with mobile and web designers, and is being used in a variety of contexts.  UI cards can simplify screen design, but they don’t always promote good practices for content.   It helps to know when using cards supports content, and when cards can result in problems for audiences.

What are UI cards?

UI cards are boxes of a fixed width that are containers for content.  They may or may not have a fixed height. They can be placed beneath one another, or beside one another, and occasionally, stacked on top of each other.  The metaphor informing the design of UI cards is the index card or the flash card.  Unlike with physical cards, it is rare that one shuffles UI cards: the position of the card is normally determined by the application.

The recent interest in cards echoes a similar interest in the index card metaphor in the early days of personal computing 25 or 30 years ago.  Apple developed an index-card based tool called Hypercard.  The novelist Robin Sloan considers the Hypercard design as “pretty cool” but notes the negative complaints it created: “It squeezes information into screen-sized ‘cards’ and doesn’t allow scrolling through long documents. The academics agree: cards are too limited. Cards are lame.”  Around the same time, Microsoft released a product called PowerPoint that was also based on the index card metaphor.  It too spawned complaints about how well slides present information.

Today, many leading digital brands use UI cards.   Unlike the earlier card-based software, today’s UI cards only take up a piece of the screen, instead of the entire screen. UI cards have quickly become a common interaction design pattern, and some UX designers even refer to a “card architecture” design concept.

Examples of UI cards

Designers tend to use UI cards in one of two ways: to hold a piece of information from a stream of messages, or to showcase items that are part of a collection.

Streams of messages and notifications are generally not related to one another.  Cards help to separate distinct messages from one another, and provide a focal point for users to see updates.  Streamed content can be either summaries or previews.  Google Now uses cards to show summary information, such as sports scores for a team one follows.  Both Facebook and Twitter use cards that show a preview of content available elsewhere, such as an article.

screenshots of Google Now
Examples of cards for Google Now showing updates for user’s stream

Collections are another major use of UI cards.  Whereas UI cards for streams work well on mobile platforms, UI cards for collections work better on tablets or laptops.  Each card is like a small poster that provides a teaser for content.  These cards often highlight visual content with limited substantive information, and provide a way to scan content, but users must navigate elsewhere to get details.  Pinterest is the best-known example of this kind of card, though many other sites use the card collection concept.  The cards aren’t necessarily intrinsically related to one another: the relationship between cards in a collection is defined by their curator — either the user collecting them, or a brand that is displaying the group.

Why are UI cards suddenly popular?

Cards mesh with current sensibilities about digital content: they package content into bite-sized pieces that work on mobile and social networks.  From a front-end perspective, using cards provides an easy-to-apply layout for a responsive web design (RWD) framework.  With their fixed width, cards can be used in one, two or three column layouts, allowing the same card designs to be used on smartphone, tablet, and laptop screens.   Fixed width cards are well suited to the flexible grids used in RWD.  Visual designers seem to like cards.  When using a fixed length, cards form a perfect grid; when using variable lengths, cards form a masonry layout that looks like a vertically oriented brick wall.

Cards are also popular because they seem to tame content.  Cards are modular, so different content items can be stacked together without calling attention to their differences.  Cards minify content, by forcing content to adapt to the constraints of the card boundary and card visual layout.  Designers like that cards allow different content to be mixed and matched without worrying that the design will become cluttered.

Interaction designers like cards as well.  Cards can break down content into digestible bits that allow users to interact with it.  By offering a container for content, cards provide the suggestion that the content is tangible and personal to the user.  Like a Rolodex card, UI cards can contain personal details, such as one’s flight info.  Like baseball cards, UI cards can be collected, and displayed on a virtual corkboard.  They can be traded as well, or more accurately, shared, through social media.  And cards provide containers to break down sequences of interaction, such as Google recommends for its Google Glasses.

screenshot of google glass cards
Sequence of cards for Google Glass. Source: Google

UI Cards as Interaction Design

Most of the impetus for UI cards has come from the interaction design (IxD) community, rather than the content strategy community.  IxD has various goals for promoting UI cards, but these goals are separate from the goals of content strategists.  When IxD refers to UI cards as “content cards” they rarely are focused on the details of the content inside the cards.

