This article was first published in “Mobile Media Practices, Presence and Politics: The Challenge of Being Seamlessly Mobile” Routledge Studies in New Media and Cyberculture [Hardcover] Kathleen M. Cumiskey (Editor), Larissa Hjorth (Editor), 2013
This paper presents an ethnographic fieldwork on Foursquare, a location-based social network and recommendations application for mobile phones. Contrary to its intended purpose of location sharing among friends, I discuss how the service facilitates local interactions with strangers based on Stanley Milgram’s concept of the ‘familiar stranger’. By revisiting his theory, I proffer the notion of a ‘networked familiar stranger’, an updated term characterized by the influence of the virtual sphere, distinguished by three main differences: (1) shared stranger, (2) enhanced storytelling and (3) virtual filtering. I conclude by discussing the possible implications of these interactions for local social interactions.
It was high noon when I walked into the local bakery situated just a few blocks away from my apartment. As I opened the door and stepped inside I immediately recognized him, my nemesis. He was standing in line, much taller than I thought he was, wearing a black suit that confirmed my inkling that he was a lawyer in the nearby office building. Although I had seen only one photo of him, I knew he was the guy that I had been battling for the last month of my life.
That was the first time I saw him in the flesh. And still, although we both didn’t say a word, our knowing looks said it all. It was obvious that our battle was far from over. Only one of us could be the Foursquare mayor of the bakery and neither of us would give up the fight.
I am not the only one having these kinds of confrontations on a daily basis. In recent years, the online sphere has been transforming into a less anonymous and a more accurate depiction of our daily life. With social network sites that display only “real identities”, search algorithms that provide the most precise “personalized results”—based on search history and browsing activity—and location-based services that pinpoint other users’ whereabouts, we are witnessing a growing cross-over of social interactions between the physical and the virtual sphere.
The emergence of smart phones with GPS capabilities, for example, paved the way for the development of online services that utilize users’ physical locations and enable them to interact with others in their close proximity in new ways that did not exist before. Popular mobile social networks such as Foursquare, SCVNGR and Instagram along with mobile versions of websites like Facebook and Yelp encourage users to “check in” to places they visit, leave tips, and see who else is in their surroundings.
This study sets to examine social interactions in location-based social networks and their implications for the users’ daily lives. The hypothesis for this research is that these interactions portray distinct characteristics due to their existence in “Net Locality”, a hybrid space that carries influences from both the local-physical place and the virtual sphere. This space is generated by a ubiquitous networked information condition that combines both digital information flows and daily practices of everyday life.
I chose to study these interactions through Foursquare, a service that is mostly used to broadcast and receive location updates among groups of friends, while at the same time offering recommendations for places in the immediate surroundings. Foursquare—a location-based service application for smartphones enabled with GPS capabilities—was launched in March 2009 and has over 10 million users worldwide (according to company data from August 2011), making it one of the biggest location-based services in the world.
The service provides its users a way to share their location with friends and explore places in their local surroundings. Users may leave tips, bookmark information about places and retrieve suggestions for recommended places nearby. By using a “curated social graph” method, the application prioritizes venues in close proximity and recommends places that match the users’ personal preferences.
After downloading and installing the free application, the user registers and creates a Foursquare profile. The profile can be based on a user’s email address or Facebook account credentials. When the user launches the application, Foursquare generates a list of nearby places based on GPS information (See Figure 1).
The list shown by the application is based on places submitted by its own users. For example, one list could display a restaurant, a coffee shop and a place called “the bench on the corner of Butler and 35th”, all user-submitted places. A user can select to check in at any of these places or add her own place to the list. In this way, the venue’s database is constantly updated with user-generated places that may be accurate or completely made up (e.g. venues called “hell” or “the love shack” I have encountered in numerous locations).
Then, the user checks-in and selects if she would like to stay “off the grid” or share this information with friends who use Foursquare, Facebook or Twitter (See Figure 9.2). If the user selects to broadcast the information, the application sends a notification to the user’s friends stating her current location. The manual check-in option mitigates privacy concerns users might have and allows for social performance.
In addition to sharing the location of the user, the service also utilizes game mechanics to motivate its users to compete with each other and earn virtual titles and badges. These accomplishments rarely have any real-life benefits and they are mostly earned only for the inherent gaming aspect. For example, if a user takes ten trips to the gym in thirty days, she will receive the “Gym Rat” badge. If a user checks-in at twenty different pizza places, she will be rewarded with the “Pizzaiolo” badge.
