Chin@mpton

Part 3: Issues surrounding the user of MSN or LBSN

 

Abstract

Social network information is now being used in ways for which it may have not been originally intended. In particular, increased use of Smartphone’s capable of running applications which access social network information enable applications to be aware of a user’s location and preferences. However, current models for exchange of this information require users to compromise their privacy and security [20].

Introduction

As the mobile social networking arena evolves, challenges clearly remain. Although mobile devices allow people to stay connected to the community, limitations such as small screens on mobile devices, poor connectivity and issues of privacy and security will continue to be of concern to users. New advances in technology both on the infrastructure side and the device side are needed in order for this segment to move forward [5].

Recent advances on capable mobile devices and social networking applications (OSNAs) are quickly converging, accelerating the transition of pervasive computing from vision to reality. The open mobile platforms, particularly Apple iPhone and Google Android, make it much easier than before for developers to build third-party applications that may potentially used by millions of people on their always-on always-carried mobile devices. While Google Android is yet to be released, Apple iPhone has already claimed six millions of users and expects to sell more than 24 million units in 2009 [21]. On the other front, OSNAs, such as Facebook and MySpace, have become extremely popular in the past several years. For example, Facebook had 123.9 million unique visitors in May, 2008 [22]. Given the availability of open mobile platforms, it is only natural to expect that people will increasingly use OSNAs on their cell phones. In particular, iPhone has unique multi-touch interface, geo-localization capability, and embedded sensors, which may well boost user experience of MSNAs. As location can be used to find and interact with nearby events, business, and friends, privacy concerns remain as a significant design challenge for MSNAs. There have been several user studies on privacy issues of location disclosure [23, 24, 25, 26, 27] and several guidelines on protecting privacy have been proposed [28, 29, 30, 31, 32, 33]. It is, however, unclear how real world applications, particularly MSNAs that leverage location, have implemented privacy protections

Main Issues

Studies have been conducted by Sarah Jean Fusco and his colleagues presenting that there are 6 categories of issues surrounding the use of MSN or LBSN, which is showed in Figure 1[34].

Figure 1: Issues Surrounding MSN or LBSN [34]

And the key issue privacy, which we will analyze in this paper.

According to Merriam Webster Dictionary, privacy is 1) The quality or state of being apart from company or observation, 2) Seclusion, freedom from unauthorized intrusion <one’s right to privacy> and 3) The state of privacy is simply described as a place of seclusion. Upon reading this, the definition sounds clear and concise. Unfortunately, though the traditional meaning of privacy is changing as we apply it to our use of new media. Privacy pertaining to Social Networking Sites is defined in this context as personal information that an individual thinks is important and not accessible by the general population. Personal information includes a person’s name, address, e-mail address, online user name, telephone number, social security number, and any other background information with which that person could be identified. Privacy also involves the individual’s right to control the distribution of personal information. Having the power to control the sharing of information and how it will be used is an individual’s right to privacy [35].

  MSN or LBSN among other technologies of new media not only have a positive impact on society but a negative or dark side as well. One of the main issues is the user’s privacy. According to “Why Web 2.0 will end your privacy,” Will Harris emphasizes the reason why social networks and sites such as MySpace are worth the price they are in the market are because of data or information [36]. More specifically it is our data and information from all of the social networking sites’ users. Everything we have ever uploaded and details about us are with them as well. This type of information is useful for the market as companies would be interested in statistically, what we are interested in and target specific locations, age groups or networks.

In one hand we are benefited from the social networking sites as we are able to keep in touch with our family and friends. On the other hand we need to be very careful when we use this site because we are risking our privacy. Almost all sites that offer free services, online networking or shopping, keep records of our activities and amalgamates it with data from online tracking sources and sell it to someone. Most likely they sell our information to the companies who need our information for advertisement of their products. There are no U.S. Federal statutes that protect online privacy. There is no effective control over the use of personal information by online surveillance.

As social networking and new media are enhancing, it shifts our social values. The usage of online credit cards are raising as convenience is important in society. But the same time, we face the loss of personal privacy which may include medical and financial details. According to Intelligence Factory observers, true anonymity is no longer possible. With today’s society moving in a faster pace than ever, features such as same day delivery and instant meals will only cause future generations to expect faster and better service.

