Social navigation is a form of social computing introduced by Dourish and Chalmers in 1994. They defined it as when "movement from one item to another is provoked as an artifact of the activity of another or a group of others". According to later research in 2002, "social navigation exploits the knowledge and experience of peer users of information resources" to guide users in the information space. With all of the digital information available both on the World Wide Web and from other sources, it is becoming increasingly difficult to navigate and search efficiently. Studying others' navigational trails and understanding their behavior can help improve one's own search strategy by helping them to make more informed decisions based on the actions of others. "The idea of social navigation is to aid users to navigate information spaces through making the collective, aggregated, or individual actions of others visible and useful as a basis for making decisions on where to go next and what to choose."
Prior to advancement of Web 2.0 and the Social Web, the World Wide Web had been a solitary space where users did not really have knowledge of where anyone else was browsing and navigating at the same time or different time. Social navigation can help to give users a sense of social presence. The scope of research on social navigation has been increasing especially as information visualization improves. Displaying social information in virtual spaces allows the modeling of user behavior to make digital systems feel more social and less solitary.
- 1 Supporting theories and techniques
- 2 Social navigation support in information space
- 3 Traces of users' activities
- 4 Tag-based Social Navigation
- 5 Implementation examples
- 6 Drawbacks of social navigation
- 7 Recent trends and implementation in products
- 8 See also
- 9 References
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. Information foraging theory applies optimal foraging theory (OFT) to human behavior when they navigate to information. It explains how people get benefit from other people based on history- rich digital objects which explains the idea of used items or paths. For examples, a used book that has notes, highlights and underlines is different from a new book, and footprints where people follow others’ footprints to get the right direction. History- rich digital objects help people to find the target faster and more efficient.
Information foraging, also, is an alternative to food foraging and ant colony optimization which states that information human-hunters follow others’ paths to reach their target in an optimal time. The optimal information has to maximize the value of the information that is gained per unit cost (like time or effort). This theory supports collaborative activities. It is a guide for designers to build good interfaces where users can get benefit from others research.
The weaknesses of this theory are when people trace information in a wrong direction, there’s no way to re-direct them unless they figure it out, and optimization is not always the case on human behavior; humans make decision when they are satisfied with the result.
The mere design of webpages also plays an important role 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 location of the page on a website, properties of a page, number of hyperlinks on a page, and possible ways of arriving at the page. Every person has a different approach to surfing the web. Internet navigation is defined as "The 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, more interesting pages will be found. Uninformed chugging through hyperlinks can be misleading and result in a higher proportion of unwanted sites being accessed. To improve surfing, users should formulate a strategy, browse content, and then adjust based on how they judge the quality of the session. Bookmarking is way to ensure you return to sites that appeal to your interests. This is the building block of social navigation as it creates a hyperlink that is saved for future browsing. When a population bookmarks the same page visits it frequently, it forms a sense of community. Recently, live updating of other present users adds another dimension to the social aspect of web browsing. For example, Facebook has a small green circle next to specific names in the chat window indicating those users are also on the site. How we communicate with others using web mediums is a foundation of social navigation.
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 users that are similar to the user. This streamlines the flow of web browsing and gets people in touch with more relevant pages.
Social navigation can also be discussed in different types of virtual worlds. Munro shares some original conceptions when considering social navigation. One of them is, 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 also points out that spatial navigation, which mostly depends on the structure itself, like landscape or map, can be contrasted with social navigation. User may even have interactions not only with the data and objects in this specific space, but also other individuals and their interactions with it.
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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 search 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 use traces of users' activities 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 (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 in order to determine online communities’ health (whether a community would flourish or diminish). Such models can also be used to predict propagation and future popularity of content, or predict voting results before voting even occurs. Furthermore, activity and traffic patterns can be used for evaluating performance of existing systems, improving site usability, as well as site architecture and infrastructure.
People are seeking for useful information everyday, and there are mainly two strategies to explore and discover an information space. The first one is the regular search: people know what they are searching for. Under this context, users have a target information in mind. They usually first need to formulate a search query in mind and then search that query in the search engine, such as Google. In contrast, another search strategy is navigation, where people do not really need a target information in mind but rather explore through pieces of information by following certain hyperlinks.
