Social navigation is a form of social computing was introduced Paul Dourish and Matthew Chalmers in 1994, who defined it as when "movement from one item to another is influenced as an artifact of the activity of another or a group of others".Dourish, P. and Chalmers, M. (1004). Running out of space: models of information navigation. Proceedings of HCI'94, Glasgow, August 1994. According to a 2002 study by J. Kuljis and R. Juric, "social navigation exploits the knowledge and experience of peer users of information resources" to guide users in the information space, and that it is becoming more difficult to navigate and search efficiently with all the digital information available from the World Wide Web and other sources. Studying others' navigational trails and understanding their behavior can help improve one's own search strategy by guiding them to make more informed decisions based on the actions of others.
Prior to the advancement of Web 2.0 and the Social Web, the World Wide Web had been a solitary space where users were unaware of where anyone else was browsing or navigating. The scope of social navigation research has been increasing, especially as information visualization improves. Displaying social information in virtual spaces allows user behavior models to make digital systems feel more social and less solitary.
Supporting theories and techniques
The concept of social navigation is supported by several theories. Information foraging theory studies human behavior when they are seeking, gathering, sharing and consuming information. It applies optimal foraging theory to human behavior when they navigate to information, and explains how people benefit from other people based on history-rich digital objects, which explains the idea of used items or paths. For example, a used book that has notes, highlights and underlines is different from a new book. History-rich digital objects help people find the target faster and more efficiently.
Information foraging is an alternative to food foraging and ant colony optimization, which state that information human-hunters follow others’ paths to optimally reach their target. Optimal information must maximize the value of the information that is gained per unit cost (like time or effort). This theory supports collaborative activities, and is a guide for designers to build good interfaces where users can benefit from others' research.
The weaknesses of this theory are when people mistrace information; they cannot be redirected unless they figure it out, and optimization is not always the case for human behavior
The information patch model studies time spent in navigation in filtered information and clustered information, and works to optimize the overall information as fast as possible; the information scent model determines information value by taking the most useful cues that have been used by other users; and the information diet model (prey selection) explains how people select the target information based on others' selections, which leads to optimal satisfying information.
Webpage design is also important in how a user interacts with the internet in a social manner. There is a correlation between accessibility and popularity: the more functional a website is, the more traffic it will receive. A more frequented web service will naturally be a more social experience. There are numerous factors that contribute to accessibility, such as a page's location, properties, number of hyperlinks, and modes of access. As every person has a different approach to surfing the web, Internet navigation is defined as "[t]he creation and interpretation of an internal (mental) model, and its component activities are browsing, modelling, interpretation and formulation of browsing strategy." There is a theory that if a user calibrates their browsing strategy to reflect their interests, pages relevant to their interests will be found more easily. Uninformed navigation through hyperlinks can be misleading and result in a higher number of unwanted sites being accessed. Bookmarking allows users to return to previously visited pages. When a population bookmarks the same page and visits it frequently, it forms a sense of community. Recently, live updating of other current users adds another dimension to the social aspect of web browsing.
Collaborative filtering is another technique that is prevalent and utilized in social navigation. It suggests that if users were presented search results based on traffic by others who share similar social interests, it would result in a more rewarding and efficient experience. For example, Amazon.com has a "Customers Who Bought This Item Also Bought" feature that presents shoppers with other products bought by similar users, which streamlines the flow of web browsing and facilitates access to more relevant pages.
Social navigation can also be discussed in different types of virtual worlds. Munro shares some original conceptions when considering social navigation. Instead of individual interaction, it can be presented as "a way of moving through an information space and exploiting the activities and orientations of others in that space as a way of managing one's spatial activities". Munro points out that spatial navigation, which mostly depends on the structure itself, like landscape or map, can be contrasted with social navigation. User can interact not only with the data and objects in a specific space, but also with other individuals and their interactions.
Traces of users' activities As users navigate through online communities they leave traces of their activities, both intentional and unintentional. Intentional traces include posts, responses to other users’ posts, number of friends, uploaded media, and other activities where users intentionally share information. Unintentional traces include browsing history, times spent on particular pages, bounce rates, and other activities where users’ actions are automatically logged by web servers into server logs.