In the near future the market and society will overall continue to advance or become enhanced through social networking and technologies of new media. And as social networking websites attempt to protect users privacy in steps, securities will further enhance as social networking continues to become part of our daily lives.

Methodologies to protect your privacy

Securing your personal information on social networking sites isn’t only a matter of privacy. It’s also an important step in preventing identity theft. If you share personal information online, you make it easier for identity thieves to make off with your life story (and credit cards, and social security number, etc.) without a second thought. Victims of identity theft can suffer significant financial losses, and can spend years working to “clear their name.”

Users of social networking sites and online communities should be aware of the legal implications of publicly posted information and ways to protect their privacy online. Here are some suggested ways to protect your privacy on social networking sites:

• Use Social Networking Sites wisely. Understand which information you should and shouldn’t share and how you can actively set limits (privacy settings) on the information you share. Each social networking sites takes a slightly a different approach to sharing your information.

• Be aware that information posted on blogs, Twitter, Facebook, MySpace, and other online networking places is public. Think twice before pressing “Publish” or “Share.” Avoid sharing personal information such as phone numbers and addresses online. Change passwords every thirty to sixty days, and don’t use passwords that are easy to guess (such as variations of loved one’s names) [37].

• Be proactive in finding and using the controls these sites provide to protect your personal information and reduce your exposure to identity theft. According to McAfee, users should be weary of the links they click on Facebook. It might look like a Farmville invite, but it may contain malicious third party applications link [38].

According to the Myspace Safety a user should follow these rules [39]:

•Children are the most likely to post valuable information on the website without realizing that it is potentially placing them in danger. Young children may also not be as perceptive to a phony profile on a website as someone who is older. Keeping the computer in a shared area allows others to know that they are being watched and therefore helps keep them out of danger from saying inappropriate things to strangers.

•It is recommended that the user always have some sort of firewall or anti-spamming program installed. People are able to embed viruses into their links which puts the visitor at risk of becoming infected.

•It is advised to all members that they should display caution when choosing on whether or not to have contact with a social networking member rather than keeping the relationship strictly virtual.

•A user is also advised to never post their address, phone number, or actual name on their profile as it makes it easier for them to become a victim. •Do not upload private photos onto social networking site. Terms of use on sites such as Facebook state that all photos belong to them once uploaded to their site.

References:

[5] D. Z. Nina, M. Bala, “An Exploration on Mobile Social Networking: Dodgeball as a Case in Point,” icmb, pp.21, 2006 International Conference on Mobile Business, 2006

[20] B. Aaron, G. Mike, H. Richard, “Solutions to Security and Privacy Issues in Mobile Social Networking,” cse, vol. 4, pp.1036-1042, 2009 International Conference on Computational Science and Engineering, 2009

[21] D. Frommer. “Apple’s iPhone 3G is the new iPod, sales to triple”. Silicon Alley Insider, June 2008.

[22] S. Olsen. “Facebook’s Sandberg: Growth before monetization”. News.com, July 2008

[23] L. Barkhuus and A. Dey. “Location-based services for mobile telephony: a study of users’ privacy concerns”. In Proceedings of the 9TH IFIP TC13 International Conference on Human-Computer Interaction, July 2003.

[24] I. E. Smith, et al. “Social Disclosure of Place: From Location Technology to Communication Practices”. In Proceedings of the Third International Conference on Pervasive

Computing, Munich, Germany, May 2005.

[25] S. Consolvo, et al.  “Location disclosure to social relations: why, when, & what people want to share”. In Proceedings of the 2005 ACM Conference on Human Factors in Computing Systems, pages 81–90, Oregon, PL, Apr. 2005.

[26] A. Khalil and K. Connelly, “Context-aware telephony: Privacy preferences and sharing patterns”. In Proceedings of the 20th Conference on Computer Supported Cooperative Work, pages 469–478, 2006.

[27] S. Lederer, et al., “Who wants to know what when, Privacy preference determinants in ubiquitous computing”, In Proceedings of the Conference on Human Factors in Computing Systems, pages 724– 725, Ft. Lauderdale, FL, 2003.