UI cards offer appealing aesthetics: they are well behaved on a grid, and provide fluid behavior during interaction transitions.  Cards present an illusion that everything one needs to know is right there in from of them.  They mimic a bulletin board that can be scanned quickly.  When a user wants to engage with the card, many interaction possibilities are available.  The card can be animated, swiped, or flipped. In contrast to the tedium of scrolling, UI cards allow a range of interaction possibilities.

While offering some fun possibilities, UI cards can privilege interaction over the substance of the content on the cards.  IxD’s approach to content is to design it from the outside in: start with the desired layout and behavior, and then expect the content to fit.  By focusing on the container for content rather than the content itself, interaction design can create problems for content.  The size of the container can dictate the size of the content, rather than the other way around.  The tyranny of form over substance is most acute with fixed height designs that follow a precise grid layout.  Such layouts can sometimes cause content to get dumbed down, much the way PowerPoint presentations can squeeze meaning and break flow from narratives because the format limits how much text can comfortably be placed on a slide.   One application called Citia tries to summarize books on square shaped cards, but the pacing of information on the cards is uneven: some cards are dense with information, others less so.

UI cards can contribute to content usability problems that may not be immediately evident.  Users often like UI cards when they encounter them, and don’t notice their limitations.  They see tidy cards often with colorful thumbnail images.  The cards seem optimized to make good first impressions.  But often, the cards end up squashing the content that must go in them, or omitting content details that don’t fit the layout vision.  The most common problems are truncating content, or hiding content below a hidden fold on the card that’s not visually obvious.  Paradoxically, while cards are meant to provide all needed content with a quick glance, they may have vertical scrolling behaviors within them.  Occasionally, they will even have a line of text or icons within the card that requires horizontal scrolling.  Users don’t know if tapping for more information when content is truncated will result in a hover state bubble, or take them away from the card.  Some content cards hide information by placing content on the backside of the card, requiring the user to tap the flip the card — if the user knows to do that.  So for all the talk about cards being a design pattern and architecture, there seems to be a lack of convention for how to design them.   Users can’t confidently predict how any new card they encounter will behave.

screenshot of cards on a recipe site
Collection of cards from a recipe site. Note that the title on one card is truncated, as are the ingredients on many cards. To see the non-visible ingredients, the user must tap the “and more” text. This is an example of content being squeezed into available space.

Another issue with UI cards is their lack of hierarchy.  When all cards are the same size, all cards look equally important, whether they have detailed information, time sensitive information, sparse information, or optional information.  Visual designers like to feature photo thumbnails on cards to help users scan content for items of interest.  That approach can work well, provided the photo conveys real information and is not simply decoration.  Cards about movies might show images from the movie or pictures of the actor.  But if all the cards are about the same topic, say articles about running pinned on Pinterest, and all the cards show clichéd stock photos of a person running intensely, then the photos provide no information to help the user scan many similar looking cards.  Instead, the user must look at every card individually and try to determine relevance based on limited text descriptions.

Cards as content units

An alternative way to look at cars is as units of content, rather than containers for content.  By focusing on units of content, we can ask: what does the card represent?

Content for cards can be pulled from 3rd parties not related to the card publisher, or content can be provided by the card publisher directly.  With 3rd party content, the card publisher often has limited control over the content.  They may capture the first 30 characters of the title, a thumbnail of the first image provided, or the first four lines of the article.  In many cases the card is simply a preview of another unit of content: a full article available elsewhere.

When brands use cards to present their own content, they should be able to choose content elements more carefully. But having control over the content does not assure that the right content is selected for a card.  Suppose the idea is to provide a card to show the weather forecast.  While simple in concept, it still requires choices about what specific information to show, and how that information is accessed.  When the form is already fixed, the choices for the content are restricted.  A severe weather alert is issued, perhaps an impeding hurricane, but there is no space on the card to show that information.  The card becomes a blob that can’t be enriched with new content elements, because the format prevents such additions.  Brands try to work out what content relating to a topic they can fit on a card, rather than ask what content they want to present, and how might cards help them do that.

