The act of sorting seems so familiar you may think little about it. We sort our possessions to organize them. Some people even sort the sox in their drawer according to color or occasion. We sort to organize things, and more fundamentally, to prioritize our content. When considered in relation to IT, sorting is the programmatic prioritization of content using a simple ordering procedure. Simple sorting routines can offer much value, even if more sophisticated techniques to prioritize content are available.
Most discussion of sorting focuses on its technical dimensions — the rules for sorting correctly. Developers study various algorithms to optimize sorting. Editors must follow detailed rules to correctly sort entries appearing in indexes. Interaction designers focus on how to implement sorting options on user interfaces. Sorting is also utilized in statistical operations, though the strict criteria applied in statistical analysis is different from how people will think about sorting content.
In contrast to its technical dimensions, the experiential dimensions of sorting receive less attention. Besides considering how to sort items, we must also consider why audiences want items sorted in different scenarios. Every day, when we write lists, we make decisions that reflect our understanding of the purpose of sorting . Do we present the list as unordered bullets, or do we number the list items? If we number the items, what do the numbers represent? All of our content faces such existential dilemmas — indicating to others how items are prioritized.
Interaction designers often assume that users will want to sort items appearing in a list. Such an assumption confuses a want with a need. In many cases users don’t want to interact to get specific views of content. They may need different views, but don’t want unnecessary work to get those views. Consider the below screenshot, from a website devoted to user interface design patterns. A spreadsheet paradigm is imposed on web content. Audiences are given the option to sort content on any criteria, but have no guidance about which criteria are important.
This pattern is widely implemented. The user interface presents the illusion of control, but offers little to help audiences understand insights in the content. It presents unnecessary work for audiences, and provides an uncertain payoff. Screens like this continue getting made because sorting functionality is ubiquitous and easy to implement. Adding it seems cost-free. The publisher never did the hard work of asking why the audience wanted the information sorted. What value does a sort offer the audience? How can computers provide such sorting without requiring the audiences to specify it manually?
Sorting tends to be discussed as being a widget and labeling issue, debating the options that are possible, and the user confusion that can result from having those options. Instead of worrying about the clarity of the widgets and labels, a better approach is to make editorial decisions that remove that complexity, and focus on the value sorting can offer.
Sorting practices can be explored in terms of five goals they support:
- Sorting to Locate
- Sorting to Rank
- Sorting to Sequence
- Sorting to Sample
- Sorting to Profile and Evaluate
While these approaches differ in emphasis, they share a common goal of prioritizing content by making a judgment about what’s important to the audience. Users prefer sorted content because it reduces the amount of content they need to view and consider. Sorting doesn’t remove content: it highlights certain content, allowing other content to be ignored. Sorting should make using content easier for audiences, which is why that task needs to be delegated to computers whenever possible.
Prioritizing by Index Value: Sorting to Locate
Indexes are markers used to locate content. With indexes, the chief role of sorting is “findability”.
Alphabetic sorting is the most widely used form of sorting. Though clearly useful, alphabetic sorting’s value is sometimes presumed when it has none. In many situations, alphabetic sorting merely provides a semblance of order without providing any true value beyond psychological comfort. The chief value of alphabetic sorting arises when the user already knows about an item and expects to find it on the list. It can help locate the item, and confirm its inclusion. Developers have use for reverse alphabetic sorting, but audiences rarely benefit from reverse alphabetic ordering. Except in rare cases, it makes no sense to offer audiences a choice of sorting either in ascending or descending alphabetical order.
Another kind of locational sorting involves sorting items into nominal (named) categories. For example, the fact checking website PoliFact identifies statements made by American politicians to see if they have “flipped” their previous position. One can sort statements according to whether is a statement is: 1. Partially (half) flipped; 2. Fully “flopped”; or 3. Not flipped. Such sorting helps audiences locate statements they might be interested in, to judge if a view is simply getting new attention, or whether it is a new position.
Prioritizing by Frequency: Sorting to Rank
Sorting is especially useful with quantitative values. Sorting can rank items on the basis of numeric values associated with the item. Sorting on the quantity determines the ordinal ranking.
In contrast to alphabetic sorting, descending order (from high to low) is frequently useful for quantitative values. People are often looking for content relating to the highest rated item, or best performing one.
