Foursquare and Yelp are each sites that capitalize on user-contributed reviews and recommendations. Users contribute their favorite places and things to do, spotlighting best-kept secrets and customer favorites. Users get visibility and even some amount of notoriety for their contributions. Their enthusiasm for, or against, a merchant can have substantial repercussions for businesses. In the age of social media, might sometimes makes right, whether the customer is “right” or not.
Social interaction models: Yelp and Foursquare
Yelp and Foursquare offer an interesting comparison in the use of social interaction models. For each of them has had to create a compelling and engaging social experience, and has done so with some degree of success.
Yelp has done it with a slight twist on reviews. Yelp’s reviews may be associated with a business, but are in fact as much about their authors as they are the business reviewed. On Yelp, users can profile their tastes, interests, habits, and opinions through the places they frequent. In this way, Yelp makes it easy for users to talk about themselves without having to fill in the “about me” box so common to un-themed profiles.
Foursquare does it with a combination of recommendations and offline activity check-ins. Users leave short posts recommending things to try or do at a location, and then separately check in to locations they visit. In a sense, Foursquare extends the practice of reviews by going mobile: Foursquare can be used to find friends on the go. But it substitutes the recommendation for the review, and in its focus on messaging over review writing, seems more closely aligned to social interactions and relationships than to reviewer taste profiling and publishing.
Yelp’s interaction models: extracting the value add
From a social interaction design perspective, the differences between Yelp and Foursquare are interesting. Each site is designed to capture users interested in real places.
Yelp captures interests in particular places and makes connections to other similar places: it turns the individual user’s subjective interest into an objective “type” of interest, and constructs relationships that are then surfaced as a directory of sorts.
For example, interest in one Chinese restaurant can be used to create links to other Chinese restaurants. Yelp can get as specific with this as it’s able to subdivide interests. Theoretically, it could get down to specific dishes, to service, price, ambience, and so on. It could do this (and does in some attributes, like price) by means of structuring form input at the review, or by extracting meta data by mining text (less reliable).
This is the essential practice of end-user review sites, and rests on the assumption that subjective review content can be translated into common social values. I call this “taste making,” for it by-and-large corresponds to the role played by media in our culture, relying in this case on local and “authentic” experts over accredited or branded (mass media) experts.
Bias in the model
The transformation of subjective interests (values held by the individual user) into some form of socially valid tastes and opinions is undermined, however, by the introduction of bias in the social practice of reviews. Bias enters the system because reviews not only serve to describe a business, but to express individual user personality also.
In any social system, the user’s interest in making an impression, and being seen (popularity, respect, credibility, or other form of social rank), introduces a second incentive to the core activity. If the core activity is the “review,” then motives corresponding to the system’s social architecture distort behavior. And indeed, popularity, leaderboard rank, visibility, follower count, and any number of similar social effects can be motivating to users for whom online interactions serve personal and psychological interests (which is not only commonplace, but deeply sticky).
Whether the user is interested and motivated by trust, reputation, celebrity, credibility, intellect, experience, or something else, will factor into his or her habits and online social participation styles. Engaging with these motives is essential to participation, but also contributes to the social bias and distortion of social content. No amount of filtering, sorting, or ordering user contributions can eliminate bias if it has been introduced by the motivating attributes of a social system.
Separating social interaction from content production
What Yelp has done, and which was smart (if unintended, I don’t know), was to offer symbolic and gestural tokens and icons to users for use in communicating with each other. This not only had the effect of building social relationships (compliments are great ice breakers) — it also offloads social interaction and communication into a separate social system. Users need not speak to each other in their reviews, but can do this by means of tokens. Reciprocity, as a social norm, then comes into play and encourages positive social behaviors. And exchange and gift economies come into play as a social mechanism governing the use of these tokens.
(Note that in many social systems, these tokens are an unlimited social resource; if there were limited numbers of tokens available to users, competition for possession of tokens for social rank would govern the dynamic.)
Foursquare’s interaction model: social activity
Now let’s look at Foursquare. In contrast to Yelp, Foursquare users profile themselves by where they have been, and to some degree by what they have done (insofar as they post a statement about it.) A look at Foursquare posts shows that consensus seems to emerge quickly around points of interest. Users may be more inclined to agree with one another on what makes a place good. But that’s not likely the reason for the uniformity of their posts. More likely is that the form here is the recommendation, not the review.
Recommendations are intrinsically more social: they are directed at an audience. And on Foursquare, the audience is those who are going to a venues, not those who are comparing venues (by review shopping). Not only are recommendations addressed to people (reviews being written for a public), they are most likely to cite the best thing to do.
And indeed, Foursquare seems more interested in cultivating social activity than in building a community of experts. Social activity benefits Foursquare by motivating users to check in to a venue when they are there, which in turn provides presence and location information useful to the mobile user.
Foursquare was built in the era of twitter, and takes inspiration more from tweeting than from writing. It serves communication and social connectedness; this, again, is clear from the site’s emphasis on friends and followers.
To help serve this purpose, the game-like aspect of Foursquare has been implemented well. A variety of badges provide two social functions: differentiating individual users from the user population overall (users differentiated by having a badge), and identifying user interests (by what the badge means). As with Yelp, the ambiguity involved in what a badge means can be compelling in itself (in Foursquare: is she a “player,” or does she just travel with male friends? Did she mean to look like a player or is that Foursquare’s doing?)
The interest here is a social interest. Foursquare attracts users who enjoy playing: for mayor, for stats, for badges, and to a lesser extent, for friends. Because social gaming and games suspend the normal conventions of social interaction while at the same time putting real relationships into play, there are endless variations Foursquare can roll out in the future.
For example, the site could embrace interests of users to whom pure social games are less appealing, and instead address their inclination to be taste makers, demonstrate expertise, display their depth of local experience or knowledge, and more. Foursquare could provide modalities to end users to bring attention to these other user personality types. Photographers might twitpic scenes and situations grabbed on location. Contests could be staged for “best of” category, including discoveries, best-kept secrets, and the more obvious local favorites. City walks could be extracted from local mayors for tips on a great first date, things to do on a family visit, or bartenders and service staff who are fun to talk to.
Frames of social activity
The advantage earned by Foursquare obtains from channelling social activity into social games. These games generate participation, offer a compelling engagement model, are fast and relatively quick and easy, and can be used as an interaction system for many different kinds of content.
From a social interaction design perspective, games are frames: interaction and user experience are framed by the game. All social situations involve a frame of some kind, whether mediated online or not. This frame supplies participants with an idea of What’s going on and How to proceed, both critical aspects to social interaction.
Use of a frame other than that intrinsic to the content itself provides other, and new, things to do. Therein lies the innovation of socially-mediated experiences: experience frames that leverage and extend relationships, forms of talk (questions, recommendations, etc), interactions with tokens (eg social gifts), gestures (eg compliments), and so on. Social interaction designers can use frames to organize social interaction around content, and thereby offload some of the social motives from content left behind, improving its value to non-participating users. Or the opposite: to concentrate social motives into communication in order to thicken a system’s social sticky. Every frame brings with it new ways to capture user interests and motivations.
Conclusion and implications
Interaction models that directly relate to users and what they find interesting, and not concepts like “community” or the “social graph,” are in my opinion the more precise approach to designing and leveraging social media. Since all social media involve some variation on talk and talking, interactions can be structured and organized by design and their outcomes ordered and presented to lay emphasis and focus on the aspects and social dynamics that propel a social system forward. We do this best, I think, not by abstracting models but by aligning them closer to user experiences. The richer our understanding of what users are like and what they do, the better our interaction models will be.