Too often, content is simply chopped up to fit on cards.   For cards to be useful, the content on them should have meaning outside of the card container.   This requires designing cards for content from the inside out, starting with the content.

Two recent content applications are pointing to how cards can represent meaningful chunks of content.  The field of journalism is under enormous pressure to become more relevant to audiences, and to increase the speed and efficiency of providing updates.  Cards are being embraced by a new journalistic genre called explanatory news that has emerged to address these challenges.  The news sites Vox and Circa are showing how cards can represent meaningful chunks of content that can be combined to form larger units of meaning.

screenshot of cards from Vox
Example of Vox cards used to explain background content.

Vox uses cards that are attached to yellow-highlighted words “to offer deeper explanations of key concepts.”  Stacks of cards “combine into detailed — and continually updated — guides to online news stories.”    Vox cards are topics, focused around a question, and answer why the topic and question matter.  Vox’s idea is that while news developments relating to a topic are always happening, some core material will be relevant context for any developments about a topic.  The cards signal: this is not breaking news, but it is important.   The linking of the cards provides a way for users to navigate through related topics.

Circa uses a concept similar to cards it calls atoms.  Circa is also working to provide more context for news, and decided to use atoms as a way to structure the information relating to a story, so that the atoms link together to form an ongoing narrative.  Each atom is a unit of content, “a fact, quote, statistic, event or image” and each unit is tagged with metadata.  The tagging of atoms allows readers to identify what’s new relating to a developing story, and to follow specific topics of interest.

Screenshot of Circa atoms
Examples of Circa’s ‘atoms’ which are similar to cards. Each atom represents a different unit of content.

Unlike other card examples,  Vox and Circa are using card-like structures to re-imagine the creation and delivery of narrative content.  The separation of content between updates and background information can be applied to other content genres.  Gartner analyst Todd Berkowitz notes the potential use of cards for content marketing: “I like the notion of being able to update stacks. From a content creator’s standpoint, this is easier than trying to update a whitepaper or eBook. The individual cards also offer re-usability. The Vox Cards can be tweeted, shared on Facebook and with Google+ circles, but providers would also want to add a button to share on LinkedIn. So when you add a new card, a provider could blog about it and also tweet it.”

Cards don’t mean updates are now trivial.  While it is easy to add a new card with new information to the stack, brands need to make sure the information on previous stacks is still current.  For some topics, the background information can be changing as well, even if less frequently than the updates.

Emerging best practices

It’s best to consider cards an interesting experiment, rather than a design pattern that reflects the distilled wisdom of many years of practice.  Still, cards hold great promise, and implemented intelligently, can provide benefits for both audiences and authors.

The value of cards comes from their ability to break apart larger clumps of content into more meaningful units that can be managed and recombined more easily.   As Circa has shown, cards can represent different kinds of information, as well as different topics.  Cards can offer an alternative to conventional articles.  Cards can promote a new workflow for content that is more focused on revising only what is necessary.  Ideally, cards could drive wider adoption of structured modular content.

For cards to realize their potential, designers and authors should stop thinking about UI cards as being like index cards, flash cards, or playing cards. UI cards will often need to be elastic to accommodate content intended for them, and they need to be intelligent, not just animated.  In recent years IxD has become critical of skeuomorphism in design — the reliance on physical metaphors in digital design.  UI cards aren’t made of paper stock, shouldn’t look or behave as if they were.

– Michael Andrews

Categories
Intelligent Content

Making linked data more author friendly

Linked data — the ability to share and access related information within and between websites — is an emerging technology that’s already showing great promise. Current CMS capabilities are holding back adoption of linked data. Better tools could let content authors harness the power of linked data.