In addition to sorting by explicit quantitive values such as price, less obvious applications of rank-based sorting exist that utilize hidden or implicit information. Behavioral values can summarize activity relating to the content, such as when articles are sorted by number of comments. The most common form of behavioral values are consumption-related popularity values, These come in many forms, and are widely used to sort content. Examples included sorting by:
- Best selling
- Most viewed/played
- Most recommended
- Most downloaded
- Most frequently bought together
Another use of quantitative sorts is to create “buckets” that rank content in summary form. Buckets are derived data that are not explicitly visible in the content, where clustering (summarizing frequency) is performed in tandem with sorting. Publishers create buckets covering ranges of values to summarize how frequently items appear. Items are sorted into buckets defined by ranges (e.g., Number of items “below $100,” “$101 – 499,” “$500 and above”). When applied to the sorting of content, these intervals don’t need to be equal in size.
In addition to ranking by a single criterion, publishers can provide different ranking perspectives that consider alternative criteria. Alternative-criteria rankings can be complex, so care is needed to hide the complexity from audiences. Imagine that content addresses five products: A, B, C, D, and E. A consumer ratings website might sort-rank items according to different criteria:
- Best picks for budget buyers: A, D
- Best picks for power users: E , C
- Best picks for novices: A, B
In some respects, this kind of sorting is similar to the sorting of columns in a spreadsheet. In a spreadsheet, one can rank alternative criteria by selecting different columns to reorder rows, to see how rankings change when various criteria are considered. What’s different here is that editorial choices are being made instead of forcing the reader to decide what’s important to focus on. Each category represents a theme rather than formal attribute. For example, what’s best for power users might be a combination of the number of features a product offers, and the extent the performance of key features are above average. That kind of score can be computed behind the scenes. Once items are ranked, only the top two items are presented, to keep the focus on what’s best in each category.
Another ranking pattern is the “Top N by category” pattern. Many publishers will curate content according to how the content ranks within different categories. A news site might present a list of top five articles in sports, in health, in politics and in business. The curatorial decisions relate to what categories are most important in the larger body of content, how many items (N) to show, and on what basis (number of views, comments, etc.)
A rank sorting can be applied to any content that involves a scale. While most often rankings are based on numeric scores, they can also be used with qualitative scales such as good, better, and best. Sorts based on qualitative ranking are known as enumerations.
Prioritizing by Time: Sorting to Sequence
Much content has a time dimension, and can be compared to other content according to various time frames. These comparisons are made through sequence-related sorting.
Chronological sorts use dates to sort content. They can be valuable in either ascending (earlier to later) or descending (recent to older) order. Ascending chronological order is useful when content items reference and build on each other. For example, I find live blogged stories easier to follow when they are in chronological order, since many statements will depend on what was said in prior statements. Many live blogs choose reverse chronological order, however. The benefit of reverse chronological order is to highlight the newest information, which is assumed to be more important than older information. Reverse chronological order works best when each item is independent of the others, so some live bloggers try to make their statements be understandable without referring to other content items.
An important way to sort content is by how stale or fresh it is. Computer scientists refer to stale content as LRU or “Least Recently Used,” where LRU content is considered obsolete and is purged from the computer’s cache memory. The concept of freshness is captured by a term borrowed from accounting known as LIFO or “Last In, First Out”. Content that is new or changed is generally more valuable than older content. People look for new content, or content that’s been updated.
LIFO is useful as a way to sort behavioral data. In many situations, the content someone most recently viewed will be the most likely content they will want to use again. Imagine someone constantly checking some pages relating to the stock performance of different companies they own or are considering buying. Because they routinely check these pages, the sorting would present these pages as a list ordered according to how recently they were viewed, with the most recently viewed at the top of the list. LIFO behavioral sorting is dynamic, changing as audience interests do, so that new items get added immediately, fading interests soon disappear from the list. It is more flexible, and less work, than having the audience create a custom list.
Sequences are a special kind of list where the order is predefined. Content relating to a sequence should be automatically sorted. Sequences can take various forms:
- Procedural with antecedent dependencies, such as “Step 1, Step 2”
- Time-defined, such as “Phase 1, Phase 2”
- Life-cycle or life-stage based, e.g., “R&D stage, Trials stage, Approval stage, Marketing stage”
A big use of sequences is to position content in time, providing a context for the sorted content. Three kinds of content sequences are:
- Before/After sequences
- Now/Next sequences
- Lower/Higher sequences
Before/After sequences position a topic within a spectrum of time. When items are classified according to their stage, they can be compared. Items at the same stage are similar, and one can locate content about events that preceded that stage, and identify content about other entities that are in a more advanced stage. An example of such a sequence sorting would be articles about a class of new drugs, where different companies are in different stages of market introduction.