In this game, users can become the “mayor” of a place if they visit it often. A “mayorship” is awarded to the person who checked-in to a specific venue the most times in a certain period. Consequently, people become competitive and try to win the title. Retaining one’s mayorship often proves to be a hard task since it requires the mayor to keep visiting the place in order to prevent the possibility of anyone else stealing the title.
When a certain mayorship changes, the old mayor receives a notification with the new mayor’s information saying that the mayorship “was just stolen”. In this way, although the mayorship is handed over from one user to another, there is no direct interaction between the users themselves. The game mechanics embedded in the service often lead to “mayorship battles” between avid users.
Due to privacy concerns, the application developers made these battles one of the only interactions Foursquare users may have with people outside of their circle of friends. When a user signs up to the service, she is prompted to connect her Facebook, Twitter and Gmail accounts, so the application will find friends who also use the service and connect with them. The user can then send friend requests to other users, which, once approved, will enable both parties to receive location notifications from each other’s check-ins.
Other than battling for a mayorship title, Foursquare users can also interact with strangers over the application when they check-in to a venue or when they read tips left by others at the places they visit. After completing the check-in action, the application displays the current mayor of the venue together with a list of users that are also publicly checked-in to the same place in the same relative timeframe (not “off the grid”). Using this display, users can go through other users’ profile pages and pictures, see who else is checked-in there and even view their profile pages on other social networks such as Facebook and Twitter, if they were connected to their Foursquare profile.
This work is based on a six-month study conducted from September 2010 to February 2011 using participant observation, user observation, and in-depth interviews. Twenty-five in-depth interviews were conducted with Foursquare users from three cities in the United States. The participation requirement for interviewees was to have used Foursquare in a specific area for a period of at least three months and with an on-going activity of at least three days a week. These criteria ensured that the participants had a reasonable chance to interact with friends, explore places and come across other users in their daily routine.
The users were recruited by sending out Twitter messages to my followers asking to retweet the message to their own followers. The message linked to a webpage inviting people to participate in a study that focuses on “how people use Foursquare”. These Twitter messages were retweeted by several people to an audience of approximately 2000 people. I received messages from 27 people expressing their willingness to participate, finally resulting in 13 users that met my participation requirement.
In addition, I used Foursquare’s website to identify fervent users by the number of mayorships and titles they held. I contacted these users through their Twitter and Facebook accounts with a request to participate in my study. Out of the 20 people I contacted, 12 agreed to take part and share their experiences. I conducted my fieldwork in Pittsburgh, New York City and San Francisco; the demographics of this study consist of 25 Foursquare users (13 men and 12 women) ranging in age from 24 to 35 living in Pittsburgh (n=8), San Francisco (n=7) and New York City (n=10). The users I interviewed had a friends network ranging from 12 to 117 friends. All interviewees received $10 compensation for their participation.
The interview protocol was structured from open-ended questions investigating the nature of the social interactions people experience when using Foursquare. More specifically, the interviewees were asked about various topics such as their check-in practice (type of place, hours of the day, etc.), motivations, interactions with friends and strangers, place attachment, privacy concerns, and more.
All interviews were conducted and recorded face-to-face and later transcribed. Following grounded theory method, after the interviews were transcribed and coded I noticed several themes that repeatedly emerged from the results. One of these themes was the idea of recognizing familiar faces of strangers through the use of the application. Consequently, this coding led me to examine these interactions in light of the “Familiar Stranger” notion, a prevailing offline term used to describe the nature of this kind of urban ephemeral relations.
The Familiar Stranger
In his 1972 paper “The Familiar Stranger: An Aspect of Urban Anonymity”, social psychologist Stanley Milgram coined the term “familiar stranger” to depict a common social phenomenon—a relationship between two strangers who recognize one another through their daily encounters in public places (such as the subway, gym, etc.) but choose not to interact. Milgram claims that although these two people never communicate, their relationship is real and it is based on both sides agreeing to ignore each other. Consequently, if one of the two is missing from the frequent encounters, the other will notice her disappearance. This notion aligns with Simmel’s idea regarding the nature of strangers where he sees strangers as an important part of the individual’s life and the community rather than distant and disconnected.
Familiar strangers are part of the physical environment, a fixture of the local milieu. Milgram claims that one of the main characteristics of urban life is the fact that people often gain familiarity with the faces of people around them, yet they never interact. He sees these people as part of the urban environment, rather than actual persons with whom people interact. In this way, familiar strangers are as important to the perception of the local surroundings as street signs, public park benches, and other local landmarks. All of these in turn support the feeling of local identity and belonging. Without them, the local living environment becomes unfamiliar and lonely.