The value of linked data

Linked data is about the relationships between people, items, locations, and dates. Facebook uses linked data in its graph search, which lets Facebook users ask such questions as find “restaurants nearby that my friends like.” Linked data allows authors to join together related items, and encourage more audience interaction with content. Authors can incorporate useful, up-to-date info from other sources within content they create. Digital content that uses linked data lets audiences discover relevant content more easily, showing them the relationship between different items of content.

BBC sports uses linked data to knit together different content assets for audiences.  Screenshot source: BBC Internet blog
BBC sports uses linked data to knit together different content assets for audiences. Screenshot source: BBC Internet blog

An outstanding example of what is possible with linked data is how the BBC covered the 2012 London Olympics. They modeled the relationships between different sports, teams, athletes, and nations, and were able to update news and stats about games across numerous articles that were available through various BBC media. With linked data, the BBC could update information more quickly and provide richer content. Audiences benefited by seeing all relevant information, and being able to drill down into topics that most interested them.

What’s holding back linked data?

Not many authors are familiar with linked data. Linked data has been discussed in technical circles for over a decade (it’s also called the semantic web — another geeky sounding term). Progress has been made to build linked data sets, and many enterprises used linked data to exchange information. But comparatively little progress has been made to popularize linked data with ordinary creators of content. The most ubiquitous application of linked data is Google’s knowledge graph, which previews snippets of information in search results, retrieving marked up information using a linked data format known as RDFa.

There are multiple reasons why linked data hasn’t yet taken off. There are competing implementation standards, and some developers are skeptical about its necessity. Linked data is also unfortunately named, suggesting that it concerns only data-fields, and not narrative content such as found on Wikipedia. This misperception has no doubt held back interest. A cause and symptom of these issues is that linked data is too difficult for ordinary content creators to use. Linked data looks like this:

Example of linked data code in RDF.  screenshot source: LinkedDataTools.com
Example of linked data code in RDF. Screenshot source: LinkedDataTools.com

According to Dave Amerland in Google Semantic Search, the difficulty of authoring content with linked data markup presents a problem for Google. “At the moment …no Content Management System (CMS) allows for semantic markup. It has to be input manually, which means unless you are able to work with the code…you will have to ask your developer to assist.”

It is not just the syntactical peculiarities of linked data that are the problem. Authors face other challenges:

  • knowing what entities there are that have related information
  • defining relationships between items when these have not already been defined

Improving the author experience is key to seeing wider adoption of linked data. In the words of Karen McGrane, the CMS is “the enterprise software that UX forgot.”  The current state of linked data in the CMS is evidence of that.

Approaches to markup

Authors need tools to support two kinds of tasks. First, they need to mark up their content to show what different parts are about, so these can be linked to other content elsewhere that is related. Second, they may want to access other related content that’s elsewhere, and incorporate it within their own content.

For marking up text, there are three basic approaches to automating the process, so that authors don’t have to do mark up manually.

The first approach looks at what terms are included in the content that relate to other items elsewhere. This approach is known as entity recognition. A computer script will scan the text to identify terms that look like “entities”: normally proper nouns, which in English are generally capitalized. One example of this approach is a plug-in for WordPress called WordLift. WordLift flags probable entities for which there is linked data, and the author needs to confirm that the flagged terms have been identified correctly. Once this is done, the terms are marked up and connected to content about the topic. If the program doesn’t identify a term that the author wants marked up, the author can enter it himself.

WordLift plugin identifies linked data entities.  It also allows authors to create new linked data entities.
WordLift plugin identifies linked data entities. It also allows authors to create new linked data entities.

A second approach to linked data markup is using highlighting, which is essentially manually tagging parts of text with a label. Google has promoted this approach through its Data Highlighter, an alternative to coding semantic information (a related Google product, the Structured Data Markup Helper, is similar but a bit more complex). A richer example of semantic highlighting is offered by the Pundit. This program doesn’t markup the source code directly, and is not a CMS tool —it is meant to annotate websites. The Pundit relates the data on different sites to each other using a shared linked data vocabulary. It allows authors to choose very specific text segments or parts of images to tag with linked data. The program is interesting from a UI perspective because it allows users to define linked data relationships using drag and drop, and auto-suggestions.