It is usually considered that navigation has advantage over search since recognizing what we are looking for is much easier than formulating and describing the information people need, which also refers to the "vocabulary problem". Therefore, social tagging serves as a new social way of organizing a set of resources. This free-form annotation approached 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, which refers to a fold-generated taxonomy. Examples of such social tagging systems are BibSonomy, CiteULike, Flickr, or Delicious.
Tag-based navigation is thus the process to find path between information resources in a tag-based information system. The navigation process is usually supported by either a tag cloud or navigate through a tag hierarchy.
Tag cloud is a textual representation of the topic or subject collectively seen by the users and it captures the "aboutness" of the resource.
On the one hand, tag cloud has many benefits. It is simple 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. On the other hand, since there exists the limitation on size of 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 would be an important issue.
Tag clouds are very simple, they can be applied to support the user in multiple ways. Researches 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
Researches also found that different Layouts is useful in performing different tasks. In addition, researches also demonstrated that tag clouds typography (font size/position) matters: font size has a bigger impact on finding a tag than other visual features like, e.g., color, tag string length and tag location.
- Use certain evaluation metrics for tag clouds with respect to coverage, overlap and selectivity
- User navigation model that combined with the evaluation metrics allows a tag cloud evaluation with respect to navigation
- User study to evaluate tag-based information access in image collections
- Examined the navigability assumption (the widely adopted belief that tag clouds are useful for navigation), they found that it does not hold for every social tagging system
As mentioned previously, one of the main issues with social tagging data is the lack of structure. Synonymy, polysemy and homonymy or problems regarding the semantic of the tags are additional issues related to tagging data. Previous research demonstrate us different algorithms for clustering tagging data which could tackle the above problems by organizing the tags according to a classification schema. Depending on the classification schema, there are two main categories: flat and hierarchical clustering algorithms.
Flat Classification can refer to three main methods:
- Content-based method: one very widely adopted algorithm for tag cloud selection is TopN algorithm proposed by Venetis et al.
- Network-based method: split a graph of connected tags into clusters, ideas from the concept of modularity.
- Machine learning method: consider the semantic relationship between tags. Similar idea from the Latent Dirichlet allocation (LDA) model.
Hierarchical Tag Clustering refers to create a hierarchical structure out of unstructured tagging data. This way, hierarchical structure can be seen as the users’ mental maps of the information space, thus the hierarchies can be used as a navigational aid in different ways.
Hierarchical Tag Clustering can also refer to three main methods:
- Hierarchical K-Means 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
- Generality in Tag Similarity Graph method includes the following steps:
- The input of the algorithm is a similarity graph of tags
- Set the most general node (can be measured based on different graph centralities) to be the root of the hierarchy
- All other nodes are added to the hierarchy in descending order of their centrality in the similarity graph based on the following rules:
- Calculate the similarity between all currently present nodes in the hierarchy and the candidate node
- If their similarity is above a given threshold: the candidate node is added as a child of the most similar node in the hierarchy
- Otherwise, the candidate node is added as a child of the root node
- Typical versions for centrality measure and similarity measures:
- Degree centrality as centrality measure and co-occurrence as similarity measure (DegCen/Cooc)
- Closeness centrality and Cosine similarity (CloCen/Cos)
Modeling tag-based navigation is important for understanding the processes taking place in a social tagging system and how the system is used. There are two essential factors to understand modeling tag-based navigation in social tagging systems: basic modeling framework for navigation and theories understanding of the ability of folksonomies to guide navigation.
- Navigation on the Web can be seen as the process of following links between web pages
- Markov chain models assign transition probabilities between web pages (also called states)
- First order Markov chains (the transition probability between states depends only on the current state) are more commonly used
- Navigation in a network can be modeled by the message-passing algorithm decentralized search
- The message holder passes a message to one of its immediate neighbor nodes until the target node is found
- That is, at each step, the decision of where to go is made by the local knowledge of the network only
- Finding a path to a node (already realized in web navigation)
Theoretic Suitability for Search
Different scholars also provide the theoretic support to argue the suitability of Folksonomies as a navigational aid, there are mainly four perspectives as illustrated below:
- Network theoretic perspective has two aspects: The general navigability of a folksonomy as a graph; or The ability of tag hierarchies to guide navigation in such a graph
- Information theoretic perspective suggest to see social tagging as the collective effort of creating a mental map that summarize an information space
- Information foraging perspective: describe the human information seeking in a digital environment
- Tagging vs. Library approach: discussed the pros and cons of the “tagging system”. They proposed a definition of a controlled vocabulary and compared unrestricted free-form vocabularies emerged in social tagging systems to controlled vocabularies
Pragmatic Folksonomies Evaluation
The author proposed in the paper the general idea that people can leverage on the OUTPUT produced by folksonomy algorithms (hierarchical structures) as INPUT (background knowledge) for decentralized search for the following reasons:
1) The performance of decentralized search highly depends on the quality of the hierarchical clustering results that developed to facilitated navigation.