In 1972, a group of Milgram’s students at the City University of New York performed a short experiment among commuters in New York City. In the early morning hours, they went out and photographed large clusters of people waiting for the trains. Then, they gave a unique number to each figure in the photographs and a week later returned to the same stations and handed out the photographs to the commuters together with a cover letter and a questionnaire. The results showed that 89.5% of the people reported at least one familiar stranger. The average commuter identified 4 individuals whom she recognized but never spoke to, compared to a mean of 1.5 individuals with whom she conversed.
Milgram claimed that the status of familiar stranger does not reflect an absence of a relationship, but a different form of interpersonal relationship that has properties and consequences of its own and that is created due to the gap that exists between people’s intentions and actions. He noted that when familiar strangers see each other in places other than the site of their routine encounters, they would be more likely to interact with one another.
THE FAMILIAR STRANGER IN ONLINE RESEARCH
Since the study of location-based services is a relatively new research field, there are little earlier references or studies that noticed the connection between this emerging technology and the familiar stranger. Even the social study of online social network websites assigned limited attention to this phenomenon. Baym, for example, describes how online interaction of music fans could translate to local physical relationships such as familiar strangers while boyd examines this term in an online social networks context when she studied the use of Friendster.
Following Milgram’s idea, boyd claims that the virtual sphere within which Friendster operated provided the additional context needed for familiar strangers to approach each other when they are both outside of their regular environment. According to boyd, by browsing the site, users found the profiles of people that they saw when they were out with their friends. Based on the additional details they acquired from the user profile on Friendster, they had enough information to message her on Friendster or approach her in real life. Since her study refers only to online social networks, this conclusion should be reexamined in the light of location-based services.
From a different lens, several projects in the field of Human Computer Interaction have dealt with the phenomenon of the familiar stranger. Paulos and Goodman created studies such as “The Familiar Stranger Project” and “Jabberwocky” that examined the ways technology can affect users’ connection to their physical surroundings. These projects tried to extend the familiar stranger relationship through sensors and Bluetooth technology so they could be used in two scenarios: when someone is new to a city and wants to feel more familiar with the place and the people around her and when someone is too familiar with the place and people around her and wants to go to an area to visit new places and see new people.
THE NETWORKED FAMILIAR STRANGER
Based on the interviews findings, I offer to revisit the familiar stranger term, and depict the networked familiar stranger that both complies with Milgram’s original ideas but at the same time merges with the influence of the virtual sphere. This updated term therefore is a direct result of both the local and virtual interactions in the hybrid space created by location-based social networks. Since the original term is strongly rooted in the local physical place, I ask to embody the connectivity that is provided by the virtual sphere by adding the “networked” element to it.
The term does not refer only to users we see first on our mobile phone and then in real-life, but it spans across interactions both in the virtual and the physical world. For example, noting the current mayor on Foursquare each time we visit the local bakery and then seeing the mayor in person is equally as significant as spotting someone first in real-life and then identifying her profile on the mobile service. Although these two narratives of interactions differ, I suggest that they both conform to the added characteristics of the networked familiar stranger. These characteristics include the shared stranger, enhanced storytelling and the virtual filtering. The shared stranger refers to the possibility of different users having the same familiar strangers and sharing them among themselves even though they do not attend the same places at the same times. By enhanced storytelling, I refer to users using the additional details provided by other user profiles to create a more elaborate imagined narrative for their familiar strangers. Lastly, virtual filtering is the process of singling out the familiar strangers out of an urban or virtual crowd through the application.
It is easier to share our relationship with networked familiar strangers among our friends, as the virtual sphere does not confine the interaction to a specific time. For example, although my friend Brian goes to the university library in the mornings and I go in the afternoons, we discovered that we both recognized the library’s mayor when we saw him at a local bar. Although he was out of context, we both identified him as the library’s mayor.
Since the application promotes its users to add their Gmail and Yahoo contacts together with Facebook and Twitter friends, the likelihood that several close friends will have the same networked familiar stranger is high. For example, Josh, an informant from New York City, described how he and his friends recognize and share people from the application in real life:
It’s just about the curiosity and being connected to people, even strangers. It’s fun to see how many people are on the app and who checks-in where . . . when I’m out with friends who are also on, we play the game of trying to spot the people we have seen on the app. It’s only connecting virtually to people through sight and adding as friends to Foursquare. I never met anyone new in person from Foursquare. Seeing people you recognize from the app is interesting in a fun way, I’ve never approached someone to say “hey I just saw you checked-in.” I’m always curious to see if people look like their pictures.