[28] V. Bellotti and A. Sellen, “Design for privacy in ubiquitous computing environments”, In Proceedings of the Third Conference on European Conference on Computer-Supported Cooperative Work, pages 77–92, Milan, Italy, 1993.

[29] X. Jiang and J. A. Landay, “Modeling privacy control in context-aware systems”, IEEE Pervasive Computing, 1(3):59–63, 2002.

[30] J. I. Hong, et al. “Privacy risk models for designing privacy-sensitive ubiquitous computing systems”. In Proceedings of the 5th Conference on Designing Interactive Systems: Processes, Practices, Methods, and Techniques, pages 91–100, Cambridge, MA, 2004.

[31] X. Jiang, et al. “Approximate information flows: Socially-based modeling of privacy in ubiquitous computing”. In Proceedings of the International Conference on Ubiquitous Computing, pages 176– 193, G¨oteborg, Sweden, 2002.

[32] M. Langheinrich, “Privacy by design – principles of privacy-aware ubiquitous systems”. In Proceedings of the International Conference on Ubiquitous Computing, pages 273–291, Atlanta, GA, 2001

[33] S. Lederer, et al. “Personal privacy through understanding and action: Five pitfalls for designers”. Personal and Ubiquitous Computing, 8(6), Nov. 2004

[34] J.F Sarah, et al. “Exploring the Social Implications of Location Based Social Networking: An inquiry into the perceived positive and negative impacts of using LBSN between friends”, 2010 Ninth International Conference on Mobile Business / 2010 Ninth Global Mobility Roundtable, 2010

[35] M.D. Timm, and J. D. Carolyn. “Privacy and social networking sites”, New Directions for Student Services 124 (2008): 89-101. Academic Search Complete, EBSCO, 26 Apr. 2010.

[36] H. Will, (2006), “Why Web 2.0 will end your privacy”, [Online] Available: http://www.bit-tech.net/columns/2006/06/…

[37] N. Black, (2010). “Commentary: Social Media, geo-location and privacy, oh my! Daily Record”, The (Rochester, NY).

[38] Facebook, (n.d.), from Help Center, [Online] Available: http://www.facebook.com/help/?faq=19067

[39] MySpace, (n.d.), from MySpace Safety, [Online] Available: http://www.myspace-safety.org/

Part 2: LBSN (Location Based Social Networking)

Abstract

Location-based Social Networks (LSNs) allow users to see where their friends are, to search location-tagged content within their social graph, and to meet others nearby. The recent availability of open mobile platforms, such as iPhones and Android phones, makes LBSN much more accessible to mobile users [16]. Besides, because our project is about location based discount information sharing, more accuratedly, it is a location based social networking in order to share hype-local discount information. Therefore, it is vital for us to analyse LSBN.

Introduction

There exist several differences between the traditional and the mobile environment. Complex (and expensive) billing models in the mobile context ask for short connection times and low data-volumes—requirements that do not exist in the flat rate dominated world of landline Internet connection. These monetary obstacles, together with the restricted input and output capabilities prevent the implementation of many of the currently successful features such as multimedia sharing or blogging on mobile devices. However, these devices exhibit other characteristics that are of advantage in mobile social networking. We believe that two key features are the user’s permanent reachability and location awareness. The location aspect is well reflected by the buzzword P3 (people-to-people-to-geographic-place) that recently emerged in the field [9].

Analysis of related work

Dodgeball is one of the first LBSN services that rely heavily on SMS to allow users to “check in” their current location and to find their friends and friends-of-friends within 10 block radius [10]. On the other hand, Loopt leverages GPS and other signal triangulation technologies to automatically sense device location, without requiring manual location updates.

