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Mobile Social Networking Applications

They deliver the right social service to the right user anytime, anyplace, without divulging personal data.
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  1. Introduction
  2. Key Insights
  3. Mobile Social Networks
  4. From PC- to Mobile-based Environment
  5. Architectural Considerations
  6. Classifying MSN Applications
  7. Trends, Challenges, Opportunities
  8. Conclusion
  9. Acknowledgments
  10. References
  11. Authors
  12. Figures
  13. Tables
mobile social media

Recent advances in mobile computing, hardware, and software empower end users worldwide through a range of mobile devices (such as smartphones and tablets) with improved and novel capabilities (such as localization through the Global Positioning System, context awareness through sensors, Internet access through cellular networks, and short-range communications through Wi-Fi networks). The result is intense competition among providers of online social services for mobile users regardless of location and profile, along with numerous mobile social networking (MSN) applications in which billions of people use their mobile devices to tap a spectrum of instant, relevant, high-quality services (such as interaction with peers with similar interests, sharing information, and creating virtual communities).

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Key Insights

  • Along with PC functions, TV, games, and business services are available through mobile devices, wherever and whenever a user might want them.
  • Mobile devices are readily discoverable by nearby people and social services.
  • Even more services are expected soon, along with numerous challenges and questions about privacy and data security.

Like online social networking sites (OSNS), MSN applications are social structures consisting of individuals or organizations connected through specific types of interdependency (such as friendship, kinship, common interest, financial exchange, and beliefs). They are based on a variety of architectures depending on whether they are extensions of existing OSNS, designed purely for mobile devices, focused on mobile users, or data- or service-oriented. Services available to mobile users follow several trends, including social gaming, business, and media. To better understand the state of MSN applications, we reviewed their architectures, trends, and impact over the past few years, motivated by the lack of previous studies surveying MSN applications in both the business sector and the research community. We were also motivated by our strong interest in understanding how MSN applications provide services to the right users at the right time through communication and context-aware technologies. One of our major contributions is proposed improvements that can be incorporated into existing MSN services, enabling seamless migration from PC-based environments to mobile environments. In addition, we also provide broad insight into state-of-the-art MSN applications, identifying strengths and weaknesses, classifications, and a new proposed classification under which a non-exhaustive set of MSN applications can be identified.

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Mobile Social Networks

Several surveys previously revealed the dramatic increase in popularity of MSN applications. Indeed, a 2011 survey found 53% of mobile users in North America used these applications.8 Another survey found nearly 40% (almost 300 million people worldwide) of those accessing monthly social networks from their mobile devices are Facebook users.23 The analytics firm ComScore3 reported the number of people accessing social networks from their phones in France, Germany, Italy, Spain, and U.K. grew by 44% in 2011, reaching 55 million users in September 2011 in these five countries. ComScore also reported that Twitter and LinkedIn more than doubled their numbers of users in the same year. Meanwhile, Warren25 concluded that the number of mobile subscribers accessing Facebook and Twitter increased by 112% and 347% from January 2009 to January 2010, respectively, while the number decreased by 7% for MySpace in the same period (see Table 1).

The increasing popularity of MSN applications is notably due to new, interesting services and ways of engaging in social interaction and collaboration through mobile devices. Indeed, in addition to locating and alerting users about friends and communities, users can also use location-based services (such as to recommend nearby commercial offers) and data-sharing services (such as photos). Some services have been extended from PC-based social networking sites to be available almost everywhere, anytime, for mobile devices following their location- and proximity-aware facilities (see Figure 1).


In addition to user profiles, MSN applications should address users’ emotional states, as well as the focus of their attention.