Björneborn categorizes online community users as “trace leavers” (i.e. users who leave actionable items) and "trace finders" (i.e. users who follow traces left by trace leavers). These participatory activities can guide other users’ information seeking behavior and influences features of social search and social navigation. Combining trace-leaving activities of social browsing with the concept of social searching relies on recording and reusing focused search activities of like-minded searchers to produce search results that are better suited to the needs of a particular online community, as demonstrated by Freyne et al.
Websites such as Amazon.com analyze user traces, such as history of purchases or product reviews, to generate recommendations for other users (e.g. "Customers Who Bought This Item Also Bought..."). Online platforms for collaborative software development such as GitHub rely on activity traces, such as the number of repositories, history of activity across projects, commits, and personal profiles to determine its users' reputations in the community.
User activity traces can be used to model users’ behavioral patterns and trends to determine the health of online communities (whether a community would flourish or diminish). Such models can also be used to predict propagation and future popularity of content, or predict results before voting occurs. Activity and traffic patterns can be used to evaluate the performance of existing systems, and improve site usability, architecture, and infrastructure.
Tag-based social navigation
There are primarily two strategies to explore and discover an information space: the first one is the regular search, where users are aware of what they are searching for. Under this context, users have a target information in mind. They usually need to formulate a search query first before inputting it into a search engine; another search strategy is navigation, where users do not have a target information in mind but rather explore through pieces of information by following certain hyperlinks.
Navigation is considered to have advantages over searching, since recognizing what users are looking for is easier than formulating and describing the information people need, which refers to the "vocabulary problem". Social tagging serves as a new social way of organizing a set of resources, and approaches the "vocabulary problem" from a new social angle. Social tagging systems allow people to annotate a set of resources according to their own needs with freely chosen words—tags, and share them with other users of the social tagging system. The result of this human-based annotation of resources is called folksonomy. Examples of social tagging systems are BibSonomy, CiteULike, Flickr, and Delicious.
Tag cloud
A tag cloud is a textual representation of the topic or subject collectively seen by the users and it captures the "aboutness" of the resource.
Tag clouds are easy to build, intuitive to understand, and widely used. It can also represent the three types of relationship among users, tags, and resources in the tagging systems. However, there is a size limitation on the tag cloud that can be presented in the screen; selecting the best tags and structuring the information space to present the relationships in the tag cloud is important.
Tag clouds are very simple, and can be applied to support the user. Researchers find that tag cloud is usually more useful for the following four different tasks, as illustrated by Rivadeneira et al.: Search: finding the presence or absence of a given target Browsing: exploring the cloud without a particular target in mind Gaining visual impression about a topic Recognition and matching: recognizing the tag cloud as data describing a specific topic
Researchers also found that different layouts are useful when performing different tasks. They also demonstrated that tag cloud typography (font size/position) matters: font size has a bigger impact on finding a tag than other visual features like color, tag string length, and tag location.
Based on previous research, common ways to perform tag cloud evaluation are: Using certain evaluation metrics for tag clouds with respect to coverage, overlap, and selectivity User navigation model that combines with the evaluation metrics to allow tag cloud evaluation with respect to navigation User study to evaluate tag-based information access in image collections Examining the navigability assumption (the widely adopted belief that tag clouds are useful for navigation)
Tag clustering An issue with social tagging data is the lack of structure. Synonymy, polysemy and homonymy or problems regarding tag semantics are additional issues related to tagging data. There are two main categories: flat and hierarchical clustering algorithms.
Flat classification can refer to three methods: content-based method, which is a widely-adopted algorithm for tag cloud selection is TopN algorithm proposed by Venetis et al.; network-based method, which splits a graph of connected tags into clusters; and machine learning method, where the semantic relationship between tags is considered.
Hierarchical tag clustering refers to the creation of a hierarchical structure out of unstructured tagging data. The structure can be seen as the users’ mental maps of the information space, and can be used as a navigational aid. Hierarchical tag clustering can refer to three methods: Hierarchical clustering is the method that adapted the K-Means algorithms to work with textual data and create a tag hierarchy in a top-down manner Affinity propagation characterizes each data sample according to its ”responsibility” and its ”availability” values. The input of the algorithm is a set of similarities between data samples provided in a matrix and the output of the algorithm is a hierarchy, and each node in the hierarchy represent a unique tag