Brightkite is a Denver-based startup, founded in 2005, that allows users to share their location, to post notes, and to upload photos through a number of interfaces, including Web, SMS, and Email. Recently, the company has also released a native client application on Apple iPhone and is planning a version for Google Android phones. These native client applications, like Loopt clients, leverage GPS and other on device technologies for automatic location sensing, though still requiring users to hit “check in” button to update location. Brightkite allows users to define their friends and subscribe to their activity streams, including locations they checked in, their posted notes, and their uploaded photos. A note is limited to contain maximum 140 characters, for users to share quick thoughts and short status updates. The “friendship” relation is mutual: a user X accepting Y’s friend request means that X and Y becomes each other’s friend [11]. A user may choose to protect her activity stream so only her friends can see her location/note/photo updates. A user may discover nearby people and browse their public activity streams. The posted notes and the photos are all tagged with user’s most recent checked-in location. Once a user checked in at a location, she is assumed to stay at that place until she explicitly checked in at another location. This mechanism gives users a complete control on when and where to share their location, addressing some privacy issues when sharing sensitive location information. When sharing user’s current location, Brightkite allows users to control the “granularity.” Namely, users can check in at a country, a city, or a zip code, without specifying the exact address.

FourSquare, most recently has become one of the most popular LBSN applications, launched in 2008 which engages its users in a game competition. User’s “check-in” at venues in order to be awarded points which contribute to their chart position. This fosters the engagement of users, which are encouraged to check-in as many times as possible. Initially, FourSquare allowed users to check-in only from about 100 cities in the US and in Europe; they have only recently removed this limitation [12]. Furthermore, the service enables the creation of bidirectional friendship links. The website audience has recently grown steadily, reaching about 100,000 members at the end of 2009. We used a public API to retrieve user friend lists and home locations with geographic coordinates. The duration of the crawl was 7 days from November 22 to November 28, 2009 and it was seeded with 1,000 randomly selected user identifiers. Due to a limit on the number of API requests that could be issued, we retrieved a subset of the entire network which contains information about 58,424 users [13].

LiveJournal is a community of bloggers with around 14 million active users as the end of 2009 [13]. Users can keep a blog or a journal and establish friendship connections among them. Each user provides a personal profile which often includes home location, personal interests and a list of other bloggers considered as friends. Friendship links may not be reciprocal. There is a public API to explore the social network, but it does not expose any method to get user profiles, where location information may be obtained. Thus, the crawling process involved both crawling the social network links through the API and downloading the HTML profile pages of the visited users. Seed users were acquired by accessing the public timeline over 24 hours and then 1,000 users were randomly selected among all the users retrieved. The duration of the crawl was 9 days, from November 2 to November 9, 2009, obtaining a sample of 1,502,684 users. Given the 1,226,412 users which provide location information, Salvatore Scellato et al. successfully obtained a meaningful geographic location for only 992,886 users [13].

Twitter is a social networking service which allows users to send short messages known as tweets. Tweets are composed only of text, with a strict limit of 140 characters: they are displayed on the author’s profile page and delivered to the author’s subscribers, who are also known as followers. Since its launch in 2006 it has gained a global and vast audience of millions of users all around the world [14]. Twitter does not enforce reciprocity in social connections: a user may follow another one even though the latter is not following back. Hence, the resulting graph is directed. Another key characteristic is the presence of a heterogeneous network structure, where a user may have many more followers than the number of users he/she is following, or vice versa. Twitter provides a public API to gather details on user profiles and follower lists. Due to a rate limit on API requests, it was not possible to collect information about all the Twitter users. The crawling process was seeded collecting 1,000 seed users from the public timeline, which shows a list of the 20 most recent tweets posted by users with unrestricted privacy settings to the entire service. The duration of the data crawling was 6 days from December 3 to December 8, 2009, gathering information about profiles and follower lists for 814,902 different users. Among them, 535,653 reported some information about their home location. Salvatore Scellato et al. have successfully geo-coded 409,093 users, translating their location information into a point on the Earth [13].

Dataset	N	K	<k>	<C>	<L>	<Dij>	<lij>	<NL>	<GC>	ρ
BrightKite	54,190	213,668	7.88	0.181	4.71	5,683	2,041	0.82	0.165	1
FourSquare	58,424	351,216	12.02	0.256	4.60	4,312	1,296	0.85	0.237	1
LiveJournal	992,886	29,645,952	29.85	0.185	4.89	6,142	2,727	0.73/0.71	0.146	0.69
Twitter	409,093	182,986,353	447.29	0.207	2.77	6,087	5,117	0.57/0.49	0.108	0.79

Table 1: Properties of the datasets: number of nodes N and edges K, average node degree <k>, average clustering coefficient <C>, average shortest path length <L>, average distance between nodes <Dij> [km], average link length <lij> [km], average node locality <NL> (in/out), average geographic clustering coefficient <GC> and reciprocity ρ[15].