Several MSN initiatives have been proposed; for example, Smith21 presented the Reno mobile-phone application in which users query one another and exchange location information in response to other queries or even when unprompted. In Reno, mobile devices are classified into types and matched to specific types of queries. The Reality Mining project6 demonstrates the ability of Bluetooth-enabled mobile phones to measure information access in different contexts, recognize social patterns in routine user activity, infer relationships, and identify socially significant locations. The CenceMe system15 collects users’ status or context information through mobile sensors, exporting it automatically to social networks. Serendipity5 uses Bluetooth to find nearby devices and a central server to match profiles for either a professional introduction or for more personal reasons (such as dating). Nicolai et al.16 proposed an application that relies on neighboring-device discovery to sense and visualize the surrounding social network on mobile devices. MobiClique18 is a mobile ad hoc network in which Bluetooth-enabled mobile devices communicate directly with other devices as they meet opportunistically. The CityWare system described in Kostakos and Neill10 is built on a similar idea but passes proximity information to an online social-networking application that aggregates and sends statistics about users’ surroundings.

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From PC- to Mobile-based Environment

The technological progress in mobile devices, communication facilities, and context-aware capabilities is the major driver behind the shift among social-networking sites from Web-based to hybrid to pure mobile applications. Mobile applications attract the attention of mobile users who want social services anytime, anywhere, eliminating the need for desktop PCs. In contrast to PC-based social-networking sites, important features for users in mobile social services include immediacy, relevance, brevity, and retrieval.7 Immediacy means users get answers to their questions or report on important events on the fly. Relevance means MSN applications use location-aware devices to send queries and messages to people within a defined geographic area, enabling groups of users to share an experience virtually. Brevity means short messages delivered through mobile devices are easier for others to understand and respond to. And retrieval means conversations are archived and retrieved later by participants or others, creating a kind of real-time archive of social interactions.

To reap these benefits, MSNs are adapting the way they provide their services, especially by minimizing explicit user intervention while aiming to deliver the right content to the right user at the right time. To this end, in addition to providing similar PC-based social-networking services, MSNs must be able to capture optimistically relevant contextual features in users’ surroundings.13 Such features include location-related, user-related, device-related, interaction-related, and spatio-temporal-related attributes.

Location-related attributes. Several location-related attributes may be relevant to MSN applications, depending on the service to be provided to the user, including type of location (such as public space, restaurant, and classroom) and neighboring objects of interest at that location (such as friends and others with similar profiles and landmarks recommended by friends). To capture the characteristics related to user location, MSN applications can benefit from such technologies as GPS, sensors, radio frequency identification (RFID), and near-field communication (NFC) chips in smartphones to establish radio communication by touching them together or even by just bringing them into close proximity. Several commercial MSN providers, including Aka-Aki, BrightKite, Dodgeball, Mobiluck, and Plazes, use location-aware devices. Several research efforts have also proposed MSN applications based on GPS, including Marmasse,14 and/or proximity sensing, including Eagle and Pentland,6 Kostakos and Neill,10 Miluzzo et al.,15 and Nicolai et al.16 CenceMe16 is an example of a system that takes inputs from a broad set of sensors, automatically learns from each user’s history of digital behavior, and outputs status information much richer than current location and communication preference. CenceMe’s ability to learn is important for MSN applications in which implicit information is inferred from GPS and sensor data to recommend nearby spatial objects (such as landmarks) likely to be of interest to the user. Likewise, an MSN application should be able to identify nearby people who might be of interest to the user (such as friends or friends of friends within the same geographical area).

User-related attributes. As the user is the focus of MSNs, several commercial applications deliver services based on personal profiles; for example, Loopt, Mobiluck, Playtxt, and Proxidating all basically compare user profiles and preferences, sending an alert when a positive match is confirmed. In addition to user profiles, MSN applications should address users’ emotional states, as well as the focus of their attention. Also helpful is for them to identify and automatically update users’ status (such as participation in sports and work or relaxing at home).9 To the best of our knowledge, commercial MSN applications do not yet support such computationally complex issues. Among researchers, inference of user activity is addressed through various approaches; for example, in SenSay,20 a smartphone prototype takes advantage of user context to improve usability, so if the user is busy and wishes to not be disturbed, the smartphone can answer/reply automatically through a short message service. Marmasse14 developed a system that uses GPS data, accelerometer data to distinguish between walking and driving, and a microphone to distinguish between talking and silence.