Pundit lets users highlight parts of content and tag it with linked data relationships (subject-predicate-object)
Pundit lets users highlight parts of content and tag it with linked data relationships (subject-predicate-object)

The third approach involves pre-structuring content before it is created. This approach can work well when authors routinely need to write descriptive content about key facets of a topic or domain. The CMS presents the author with a series of related fields to fill in, which together represent the facets of a topic that audiences are interested in. As Silver Oliver notes, a domain model for a topic can suggest what related content might be desired by audiences. A predefined structure can reveal what content facets are needed, and guide authors to fill in these facets.  Pre-structuring content before it is created builds consistency, and frees the author from having to define the relationships between content facets. Structured modules allow authors to reuse descriptive narratives or multi-line information chunks in different contexts.

Limitations: use of data from outside sources

While authors may get better tools to structure content they create, they still don’t have many options to utilize linked data created by others. It is possible for an author to include a simple RSS-type feed with their content (such as most recent items from a source, or mentioning a topic). But it is difficult for authors to dynamically incorporate related content from outside sources. Even a conceptually straightforward task, such as embedding a Google map of locations mentioned in a post, is hard for authors to do currently.  Authors don’t yet have the ability to mashup their content with content from other sources.

There may be restrictions using external content, either due to copyright, or the terms of service to access the content. However, a significant body of content is available from open sources, such as Wikipedia, geolocation data, and government data. In addition, commercial content is available for license, especially in the areas of health and business. APIs exist for both open source and licensed content.
Authors face three challenges relating to linked data:

  1. how to identify content elements related to their content
  2. how to specify to the system what specific aspects of content they want to use
  3. how to embed this external content

What content can authors use?

Authors need a way to find internal and external content they can use. The CMS should provide them with a list of content available, which will be based on the APIs the CMS is linked to. While I’m not aware of any system that let’s author’s specify external linked data, we can get some ideas of how a CMS might approach the task by looking at examples of user interfaces for data feeds.

The first UI model would be one where authors specify “content extraction” through filtering. Yahoo Pipes uses this approach, where a person can specify the source, and what elements and values they want from that source. Depending on the selection, Yahoo Pipes can be simple or complex. Yahoo Pipes is not set up for linked data specifically, and many of its features are daunting to novices. But using drag and drop functionality to specify content elements could be an appealing model.

Yahoo Pipes interface uses drag and drop to connect elements and filters.  This example is for a data feed for stock prices; it is not a linked data example.
Yahoo Pipes interface uses drag and drop to connect elements and filters. This example is for a data feed for stock prices; it is not a linked data example.

Another Yahoo content extraction project (now open source) called Dapper allows users to view the full original source content, then highlight elements they would like to include in their feed. This approach could also be adapted for authors to specify linked data. Authors could view linked data within its original context, and select elements and attributes they want to use in their own content (these could be identified on the page in the viewer). This approach would use a highlighter to fetch content, rather than to markup one’s own content for the benefit of others.

Finally, the CMS could simplify the range of the linked data available, which would simplify the user interface even more. An experimental project a few years ago called SPARQLZ created a simple query interface for linked data using a “Mad Lib” style. Users could ask “find me job info about _______ in (city) _______. “ The ability to type in free-text, natural language requests is appealing. The information entered still needs to be validated and formally linked to the authoritative vocabulary source. But using a Mad Lib approach might be effective for some authors, and for certain content domains.

Moving forward

According to one view, most of the innovation in content management has happened, now that different CMSs largely offer similar features. I don’t subscribe to that view. As the business value of linked data in content increases, we should expect a renewed focus on intelligent features and the author experience. CMSs will need to support the framing of more complex content relationships. This need presents an opportunity for open source CMS projects in particular, with their distributed development structure, to innovate and develop a new paradigm for content authoring.

—Michael Andrews