2) The performance of the decentralized search algorithm depends on the suitability of folksonomies.
3) Therefore, the authors proposed that we can leverage the simulation method on decentralized search to evaluate the suitability of folksonomies.
Various applications of social navigation have been studied in educational systems. One such example is Knowledge Sea II. Compared to traditional approaches (so-called Closed Corpus), this system is able to gather online information (named Open Corpus) and feedback from different sources. Group traffic is used as feedback to indicate social navigation information such as "the most important parts of the textbooks". After a classroom study, Knowledge Sea II system shows better performance in visualization of content relevance of the textbook and satisfaction of student users.
Mertens and his colleagues optimize an existed system: virtPresenter, with addition of hypermedia navigation concept. bookmarks, footprints and structural elements are integrated to help users to access lecture recordings and support social navigation for the future users as well. The new version of virtPresenter shows better performance in social navigation function such as: visualization, week-based filtering and exchangeable bookmarks.
Farzan and Brusilovsky introduce the AnnotatEd system, which combines functions of web annotation and adaptive navigation support to synergize social navigation application in web-based education. With implementations of web annotation and social navigation support (SNS), this AnnotatEd system could integrate into Knowledge Sea II or ASSIST-ACM. AnnotatEd integrated Knowledge Sea II has been evaluated for six semesters in School of Information Systems University of Pittsburgh, which shows the significantly higher positive user attitude towards this new system because of its social navigation integration.
Urban mobile information system
In Marcus Foth’s (2008) book, a system called CityFlocks is introduced to show social navigation implementation in urban mobile information system. This implementation is described in more details by Bilandzic et al. (2008) earlier. In the article, to solve so-called “socially blind” problem even based on booming of mobile phone user, CityFlocks is designed enabling web annotations combined with coordinates upon physical targets in the city. Also, this social navigation implementation could be applied in direct or indirect way. To achieve this, focus groups are chose to collect requirements and problems in social navigation. Accordingly, CityFlocks is designed and generated using appropriate techniques such as Google maps and information retrieval. User tests of CityFlocks indicated that an indirect approach is more acceptable than a direct one, because of the concerning of talking to a complete stranger.
Two prototypes of social navigation system have been introduced: “Juggler” and “Vortex”. The “Juggler” system combines MOO, a textual virtual environment, and a Web client. The “Vortex” system uses an alternative way: simplified desktop, to present URLs.
History-enriched implementation of social navigation is based on the making the traces of behavior of latent users visible to future users. The implementation of such idea can be traced back to the first system introduced by Wexelblat and Maes who introduced an information spaced enriched with various social navigation mechanisms: document map, navigation paths, and documents' annotations and signposts. They used six properties in Footprints system, Proxemic versus Distemic, Active versus Passive, Rate of Change, Degree of Permeation, Personal versus social, and Kind of Information. More examples of history-rich information spaces has been implemented in different context such as educational domain, location-based networking, and food recipes.
Social Navigation Network (SoNavNet), a location-based social networks (LSBN) application, devised by Karimi and his team, is aimed at sharing navigation experience. Other than simply showing the shortest time or distance like Google Maps, users’ specific experience and recommendation are underlined. With both geo-position and message function, SoNavNet allows users to send request to their friends while present their current location and Points of interest(POI), from which, they will acquire route and venue information oriented to their needs.
Svensson and his team created a recipe recommendation system, European Food On-Line(EFOL), which equips with both direct (chatting with other user) and indirect navigation (collaborative filtering) approaches. Social Navigator was implemented as a Java servlet for modelling users' behavior and net-based communication usages.