Device-related attributes. Mobile devices are characterized by processing, memory, sensing, and battery capability, as well as display screen and compatibility with existing technology. These parameters are being improved on such devices as smartphones and tablets; for example, smartphones, which may contain a large number of sensors and integrated devices, are being upgraded into powerful computing platforms. However, such progress is not always accompanied by corresponding progress in MSN services. Indeed, existing MSN applications are not always available on all platforms and devices; for example, Jambo and Toothing are available for cellphones and PDAs, Whrrl can be downloaded onto the BlackBerry Pearl, Curve, and Nokia N95 smartphones, and Friendster is optimized for Android, iOS, and Windows Phone 7 smartphones with screens larger than 3.5 inches. For convenience, MSN providers support services that comply with current standards, as compliance yields better interoperability, particularly among mobile devices supported by different technologies. Compliance also yields enhanced games and social-media services because many of them have specific processing and display requirements.

Spatio-temporal-related attributes. Spatio-temporal events are important features of context awareness. Events like day, night, and rain have different effects on users, as well on the services provided to them through mobile devices. Indeed, users could be disturbed by nearby events (such as heavy rain or loud sounds like thunder) so their moods might influence the service they are looking for and the way they interact with others through MSN applications. Despite the importance of these effects, few reported research efforts (such as Liaquat et al.11) address spatio-temporal attributes and their effect on MSN applications. Such efforts have focused on the behavior of mobile users while using their devices over various time periods; they have also studied temporal social communications where different centrality measures (such as proximity) can help determine optimal ways to disseminate information within social networks. Further work is needed to capture and analyze spatio-temporal events and their effects on MSN services. Sensors may help, as they are promising tools for data acquisition and for capturing patterns of user behavior during spatio-temporal events.

Social-interaction-related attributes. MSNs offer a novel user-interaction paradigm combining the benefits of PC-based social networks and mobile-computing devices.22 This interaction might be achieved by sending and receiving text messages (such as SMS), multimedia messaging service, or MMS, and/or email. As these facilities are not necessarily available on mobile devices, interaction among devices is not always straightforward. In commercial MSNs, device interaction is achieved basically through Bluetooth (such as in Aka-Aki), Wi-Fi connectivity (such as in Jambo), and mobile Internet connections (such as in Facebook and Twitter). Among researchers, BlueFriend22 takes advantage of mobile devices and Bluetooth technology to scan the environment for members of the BlueFriend community. If found, virtual personal cards (with user profiles and preferences) are exchanged to assess the degree of matching among nearby users.

Serendipity5 combines the existing communication infrastructure with online-introduction-system functionality to facilitate interaction between physically close people through a centralized server. It repeatedly scans for Bluetooth devices, transmitting the discovered devices to a server that calculates a similarity score between any two proximate users. When this score reaches a predefined threshold, the server alerts both users, sending them information that might include pictures, news of mutual interest, and talking points.

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Architectural Considerations

MSN follows three different types of architecture: centralized (such as Facebook and Twitter), peer-to-peer, or P2P (such as BlackBerry Messenger, Lovegety, and Proxidating), and hybrid (such as Jambo). Centralized architectures allow users to access multiple services by interacting with remote MSN servers through their mobile devices, freeing the devices from overly demanding processing load and extending battery lifetimes. P2P architectures allow users to interact directly through specific software tools and hardware facilities (such as Bluetooth and Wi-Fi) on their devices. In addition to sharing similar contexts, users may also meet face to face when in neighboring locations. Combining centralized and P2P architectures, hybrid architectures allow users to interact through their mobile devices while accessing services from remote MSN servers.

Chang et al.2 proposed a centralized architecture consisting of four main components: client devices, wireless-access network, the Internet and its hosts, and the server-side, including database- and application-specific servers. With a Web-service technology, the server can query a location module installed on the client device. The wireless-access network serves as TCP/IP pipes to allow the client and the server to communicate. The Internet component consists of third-party application servers (such as MapServer, the Simple Mail Transfer Protocol mail server, and Voice over IP).