Now/Next sequences are similar to Before/After, but are more focused on content relating to a single person or entity, rather than a range of entities. Content can be sorted according to what matches the current context, and list content that will be relevant to the subsequent stage. An example of Now/Next sorting is content about repairing a product.
Lower/Higher sequences define time in terms of proficiency required. The content is ranked according to the abilities of the reader. Publishers frequently classify content according to the level of expertise needed to understand the content. Content might be classified as Beginner, Basic, Intermediate, Advanced, or Expert. The sequence associated with those labels is generally easy to understand. Alternatively, the publisher could rank the difficulty of the content according to a color code:
- White belt (= Beginner)
- Yellow belt (= Basic)
- Blue belt (= Intermediate)
- Purple belt (= Advanced)
- Black belt (= Expert)
Such rankings can be useful provided the audience knows the level they are at. They might graduate from one tier after viewing all content in that tier. The content sorting might identify and show all Yellow belt content that the reader has not yet seen.
Prioritizing by Novelty: Sorting to Sample
While sorting is generally thought of as involving either ascending or descending order, publishers can also sort items in a statistically random order. This ensures that items presented are unique each time a page loads.
For audiences, random sorting provides novelty, offering something that may not have been encountered previously. Random sorting and selection can make content viewing more interesting provided the item pool represents content of potential interest.
Random presentation of content can be used to discover if certain content receives greater than expected attention. Publishers might discover through random promotion of content that audiences are interested in topics that previously did not receive much attention when prioritized by frequency of views.
Prioritizing by Relationships: Sorting to Profile and Evaluate
Sometimes audiences need to sort content to see the relationships between items. One example would be to see what’s more general and what’s more specific. For example, Wikipedia uses a hierarchy based on categories, subcategories, and pages. The entry for a category will be broader in scope than an entry for a page that’s not a category.
For many topics, audiences understand which items are broader than others. But for more specialized fields, automated sorting of topics from broader to narrower is useful. Suppose someone encountered content relating to enzymes. They see content on the following topics:
- Acid preparations
- Betaine hydrochloride
The list has no order. Unless the reader is a specialist, they would not know which topic is the most specific. The sorting order from broader to narrower would be Digestives > Acid preparations > Betaine hydrochloride. Hierarchies provide context for the content, indicating what is background and what is detail.
Websites typically present hierarchies to audiences as an input into a task to complete. The website will require audiences to assess visual relationships in a menu, and select the narrower option in a manual process of “drilling down”. Alternatively, publishers can incorporate such sorting into an automated presentation of content, where the content order is predetermined for the audience. Two common patterns are to show broader topics followed by narrower examples, and to show a narrow example then present the broader topic it represents. Instructional content often utilizes non-visible, programmatic hierarchies to guide presentation of content, to ensure comprehension, or to encourage the review of key concepts.
Nested sorts involve sorting on two or more criteria, such as sorting on a qualitative category and a numeric value together. They are difficult for users to specify themselves, so are best offered pre-packaged. Nested sorts are useful for dynamic content that will change frequently. PolitiFact, the Pulitzer-Prize winning website, rates statements by politicians according to their veracity. A nested sort allows the audience to see how many statements by a politician were associated with different categories of truthfulness:
- Mostly True
- Mostly False
- Pants on Fire!
Automated Editorial Sorting
Content designers and content engineers should consider how computers can order lists to deliver the greatest audience benefits. This approach can be described as automated editorial sorting. It is automated, in the sense that a computer algorithm performs the sort without requiring user interaction. And it is editorial in the sense that prioritization delivered by the sort reflects a judgment concerning what information is most valuable to highlight.
Sorting should not be treated as generic functionality that can be applied indiscriminately to any kind of content. Sorting should provide context for audiences. To be valuable, sorting should surface the content that audiences consider to be their highest priority. It is not enough to give audiences tools to dig out that information themselves. Audiences expect publishers to anticipate what they need, and present content to them in a well ordered manner.
— Michael Andrews