Recent failures in developing new mobile services, such as WAP, call attention to the latter type of thinking. ... earlier in computer conferencing. Today ... structure of all networks in which they are present, hubs make them look like small worlds.
Designing People’s Interconnections in Mobile Social Networks Giuseppe Lugano, Jorma Kyppö and Pertti Saariluoma University of Jyväskylä, P.O.Box 35, (Agora), Jyväskylä, FINLAND Methodology of designing mobile social services is an important challenge for modern human computer interaction research. In this paper, we suggest a model for design. It is intended to present some essential components of designing mobile social services. The main phases shall be action oriented analysis of user goals and needs, generation of the social network and finally designing personal social space for potential users. Keywords: Mobile Social Network, Methodology, Human Technology.
1 INTRODUCTION The recent developments of information and communication technologies, the Internet especially, seem to have enforced the concept of “technological imperative”, according to which social progress follows technological progress. This technology based view is not universally accepted;. Checkland [9] and Mannermaa [23], for example argue that technical innovations always require a social innovation. It can also be suggested that the whole design process should begin with analyzing human actions [34]. Recent failures in developing new mobile services, such as WAP, call attention to the latter type of thinking. User-centred design opens a wide set of problems. One can investigate as Kaasinen [16] the main factors influencing of user acceptance, such as the perceived value of the service, perceived ease of use, trust and ease of adoption. One can also concentrate on testing prototypical and existent services [27]. However, it is also possible to pay attention to developing the optimal design thinking [34]. Here, we focus on the latter, designing mobile social services on the top of graph theoretical and psychological concepts. The conceptualization of this design process is very challenging, because it may on one hand mean top-down design requirements suggested by governments and companies or open design emerging from self-organizing mobile communities. Mobile applications designed for social networking are known in literature as “mobile social software” (Mososo). Instead of this term, we will use “mobile social service”, to emphasize the human perspective more than the technical one. The origins of mobile social services can be found in “social software”, which is simply a “kind of software that supports group interaction”, as stated by Clay Shirky, who used that expression for the first time in public in 2002. Of course, social software is connected to the previous developments in Computer Supported Collaborative Working (CSCW) and Learning (CSCL), and earlier in computer conferencing. Today, almost every piece of software can be used as social software, as there are always features that allow remote communication and collaboration. Here, by extending Shirky’s definition, we characterize mobile social services as “tools that support interaction among networked mobile users” [22]. Understanding social relationships in the mobile environment is important not only for offering a support to usual face-to-face interactions, but also to improve our possibilities of maintaining, augmenting the personal social network and controlling our personal information flow. Solving problems associated to designing mobile social service networks presupposes on one hand the use of classical mathematical graph theory and on the other social psychological concepts and theories [8]. The way these two theory languages can be integrated is a central problem in developing scientific basis of mobile social services.
2 CONCEPTUAL BACKGROUND 2.1
Network research, from the Random Model to Complex Systems
Being inherently a multidisciplinary discipline and having been studied from several perspectives, network research presents also a heterogeneous terminology for its basic concepts. According to Harary [14], a graph G consists of a finite nonempty set V=V(G) of p nodes together with a prescribed set X of q unordered pairs of distinct nodes of V. Each pair x={u,v} of nodes in X is an edge of G and x is said to join u and v. A network N is a graph with a numerical value, f(e), assigned to each edge e [15]. Paul Erdıs and Alfréd Rényi, in their time suggested that networks could be modeled by connecting their nodes with randomly placed links [13]. In such a model, called random graph model, each node can get connected to any other arbitrary node with some probability p. These networks look homogeneous since every node, in average, has the same number of links. The simplicity of their approach and the elegance of
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some of their related theorems revitalized graph theory, leading to the emergence of a field of mathematics that focuses on random networks [6]. In 1998, physicist Albert-Laszlo Barabasi and his colleagues, when trying to map the connectedness of the World Wide Web [3], found that most nodes are connected to a few others, but a few of them, called then hubs, are far more connected than other nodes. The general aspect of this phenomenon has been noticed in other and earlier contexts, such as the small world experiments [24], job-finding [13], and the development of organizational liaisons [4]. Later studies have found that hubs appear in most large complex networks that scientists have been able to study so far. Dominating the structure of all networks in which they are present, hubs make them look like small worlds. Indeed, with links to an unusually large number of nodes, hubs create short paths between any two nodes in the system [5]. Scientists have called networks that present this kind of network connectivity scale-free networks. Recently, the social network of mobile phone calls has been proved to be a scale-free network [2].