There are many reasons to use LBSN. For example, the most straightforward reasons for neighborhood interaction are place-based [19]. That residents are collocated in a shared locale necessitates some form of governance that a corporate office or onsite management usually enacts. Thus, one purpose of communication and interaction is the exchange of information between residents and management about rental payments, utilities, repairs, noise, and other issues that directly relate to the shared space residents co-inhabit.

It seems that tying one’s personal social network to real world activities is proving to be extremely valuable. ABI Research predicts that location based services will generate about $2.6 billion this year in revenue and more than $14 billion in 2014. Currently, two-thirds of all Smartphone owners’ check in with a location based app at least once a week. It is expected that LBSN services will attract 82 million subscribers by 2013 [17].In Europe, the number of users of location based services is expected to increase from 50 million in 2008 to 130 million in 2014 [18].

References:

[9] V.A. Marco, et al. “VENETA: Serverless Friend-of-Friend Detection in Mobile Social Networking,” wimob, pp.184-189, 2008 IEEE International Conference on Wireless & Mobile Computing, Networking & Communication, 2008

[10] H. Lee “Mobile social networks and social practice: A case study of dodgeball”. Journal of Computer-Mediated Communication, 13(1):341–360, October 2007.

[11] L. Nan, C. Guanling, “Analysis of a Location-Based Social Network,” cse, vol. 4, pp.263-270, 2009 International Conference on Computational Science and Engineering, 2009

[12] FOURSQUARE. Foursquare. Everywhere. [Online] Available: http://bit.ly/coJPSY.

[13] S. Salvatore, et al. 2010. Distance matters: geo-social metrics for online social networks. In Proceedings of the 3rd conference on Online social networks (WOSN’10). USENIX Association, Berkeley, CA, USA, 8-8

[14] RJMETRICS, New Data on Twitter Users and Engagement, [Online]. Available: http://bit.ly/9JSNCf.

[15] GARLASCHELLI, D., AND LOFFREDO, M. I. Patterns of link reciprocity in directed networks. Phys. Rev. Lett. 93, 26 (2004).

[16]C. Guanling and R. Faruq. Analyzing privacy designs of mobile social networking applications. In Proceedings of the IEEE/IFIP International Symposium on Trust, Security and Privacy for Pervasive Applications (TSP), pages 83–88, Shanghai, China, December 2008.

[17] “82 million location-based mobile social networking subscriptions by 2013”. ABI Research Market Analysis Report, November 2008.

[18] Kate Shaw, (2010) “Comprehensive Guide to Location-Based Social Media”, [Online], Available: http://www.thesearchagents.com/2010/06/c…

[19] F. Marcus, “Facilitating Social Networking in Inner-City Neighborhoods”, Computer, Sep. 2006, volume 39, Issue 9, pp 45-50

Part 1: MSN (Mobile Social Networking)

Abstract

With the evolution of the mobile platform and the rapid adoption of mobile devices such as cell phones and other handheld devices, social networks, which began as web-based applications, have migrated onto the mobile platform.

Introduction

In the mid-1990s, the Internet emerged as a robust technological platform and digital-based innovation began to develop. For the first time, technology was being used by firms in industries as diverse as media, healthcare, and financial services to create new products and reach new customers [1]. In addition to the impact of technological innovations on businesses, many people started to use the Internet as a means of communication with their friends, coworkers, and family members. While email served as the major vehicle for communication, social networks, which consisted of a variety of individuals who might be scattered geographically but who used the Internet as a conduit for discussion of common interests and ideas, began to emerge [2]. Initially, these networks were based solely on the World Wide Web and depended on the interaction among users who accessed various websites where dialogues could take place. However, with the advent of an increasingly stable mobile platform, the development of location based services and the rapid adoption of mobile devices such as cell phones, gaming machines and handheld computers by a wide variety of users, social networks have migrated onto the mobile platform.