As we can see, Josh enjoys sharing the experience of identifying a familiar face with his friends. In a way, the game Josh and his friends play allows them to share personal experiences with their familiar stranger among themselves and by that expand the number of shared networked familiar strangers. Throughout the interviews I noted that sixteen out of the twenty-five users had at least one instance in which they shared a networked familiar stranger with their friends.
This practice might also lead to a situation in which two friends share the same networked familiar stranger, but she will be associated for each one of them to a different physical place. Thus, in addition to the physical venues, the social space created by the application becomes the “place” from which users identify their networked familiar strangers. Sharing the networked familiar stranger therefore can be considered as a way for users to strengthen their ties with their close friends. In other words, when we discuss with friends the experiences of identifying the same familiar faces around us, our interpersonal connection with them grows stronger since we now have even more joint interests.
The Shared Stranger is a specific characteristic to location-based services due to the built-in sharing tools provided by the virtual sphere. These tools create sharing opportunities that might not have been realized in the physical place. For instance, two friends who took different trains into Grand Central each day might have found themselves commenting about the pretzel salesman near Track 8. But the fact that their visits to the train station are documented and shared through the application contributes to the possibility of a shared stranger.
We know more about our networked familiar strangers. As Milgram notes, many people often think about the familiar strangers around them and try to imagine their lives, jobs, etc. These details contribute to the imagined relationship with our familiar strangers.
In this way, people build a background story for their familiar strangers and by that differentiate them from all other strangers. I argue that location-based social networks enhance and expand these imagined narratives due to the personal information people post to their profile pages in addition to other details that are presented by the services. For example, by browsing a user’s profile on Foursquare, one can learn the user’s first name and the initial of her last name and the user’s home town; one can also browse her other Foursquare friends, see the number and kinds of badges she obtained, and the places where she holds a mayorship together with tips she might have left in other places. In addition, if the user connects her Facebook and Twitter to her Foursquare profile, other users can also browse through them. The sum of these details can greatly help in the storytelling process of the networked familiar stranger.
Twenty informants out of the twenty-five I interviewed confirmed that after they check in to a place they use the application to look up the mayor. From the mayor’s profile page they sometimes also see which other mayorships this mayor holds and by that learn more about the places where she is considered to be a regular. For example, Lisa, one of my informants from New York City, told her Foursquare fairytale story about how she virtually met her dream guy, although she never really did meet or talk to him:
The mayor of my favorite coffee place is this guy I’ve seen there several times and have been keeping an eye on for some time now. It all started when I saw he was the mayor of my local sushi place and since then I noticed that he is also the mayor of several other places I really like. I’m pretty sure he is an architect because he is the mayor of an architect’s office and the Bronx Department of Buildings. We’ve never talked and I think we never will but he is dreamy.
Lisa’s depiction of her dream guy is a common way users utilize the information shared on the service to learn more and become familiar with the people around them. Although even before location-based social networks people imagined what their familiar stranger’s life is like based on clothes, behavior, and the hours and places where she was seen, the use of this service provides additional information that is otherwise not easily available. This information can include the frequency of visits to certain places (browsing the mayorships a user obtained), personal preferences and opinions (reading tips the user left) and social circles (going through her list of Foursquare friends).
In other words, users build their impression of their networked familiar strangers based on the information shared on this platform. Twenty of the informants said they feel these details have more credibility than online dating or Facebook profiles due to the fact that people cannot forge their actual location or their check-in history.
The way we single out the networked familiar stranger from the crowd now takes place also in the virtual sphere. The overwhelming amount of people and possible interpersonal connections from the urban surroundings together with the online sphere must be reduced and filtered. Since the networked familiar stranger is a direct result of that, we are now filtering out the messes not only based on their visual appearance but also through the use of location-based technology.
Some familiar strangers in Milgram’s study turned out to be “socio-metric stars” in that they were recognized by a large group of people. One of these stars was a woman who waited for the train in a mini skirt every day, even on cold winter days. As a result, many of the commuters recognized her in the study pictures.