2.2
Sociometry, the “Art of Modeling Relationships”
The mathematical concepts alone are not sufficient to analyze how people act in mobile networks. It is also necessary to use social psychological knowledge. In particular, sociometical tradition provides suitable basis for such investigations. A good example is Moreno’s classic research in sociograms, graphical representations of group relations, where nodes indicate the subjects, edges the existence of a relation and the interconnection value giving an estimate of the intensity of relationship. In order to get a sociometric measure, a sociometric test was used, which could be based on several techniques, such as questionnaires, surveys and observations. Moreno’s original six requirements for a true sociometric test, defined in [25], can be adapted to the mobile environment as follows: 1. Limit of the group corresponds to the user’s address-book 2. User’s choice limited to the size of address-book, which can grow indefinitely 3. User makes a choice on the base of the matching criteria / feature of the mobile phone 4. User’s choices update the compatibility matrix 5. User’s choices are not displayed to other people and are stored only on the user’s phone 6. Questions are gauged to the level of understanding of each user As Lindzey observes in his review of sociometric studies [19], “relatively few studies in this area meet all requirements expressed by Moreno for the sociometric test”. As the sociogram can be seen as a snapshot of the social network, modeled in terms of nodes and edges, it can be studied using the methods of graph theory. One of the limits of sociometry consisted in the difficulty of drawing the sociogram when the network consisted of many nodes, making it difficult to discover properties or relations of the group. Today, using appropriate software this constraint has been overcome and it is even possible to study networks consisting of thousands of nodes, which can be studied using complex network theory. As we will see in the section 3.2, it is now possible also to collect data of mobile communications and inferring a social network by applying our adaptation of the traditional sociometric test.
3 A THREE-STEP MODEL OF DESIGNING MOBILE SOCIAL SERVICES We present in this section a three-step model which can be used to design mobile social services. Such model takes into account user needs and orientation (step 1), then applies social network analysis (step 2) and finally use the results as design recommendations for the mobile application (step 3). The method relies on the availability of a user profile and of a history of past conversations, expressed by communication logs.
3.1
Defining User Needs and Orientation
Utilization of Information and Communication Technologies (ICT) presupposes the analysis of the user needs [28,35]. When this analysis is based on psychological or social psychological methods concepts and theories, we can speak about user psychology [33,34]. In the first stage of developing a mobile social service, it is necessary to define accurately the action goals and needs motivating people to adopt some mobile social service. In the case of mobile social services, the communication need is the central [36]. Hence, knowledge of human actions, needs and goals involved in the communication process is crucial to develop mobile services. People communicate for some specific purpose. Services offer individuals a possibility to form communication networks with familiar and strangers. From the service designer’s perspective it is important to understand the goals of networking to be able to develop proper use scenarios [32]. An example of a concrete scenario might be the organization of the address-book to support awareness and group coordination [29]. Although individual user-device interaction is useful, the most typical kind of interaction is between the user and her social network. A typical scenario could be sending the same message to inform a group about an event, for example “Sunday between 13 and 14 we play football. Ten places
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available. Who’s interested?” Here, the goal is what Ling [20] consider the most central consequence of the mobile telephone: coordination of social interaction. In order to develop a mobile social service, we need to have a clear idea about the structure of social goals, the ways people want to reach them and the possible obstacles they present for developing mobile tools for mediated social interaction.