The reasons why social networking goes mobile

Many industry analysts have yet to make forecasts for this nascent market, but early signs suggest there could be demand, particularly from teens and young adults. Already, 33.2% of 18- to 24-year-old Americans post photos to Web sites via mobile phones, according to mobile consultancy M: Metrics. By contrast, only 18.7% of these young adults play downloadable mobile games, one of the most successful forms of mobile content to date — and a $600 million market in the U.S. last year, according to consultancy IDC [7]. “This suggests to me there’s absolutely interest in participating in mobile social networks,” says Mark Donovan, an analyst at M: Metrics. Just how big could mobile social networking get? This application’s usage could become “as big as online social networking,” says Dennis Crowley, founder of wireless social network Dodgeball, owned by Google. About 45% of active Web users have been to online social networking sites, according to a recent study by Nielsen/NetRatings. As MySpace expands beyond its core market of teens and young adults, “We expect penetration of MySpace mobile to match penetration of cell phones,” which are owned by 80% of Americans, says Digiaro. Mobile access could become even more prevalent outside of the U.S., where in some cases more people use cell phones than personal computers to surf the Web [7].

Indeed, it’s the cell phone, rather than the personal computer, that’s the constant companion for today’s hip and socially networked. Why wait till you get home to log onto the PC to tell your 20 closest personal friends about your date? Teens can use a network-friendly cell phone to relay stories, pictures, and videos instantaneously. “You can use the mobile application in this two- or three-minute gap while waiting for a train,” says Kakul Srivastava, product manager for photo-sharing site Flickr, which is owned by Yahoo and allows for mobile picture posting. “People are out there, living their lives. They are not sitting in front of the computer [7].”

In addition to traditional print media companies that have developed content for the Internet platform, users themselves have begun to create their own content for the World Wide Web in the form of blogs for example, which they post on websites. On the mobile platform, individual creative talents such as artists and musicians are selling premium content such as ring tones and wallpapers to cell phone users. The commercialization of content developed by users constitutes a new stage in content development which will continue to evolve especially with regard to mobile social networks.

Instead, mobile social networks promote the positive aspects of interaction and add credence to the Porterian notion of clustering of individuals and businesses in geographical proximity in order to achieve economic success [3] as well as to the idea that groups of people using mobile social networks will find new ways of organizing and interacting, and in doing so, will in some way change the nature of the social order [4].

Candia, et al. [6] used mobile social network data to investigate aspects of human dynamics and social interactions. Such rich data is not interesting only for mathematicians, but also for social scientists who can have deep insights to consumer habits, attitudes and routines. These findings are relevant for interaction designers as they provide concrete elements for enriching the user experience. After the technical convergence of computer and mobile networks, also the mathematical and social traditions in the study of social networks are converging. This trend is demonstrated also by a growing number of pioneering studies that are uncovering intriguing aspects of social structure and dynamics highlighting their design implications. For instance, in the Reality Mining project conducted at the Massachusetts Institute of Technology (MIT), Eagle and his colleagues collected over 350.000 hours of continuous data on human behavior, including information about location, communication, proximity and activity, from 100 MIT students [8]. They addressed research questions such as the evolution of social networks in time, the predictability of people’s lives and the way information flows. Such understanding was aimed at designing better tools for the coordination of interpersonal and group interactions.

References:

[1] A. Andal-Ancion, P.A. Cartwright and G.S. Yip, “The Digital Transformation of Traditional Businesses”, MIT Sloan Management Review, summer 2003, Volume 44, No.4.

[2] K. Hafner, “The Epic Saga of The Well”, Wired Magazine, May 1997, Issue 5.05.

[3] M.E. Porter, “Clusters and the new economics of competition”, Harvard Business Review, November-December, 1998.

[4] R. Howard, Smart Mobs: The Next Social Revolution, Cambridge, Massachusetts, Perseus Books, 2002.

[6] Julián Candia, et al. 2008. “Uncovering individual and collective human dynamics from mobile phone records,” Journal of Physics A: Mathematical and Theoretical, volume 41 (May), pp. 1–11.

[7] K. Olga, (May 2006), “Social Networking Goes Mobile: So-called “friend sites” are wising up towireless” [Online]. Available: http://www.businessweek.com/technology/c…

[8] L. Giuseppe, “Mobile Social Networking in Theory and Practice”, First Monday, Nov. 2008, Volume 13, No.11