If the familiar strangers stood out of the crowed based on their visual appearance, I suggest that the networked familiar strangers stand out for additional reasons. Eighteen out of the twenty-five informants said that unless they had used Foursquare they would not have noticed the networked familiar strangers in their daily life. Patrick, one of the Informants from New York City said:
It’s New York so people are everywhere. Especially when you are at a particularly crowded venue like a bar or at a concert. If you aren’t looking for someone specifically, you likely won’t see them.
For Patrick, Foursquare is another filter for the masses. It seems that although the city overload is making users filter out their surroundings, the addition of location-based services to the equation seems to actually promote users to be more aware of those around them. The virtual sphere expands our perception of strangers in our local surroundings. It works in both ways; people who we see on the mobile service and get familiarized with in the virtual sphere are then identified in the physical place we visit. But also, people we first see in real-life and to whom we pay no attention can suddenly become networked familiar strangers after we see their profile picture in the virtual sphere.
The information overload of the virtual sphere adds to the urban overload and together they form the networked familiar strangers. Based on my interviews, I argue that networked familiar strangers would not have been noticed otherwise in the daily life setting. In several cases, it was the actions in the virtual world such as being the mayor of a place that introduced them and made them familiar. Such a situation happened to Justin, a Foursquare user from Pittsburgh:
There was a guy at my gym that was the mayor for a while before I stole it from him. I kept seeing his picture but I thought it was strange to me that he did not look familiar to me. Someone I would see often at the gym. I eventually recognized him in person but strangely enough it took a while for me to cross paths with him. I have still never spoken to him though. It felt bizarre. You just have this one picture representing him on Foursquare and that’s all you have to go by in terms of information. I would definitely not notice him otherwise. He just would have blended in with the rest of the regulars. I doubt he would have caught my eye.
As we can see from his story, Justin’s networked familiar stranger was not someone he would notice otherwise unless he had used Foursquare. In this case, the use of the application made Justin pay closer attention and actively look for his networked familiar stranger. The application serves as an additional filtering platform that promotes us to identify the networked familiar strangers not only based on their visual appearance but also based on other characteristics such as their visit frequency to a specific place or the tips left there.
The overlap between the vast amount of digital information and the urban city overload creates the terms for the emergence of the networked familiar stranger. This social interaction takes place in a Net Locality space, which represents the need of people to contextualize themselves within a massively growing network of information. Consequently, interactions that are produced in this condition share characteristics of both the local physical world as well as of the online sphere. Viewing this type of interaction as one that aligns with previous ideas about the ordering mechanisms of strangers in urban settings, I argue that the networked familiar stranger, therefore, is an ordered way to acknowledge and handle the different aspects of offline and online urban anonymity.
Gaining a networked familiar stranger is the result of a social process, which takes time, like any interpersonal interaction. In this regard, my informants indeed noticed that their connection to the networked familiar stranger had to be built over time and through daily use of the applications mainly in third places such as coffee shops, bars, gyms etc. The process of obtaining and maintaining a networked familiar stranger may include some kind of online interaction in the virtual sphere, but these interactions do not necessarily affect the users’ local interactions. In other words, users can interact over the service (compete for mayorships, follow each other on Twitter, etc.) but could still ignore each other in real life.
Throughout the interviews, my informants described their networked familiar strangers as people they would not have noticed otherwise, much less be interested in knowing their background. I argue that the networked familiar strangers are added to our already established familiar strangers. The use of location-based social networks filters the networked familiar strangers based not only on physical encounters but also on their own visits to a specific place. In other words, if a user frequents a place and becomes its mayor, although we never met her, she might become our networked familiar stranger.
The real life barriers that build up between familiar strangers are very different from the virtual ones. As boyd noticed, users feel much more comfortable approaching other users online than in real life. It seems that the fact that the users are in a different realm, with the application acting as a unifying point of interest and play, encourages users to get to know others both directly through virtual interaction or indirectly through virtual stalking. This practice promotes a stronger connection to familiar strangers in our local surroundings. Users recognize more faces and therefore feel more “at home” as the use of the application contributes to the feeling of familiarity in local communities. For example, Kyle, an informant from Pittsburgh, noticed that whenever he used the application in a place he felt had many strangers, having a prior connection to someone through Foursquare instantly transformed his connection to them and “made the place more welcoming.”
And it might indeed feel that way for Kyle, since the networked familiar strangers are rarely different from him. The familiar faces users recognized through Foursquare were of people who visited the same places as they did and shared the same interests. Just like in Milgram’s familiar strangers, the service promotes segregation among users with the same cultural capital, distinguishing themselves based on cultural and financial factor. This could lead to overfiltering which results in encountering the same people in the same places. For example, if a certain user frequents a pricy restaurant or a posh sports club, she will encounter other users that visit the same places and contribute to the formation of an area that carries a distinct character.