3.2
Generating the Social Network
Once we understood the reasons for people to communicate, thus connect in the mobile environment, we need to define some similarity metrics in order to be able to measure such social relationships. Although this may mean that we have to make a number of simplifying assumptions, it is necessary to be able to analyze the behavior of large communities using such services. In that case, it is no longer possible to use standard observational patterns, but we have to rely on mathematical analysis. For such analysis it is essential to make a homophily assumption. People’s homophily is the degree to which individuals in dyad are congruent or similar in certain attributes, such as demographic variables, beliefs and values. Defined by Lazarsfeld and Merton [18], the homophily theory states that most human communication will occur between a source and a receiver who are alike. Homophily implies that distance in terms of social characteristics translates into network distance, the number of relationships through which a piece of information must travel to connect two individuals. The initial network studies showed substantial homophily by demographic characteristics like age, sex, race/ethnicity and education [7,21] and by psychological characteristics like intelligence, attitudes, and aspirations [31]. In the context of mobile communication, homophily corresponds to people’s similarity in properties present in the user profile, such as demographic information, preferences or mobile usage patterns. From modern group theoretical perspective people in groups are not homophilous, but they have complicated role structures [8]. However, the classic assumption makes mathematical analysis of service user’s behavior easier. Thinking about the process, the first step consists in the selection of a viable similarity measure; once it has been decided, available data has to be mined to find connectivity patterns between all actors of the social system by using efficient algorithms. Previous studies of automatic modeling of the user’s social network have considered public datasets present in the web, such as conversations in IRC [26] and in newsgroups [10], or private ones, such as email history [11] or activity in a social networking site [1]. Making use of personal information, mobile social services present privacy concerns to users who do not want their data to be collected, analyzed and maybe traded for other purposes than the original ones. For this reason, to minimize privacy concerns we suggest storage and analysis of communication data on the user’s device. Obviously, the view of the social network from the perspective of the single user (egocentric) communication does not necessarily shows all the ties between actors of the social system, but nevertheless it serves to the individual needs of communication and sharing of information. Although virtually infinite measures of similarity can be suggested, here we introduce two of them: frequency of communication and similarity of mobile usage patterns. In both cases, the social network is derived by the compatibilty matrix, square matrix of the size of the user’s address-book, with users’ interconnections values ranging from 0 to 1. The interconnection value represents the strength of the relation, and a link exists only if the value is greater than zero. Of course, the access to some services might require not only the existence of a relation, but also its strength to be greater than a user-defined threshold. The first algorithm, called Frequency Algorithm, takes in input mobile communication logs relative to a defined period of time and fills the compatibility matrix M with output values. The interconnection value between two people A and B corresponds to the percentage of communication units that A reserves to B through the favourite communication channel. Only outgoing communication of A is considered to evaluate her relation to another participant B. Incoming communication from B is not relevant in this context, as it does not express anything on the relation seen from A’s perspective. For example, if a selling agent is calling us many times a day to promote a commercial offer, it does not imply that there should be a higher interconnection value from us to him. Incoming communication becomes very relevant if we aim at assessing the reciprocity of the relation, which could be studied applying the algorithm on both A and B and comparing the interconnection values measured from the two perspectives. The second algorithm, called Homophily Algorithm, is based on similarity of mobile usage patterns (i.e. time of communication, communication destination), preferences (i.e. music taste) or measurable sensor data (i.e. user location). Once a homophily parameter has been chosen, it presents a number of possible values. For example, communication destination could consist of three possible values: one might choose to call somebody in the same city (local call), in a different city, which is in the same country (national call) or abroad (international call). The interconnection value is assessed by correlating the similarity of user behavior on each possible value of the chosen parameter. The maximum interconnection value between two people would happen if both did the same choices for each value during the considered period of time.