Rana et al.19 proposed a service-oriented architecture with three main layers: service integrator, back-end services, and mobile client. The service integrator integrates mobile-device software and back-end services (such as for location tracking) through a standard interface. The back-end service layer is responsible for collecting Web data through special application programming interfaces (APIs) that set up connections between social networks and data-collector services. The service integrator ensures the interoperability of mobile clients with services. The mobile client allows client applications (such as Android and iOS) to access available services.


Combining centralized and P2P architectures, hybrid architectures allow users to interact through their mobile devices while accessing services from remote MSN servers.


Johansson9 presented an architecture in which mobile devices (with audio, Bluetooth, and GPS) collect and process contextual information to assess its importance; data processing is handled by an MSN engine on the mobile device. The processed data is then used as input to the MSN.

Mani et al.12 developed a software prototype that supports P2P spontaneous social networking through fast setup and deployment of a distributed social network that supports several services, including community creation, instant messaging, and VoIP.

Regardless of the type of architecture and number of layers or modules it includes, an MSN architecture must support several requirements: context awareness, acquiring and analyzing contextual data collected from Bluetooth, GPS, sensors, and other technologies; client/server and P2P communication, enabling mobile devices to communicate with each other, as well as with the server’s back-end, to receive requested services; and services allowing generation of requested services and updating of related data. Hybrid architectures are best for addressing such requirements.

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Classifying MSN Applications

We have identified four broad categories of MSN applications in the literature:

Pure and hybrid. Tong24 classified MSN applications as pure and hybrid. Pure MSN applications are designed for mobile devices; hybrid MSN applications are designed for Web-based platforms but have been extended to mobile platforms;

Discovery. Pietiläinen18 proposed a categorization including three types of applications: proximity-based, check-in-based, and participatory sensing. Proximity-based MSN (such as Eagle,5 Kostakos and Neill,10 and Nicolai et al.16) uses devices that allow discovery of nearby devices. Related device information is useful for matching profiles on a central server,5 visualizing the surrounding social network on a mobile device,16 and displaying collected statistics of encounters with other users.10 Similar applications, including LoveGety and Nokia Sensor, have also been deployed in commercial settings that use Bluetooth to discover potential mates. In check-in-based MSN (such as BrightKite, Foursquare, Gowalla, Loopt, Mobiluck, and Whrrl), users constantly notify centralized Web servers of their current location and status. Mobile devices are just a way to update and consume available services. In participatory sensing,15 mobile devices collaboratively collect data from sensors (such as accelerometers, cameras, and GPS). Data is typically stored on central servers that provide aggregated reports of the data through a Web-based interface;

Major features. O’Sullivan17 proposed a classification system including six groups based on dominant features. In the texter group, the service focuses on sending short, text-based messages to a group of people simultaneously. In the radar group, the service knows the locations of users and their friends; applications allow users to check for nearby friends and/or receive notification if friends are nearby. In the geotagger group, MSN applications allow users to tag locations with images and information on a world map. Users may tag places for shopping, dining, or other activities, sharing the tags with friends. In the dating group, applications are identical to their online counterparts in which users create profiles for helping identify one another. In the social-networker group, applications aim to be like online social-networking platforms. And in the media-share group, applications share media files with groups of people; and


Current information-exchange models provide little protection for user privacy; for example, Facebook requires users allow access to their personal information and associate that information with their identities.


Push and pull. Rana et al.19 divided MSN applications into push-and-pull categories according to how data is acquired. Pull applications collect real-time information (such as micro-blogs and status) from various social networks using social APIs. Push applications are able to publish users’ contextual information collected by sensors to various mobile networks.