Humphreys suggests that location-based services can change the way users experience urban public space and rearrange social and spatial practices. In this way, spotting familiar faces from the application in the busy urban streets, browsing through profile pages of users that are also checked-in at the same place and time, and sharing these experiences with friends can create a social setting that contributes to the users’ perception of their local area.
Sutko and de Souza e Silva have challenged the idea that location-based services increase social interactions and help users meet new people in public space. They claim that location-based services only support already established social norms of interactions despite challenging traditional sociability practices. These findings both differ and align with the results of this study. As we have seen from the results of this study, Foursquare interactions do reinforce already existing familiar stranger relationships, but at the same time they also create new relationships with networked familiar strangers that did not exist before.
This study has several limitations. First, one must keep in mind that despite the fast spread of location-based technology, it is still in an early phase of development and distribution. The users I interviewed can be generally described as early adopters of technology and therefore their use might shape the future ways in which the general public uses them. In addition, the application I chose to study does not represent all location-based services. There are several services that compete with Foursquare for the same audience. These services may offer different user interaction possibilities for their subscribers and therefore might lead to other usage patterns.
Moreover, the information gathered during this research represents only the interactions during the study’s timeframe and might change due to on-going developments and iterations in the services software. Since I conducted the interviews, Foursquare has issued several updated versions of the services that add new functions. Having said that, although the services might add or change the options they offer their users, the basic representation of nearby strangers is at its core.
CONCLUSION AND FUTURE WORK
This study describes the emergence of the networked familiar stranger, a novel social phenomenon that is derived from the merging of the virtual and local-physical spheres.
My findings show that the use of Foursquare has direct implications to our experience of familiar strangers in our surroundings and promotes the discovery of networked familiar strangers. Moreover, the additional information brought by location-based social networks contributes to strengthening connections with existing familiar stranger as well as helps users identify networked familiar strangers, share them with friends and enhance their imagined life narrative.
Future research should explore if the relationships between networked familiar strangers affect the already existing familiar strangers. Another issue that should be researched deals with the difference in the strength of ties with familiar strangers versus networked familiar strangers. The use of location-based services in rural areas should also be addressed in further research due to the difference it proposes to the users’ local nearby social setting. Moreover, the ease of creating a crossover from a networked familiar stranger’s status to an actual friendship is another aspect that should be examined.
Since location-based social networks such as Foursquare are still in their early stages, it is not yet clear which one of them, if any, will find their way to the mass use of the general public. It is clear though that the technological development of location-based services in the next years will provide researchers exciting new subjects of inquiry to the study of local social interactions.
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 https://Foursquare.com/about (accessed September, 5, 2011).
 The “Curated Social Graph” method, developed specifically for the service by Foursquare, is an algorithm that considers friends’ check-ins, the user check-in history and other factors to produce a list of recommended places in the nearby area. “Social Graph” algorithms are commonly used by online social networks such as Facebook, Google+, etc. to provide a more personalized experience by utilizing the information gathered about users relationships and interests.
 As the Foursquare team notes on their blog (http://blog.Foursquare.com/2011/03/14/1up-the-importance-of-platforms-and-how-we%E2%80%99re-extending-ours/ [accessed September, 5, 2011]), one of the problems in the beginning of Foursquare was creating a venues database. The company decided to utilize their users to solve that problem and enabled user-generated venues. Two years later, the database had more than fifteen million user-submitted venues.
 Henriette Cramer, H., Mattias Rost and Lars Erik Holmquist, “Performing a check-in: emerging practices, norms and ‘conflicts’ in location-sharing using Foursquare,” Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services (MobileHCI ‘11) (ACM, New York, NY, USA, 2011): 57-66.
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 Online social networks and dating websites differ from location-based social networks due to the added technological factor of location. In location-based social networks, the user’s location is the only information details the user cannot control. A user cannot pretend to be in another city or state since the system is programmed to recognize attempts to check-in at places that are not actually in the users’ proximity and does not award points, titles etc. This is a key difference between online social networks websites such as Facebook, MySpace etc. and location-based services.
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 Daniel M. Sutko and Adriana de Souza e Silva, “Location-aware mobile media and urban sociability,” New Media & Society 13(5) (2011) 807-823.
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