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Once the interconnection values are obtained, user clustering is performed on the compatibility matrix, to obtain small groups, or communities, which will potentially benefit of a mobile social service. Traditional clustering methods are hierarchical; two are the basic approaches that can be taken in hierarchical clustering: agglomerative and divisive. In the former, one starts with the empty set and nodes are gradually aggregated into larger sets at each step of the method, according to their similarity or distance. After a certain number of steps, all nodes would eventually belong to the same cluster. It is up to the researcher to stop in a suitable step. Instead, the latter approach follows start with the graph as a whole, regarded as a single cluster, and at each step produces sub-sets of nodes, making the splitting choice on the base of having or not a certain similarity parameter. As in the agglomerative method, the researcher has to choose a suitable number of steps, as at a certain point the divisive approach would lead to a cluster for each node of the network. As Girvan and Newman point out, hierarchical clustering method, although useful, is far from perfect; a good answer to shortcomings of hierarchical clustering is a clustering method based on edge betweenness [12], which showed high sensitivity and reliability.
3.3
Social-Network Based Service Design
The third and final step takes care of the presentation of the application / service to the user. Every feature of the mobile phone (calendar, address-book, radio…) can be augmented with social networking concept. In some cases, it is applied at individual level, for personal information and time management, in others at group level, to improve interaction with existing contacts or even to offer opportunities to communicate and meet with “familiar strangers” [30]. As a whole, we can view the mobile social service as a “personal social space” made of three components: Me, My contacts and My Groups (or communities). The first component of the social space, “Me”, consists of the user’s needs and orientation. User performs personal information and time management takes place in the area which does not overlap with any other balloon. For example, if we consider the alarm clock, there is no need of sharing it with other contacts, as it seems strange to set it to wake some other people up in the morning or to check if they remembered to set it up. In many other cases, the area where the three components overlap is where the contents are shared.
Fig.1. Personal Social Space
Interaction with existing social network happens in the “Shared space”; this area consists of contacts of our social network who have invited us to join a group which we joined or who accepted our invitation. It is worth to note that our contacts not necessarily want to belong to any group, but still want to interact with us at personal level (interpersonal communication). Each area has its own features and reflects different kind of communication, which should be taken into account in the design process of the mobile social application. Considering a scenario for personal information management, we could consider the situation in which a mobile user cannot add a new contact to the address-book because the memory is full. This happens because we seldom decide to delete or archive numbers. However, considering our usage patterns, only a small number of our address-book is used very often, and some numbers are used very seldom. With the availability of large memories, that should not happen anymore, but maybe the possibility of adding an even higher number of contacts can make the management of the address-book even harder than nowadays. Incorporating social networking, this could lead the application to periodically suggest a list of numbers to archive, on the base of the user’s conversation history. When considering a scenario for group communication, we could imagine parents who would like to regularly share with their children living abroad comments, photos or audio/video clips. That could be seen as an internal mobile blog, open only to the group members. In the same way, an elderly person living alone could start a group which includes her neighbors. In case she does not perform a certain action for a period of time, this could trigger an emergency call alerting the other group members. Sensors could easily keep track of many parameters, such as health conditions, and communicate through local connectivity. Many other applications could benefit possibility of interact with strangers through the similarity algorithm; tourists which leave for a destination might join a group with the name of the city which are going to visit and send a message asking for help during their staying. Of course, one could say that many of the same goals can be achieved already with Internet social networking services or simply taking personal initiative in a face-to-
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face setting. We do not want mobile social software services to substitute any of the other channels of interaction, but add an effective alternative to them, taking advantage of the availability of a powerful computing environment in a mobile situation. REFERENCES [1] Adamic L., Buyukkokten O., Adar E., “A Social Network Caught in the Web”, FirstMonday, 8, 2003. [2] Aida M., Ishibashi K., Miwa H., Takano C., Kuribayashi S., “Structures of Human Relations and User-Dynamics Revealed by Traffic Data”, IEICE Trans. Inf.&Syst., vol.E87-D, n.6, 2004. [3] Albert R., Jeong H., Barabási A.-L., “The diameter of the World Wide Web”, Nature 401, 130-13, 1999. [4] Aldrich H., “Even Dwarfs Started Small: liabilities of age and size and their strategic implications”, Research in Organizational Behavior, 8, Academy of Management Review, 19, 1994, pp.645-670. 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