Embedded visualization implementation
Social navigation implementation plays a significant role in guiding users to forage information they need. Visualization is an indispensable part when showing information in great detail. Willett and his team designed Scented Widgets, which improves navigation in both popular and undiscovered realms with embedded visualization. They implemented scent metrics with a standard interface widget and used visual encoding for data. Hue, Saturation, Opacity, Text, Icon, Bar Chart and Line Chart are scent encodings to highlight various information, which can display different types of data at the same time. They used Java Swing and the platform’s pluggable look and feel functionality to create and change widgets at runtime. In order to design a user-friendly interface, they followed Scent Encoding, Layout, and Composition guidelines which gave clear instruction in how to use scent widget better showing multiple information.
Implementation in Usable Security
Given a file sharing system, every user can determine which files in their own computer can be shared through the network. Initially, user have to configure these security-related settings on their own. Because of being uninformed, about eight out of ten users would unintentionally leak their private information such as credit card information or address, which can cause unexpected results. Based on this problem, Paul DiGIoia and Paul Dourish from University of California, Irvine have introduced a “Pile Metaphor” model which using social navigation idea to solve this specific issue.
The design of “Pile Metaphor” model focuses on two major parts. First of all, users can be shown that how other users in this system decide which files are shared, which are not. And such information will be shown directly in the appearance of the folders, that is to say, different folder appearances indicate different sharing levels. Based on this straightforward design, users can easily know that whether their decision is appropriate or not. Secondly, the “Pile Metaphor” model also shows the extent that how many people in the whole system have read one user’s own file. This feature is achieved by showing the tidiness of the pile. For example, the more times a pile of file are read, the messier the pile is. Again, based on this direct information, users will reconsider which files can be shared continuously, and which should be set as invisible to the public.
There are two major advantages regarding the “Pile Metaphor” model. First, introducing this model to a system does not change the fundamental design of the system. This model is like a small plug-in, and will have significant influence on the users. Second, this model will not detract users from their work, because every security-related features will be shown directly on the user’s interface.
Implementation in human-robot interaction
One of the common methods people used in the field of social navigation is to construct proxemic, which can be connected with human-robot interaction. A study shows interests in different kinds of navigation behaviors humans expect from a robot in a path crossing scenario. The study focuses on two main questions of robot behavior. First, what is the definition of expected actions? Then, can spatial relationship help with the expected action? The result reveals that spatial relationship actually relates to the behavior, which leads to a possible prediction to the expected action.
Social navigation can be used in so many fields that most people can benefit from it, and also wants to join it to gain more benefits. However, as the saying goes, “every coin has two sides”, so does social navigation which also has some drawbacks that can be used by malicious users who are intended to mislead the public or obtain private information about specific person.
Researchers Meital Ben Sinai, Nimrod Partush, Shir Yadid and Eran Yahav from Israel Technion did some experiments in 2014 and wrote an article, “Exploiting Social Navigation”, to discuss about the results. According to the article, attackers can use plenty of machines to fake users’ behavior and fabricate information to mislead other real users. In this case, they attacked a real-time traffic software which allows users to report traffic news, and broadcasts these messages to others. These researchers used phony users to fabricate traffic information like obstruction or traffic jams and successfully let the system mislead real users with other itineraries. This can cause several problems, as the researchers mentioned. One problem is that real users would take more time and more money to go another longer way compared with the origin way which cost much less. What’s more, this attack may also lead people to some unsafe roads or even nonsexist ways, which causes security-related issues. To solve this shortage of social navigation, they encourage us to verify the users’ identification by checking real name with verification code, or checking users’ behavior with machine learning technologies.
The verification technique will lead to another problem of social navigation, information disclosure. In accordance with the article mentioned above, the four researchers discussed that malicious attackers may make use of the information of a specific user and gain plenty of private information of the user, such as the place he/she usually goes to, the route which he/she usually drives and so on. These information will also cause security-related issues, since attackers can use such information to track other people with vicious intention.
Recent trends and implementation in products
As popularity of social networks and social web grows, great deal of data can be collected through the footprints of users left behind as they interact within different social computing systems. This growth has led into more novel and diverse implementation of social navigation support, including in Education, Media, News, and Tour Guide Systems. Implementation of Social Navigation in shared 3D environment works in the similar way, as allows users to see trail and information of others who used to be in the same place before in the virtual world. This architecture has been evaluated through a prototype system, proving its performance and usability. Bosch improved real navigation systems for driving, used social navigation to reduce driving time on the road. The model even considered altruism and CO2 emission in a novel way, which has been evaluated in Bay area, achieving improvement of 10%.
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