Proposed classifications for MSN applications. Though Tong’s proposed classification system24 focuses on the nature of MSN applications by dividing them into pure and hybrid categories, the classification does not account for interaction between the application and the user (ultimately the mobile device), as highlighted in Pietiläinen’s classification system.18 Neither classification emphasizes the services provided by MSN applications. The issue of services is the basis of the proposed grouping in O’Sullivan.17 This grouping does not seem accurate in light of the overlap between some groups that combine location-based services, messaging, media sharing, and geotagging. Rana et al.’s proposal19 is restrictive because it focuses on acquisition of data from user devices, as well from other social networks. Consequently, these proposals are inadequate for classifying MSN applications. We therefore propose to group MSN applications according to their categories, audience, usage, and interaction approaches (see Figure 2). The category group classifies these applications into pure and hybrid, as in Tong.24 The audience group classifies MSN applications with respect to whether they accommodate individuals of all interests and backgrounds or have a niche focus, catering to specific groups of people. The usage group classifies MSN applications according to their purpose, which could be informational (such as informing communities of news and promotions and addressing everyday problems), professional (such as job seeking), educational (such as collaboration with fellow students), dating, multimedia and content sharing, and social connections (such as being in touch with friends). The interaction-approach group includes the three subclasses proposed by Pietiläinen,18 proximity-based, check-in-based, and participatory sensing. Table 2 includes a partial list of MSN applications based on the classification in Figure 2.

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Trends, Challenges, Opportunities

MSN users constantly search for ways to interact, engage, and share information while on the move through mobile devices (such as smartphones and tablets). Some newer devices support fourth-generation communication technologies, motivating vendors to provide services on a range of platforms, including Android, BlackBerry, iOS, and Windows 8. In addition to hardware improvement, application developers are moving toward mobile advertising, TV, and social gaming, as well as toward new services (such as mobile wallets), mobile commerce, and cloud-based services. These services are enticing research topics.

Emerging cloud-computing platforms (such as Amazon Web Services, Google App Engine, and Salesforce) can be coupled with mobile devices and MSN applications to create mobile social-cloud ecosystems in which MSN applications improve the user’s experience and productivity, with cloud computing providing a robust, scalable, low-maintenance infrastructure. The mobile social cloud is driven in part by recent dramatic performance improvement in the IT infrastructure, together with innovations related to cloud computing (such as distributed computing, multicore processors, service-oriented architectures, and virtualization).

Meeting MSN-application-user expectations involves several challenges: One is performance, especially when users expect the same level of service on their mobile devices they enjoy on the desktop. Performance depends on available bandwidth of current cellular networks, though it does not effectively support increased video-content exchange and delivery. For better performance, MSN applications must be able to cope with integration and standardization; social-networking stakeholders compete in the mobile-services market by proposing different proprietary solutions that do not find widespread acceptance due to integration difficulties. Manufacturers, designers, and developers must all agree on open solutions based on standards to address heterogeneity and interoperability of different hardware and software technologies.

Moreover, MSN application users meeting opportunistically through proximity-aware devices must be able to address challenges involved in maintaining efficient communications between mobile devices; for example, future wireless technologies (such as Bluetooth v3.0, low-power Wi-Fi, and Wi-Fi Direct) may someday support more-efficient opportunistic communications. Indeed, Bluetooth v3.0 includes native support for alternative physical layers to increase capacity while delivering low power consumption. Wi-Fi Direct promises to automate ad hoc device-to-device communications through 802.11.

Despite these advances, developers must still guarantee efficient opportunistic communications, as new protocols and mechanisms are needed for the detection and control of temporal communities resulting from opportunistic communications. In addition to maintaining user privacy in these communities, researchers must begin to address how to aggregate the communities, looking for patterns in their creation and maintenance and improving content dissemination and resource sharing.

Another research challenge concerns design and implementation of adaptive discovery of friends or people sharing the same interests. Adaptation may help minimize the energy consumption of mobile devices, supporting dynamic changes in context and benefiting from historical information. Novel mechanisms may some day support prediction of friends and identification of those nearby who share the same interests. Indeed, user A frequently detects friend B nearby but not friend C due to the limitations of proximity-aware devices. However, if friend B is able to detect friend C, then friend B is able to notify friend A that friend C is not far away and could be expected to appear soon. A can then be prepared to be in touch with C or alternatively might leave to avoid contact with C.

Personalization is another MSN challenge, with users requesting services with easy-to-use interfaces and the ability to match profiles, backgrounds, and contexts. It may be partially solved through regional languages in order to promote strong penetration of MSN applications; for example, Facebook recently launched a mobile application in multiple Indian regional languages, including Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Punjabi, and Tamil. Since personalization requires knowledge of personal information, security and privacy is a serious challenge for developers of all MSN applications.

These applications must prevent data misuse or breaches of confidentiality, especially with businesses finding mobile phones to be at the core of their interest in collecting data and disseminating products. Current information-exchange models provide little protection for user privacy; for example, Facebook requires users allow access to their personal information and associate that information with their identities. In other systems using Bluetooth, nearby individuals can snoop other users’ data sent openly through wireless connections. Access to user data makes it easy for malicious users to spoof and inject traffic into MSNs.1 Knowing such attacks are possible, users lose trust in their service providers and fellow users, as they may not be the people they claim to be. In such instances, users are reluctant to disclose personal information, including identity and location. Using a trusted central server that collects information from individual users, computing and disseminating proximity results on demand, may guarantee privacy and security in MSN applications. However, such a solution would be ineffective unless dedicated servers are deployed locally to support MSNs.4

Several recent projects offer solutions to privacy and security issues in MSN applications; for example, SocialAware1 allows interaction of social-network information with real-world location-based services without compromising user privacy and security. Interaction is based on encrypted identifiers associated with a verified user location. The system then allows location-based services to query the local area for social-network information without disclosing personal user data.1 Moreover, Dong et al.4 developed techniques and protocols for computing social proximity between any two users looking to discover potential friends. To prevent malicious hacker attacks (such as falsifying proximity) during exchange of attributes of the two users for whom the proximity is calculated, the authors developed a proximity-pre-filtering protocol to determine whether the proximity between users exceeds a given threshold. To protect privacy, the protocol ensures the initiator can know only the comparison result between the estimated proximity and the threshold. To defend against attacks, Dong et al.4 developed a secure protocol based on homomorphic cryptography consisting of three major components: authentication without long-term linkability; efficient and privacy-preserving proximity pre-filtering; and private, verifiable proximity computation. Dong et al. also developed another protocol that leverages both homomorphic cryptography and obfuscation.

Although a number of initiatives have been proposed for user privacy, users and service providers alike need more efficient, scalable mechanisms for future MSN applications. Current policies in MSN applications should be personalized. Researchers may thus propose and implement new models for privacy and trust where user context, goals, profile, and cultural background are included. These models should also give users greater control over their personal data (see Figure 3). Ensuring privacy and security are related to services and applications, technology, and user/system/application interfaces, as an integral part of secure personal communication.

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Conclusion

Mobile devices have spurred explosive growth and deployment of services delivered through MSN applications. In order to deliver the appropriate service to the right user at the right time, they exploit context awareness and emerging communication technologies. However, this goal is not yet completely achievable, so additional effort is needed to capture and analyze contextual features to allow smooth migration of services from PC-based environments to a mobile environments. We have surveyed current commercial MSN applications, analyzing many proposed MSN architectures. We also proposed a better way to classify MSN applications, summarizing related trends and challenges. A hybrid architecture involving P2P communications assisted by online servers is the most flexible. Providers are willing to offer the right services through the right technology and appropriate interfaces, but constraints involving mobile devices and dynamic contexts must still be addressed.

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Acknowledgments

We thank the anonymous reviewers for suggestions and comments that helped us improve the content, quality, and presentation of this article. We also thank Moshe Vardi for his kind encouragement, time, and support throughout its preparation.

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Figures

F1 Figure 1. MSN services and their providers; provider key: N: network; A: application; S: service; Y: system.

F2 Figure 2. MSN applications classifications.

F3 Figure 3. MSN challenges, trends, opportunities.

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Tables

T1 Table 1. Number of mobile subscribers accessing specific social networking sites via mobile browsers in 2010; source: C. Warren25

T2 Table 2. Classifying MSN applications.

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