To choose restaurants and coffee shops, people are increasingly relying on social-networking sites. In a popular site such as Foursquare or Yelp, a place comes with descriptions and reviews, and with profile pictures of people who frequent them. Descriptions and reviews have been widely explored in the research area of data mining. By contrast, profile pictures have received little attention. Previous work showed that people are able to partly guess a place's ambiance, clientele, and activities not only by observing the place itself but also by observing the profile pictures of its visitors. Here we further that work by determining which visual cues people may have relied upon to make their guesses; showing that a state-of-the-art algorithm could make predictions more accurately than humans at times; and demonstrating that the visual cues people relied upon partly differ from those of the algorithm.
In the context of location-based services, place reviews have been widely explored. Tips and descriptions have been explored to study the popularity of Foursquare places @cite_11 @cite_4 ; to explain the demand of restaurants in Yelp; @cite_6 ; and to learn their relationship with check-ins and photos at Foursquare venues @cite_38 .
@cite_38 @cite_4 @cite_6 @cite_11
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Location-based social networking platform (e.g., Foursquare), as a popular scenario of participatory sensing system that collects heterogeneous information (such as tips and photos) of venues from users, has attracted much attention recently. In this paper, we study the distribution of these information and their relationship, based on a large dataset crawled from Foursquare, which consists of 2,728,411 photos, 1,212,136 tips and 148,924,749 check-ins of 190,649 venues, contributed by 508,467 users. We analyze the distribution of user-generated check-ins, venue photos and venue tips, and show interesting category patterns and correlation among these information. In addition, we make the following observations: i) Venue photos in Foursquare are able to significantly make venues more social and popular. ii) Users share venue photos highly related to food category. iii) Category dynamics of venue photo sharing have similar patterns as that of venue tips and user check-ins at the venues. iv) Users tend to share photos rather than tips. We distribute our data and source codes under the request of research purposes (email: yi.yu.yy@gmail.com). Online Location Based Social Networks (LBSNs), which combine social network features with geographic information sharing, are becoming increasingly popular. One such application is Foursquare, which doubled its user population in less than six months. Among other features, Foursquare allows users to leave tips (i.e., reviews or recommendations) at specific venues as well as to give feedback on previously posted tips by adding them to their to-do lists or marking them as done. In this paper, we analyze how Foursquare users exploit these three features - tips, dones and to-dos - uncovering different behavior profiles. Our study reveals the existence of very active and influential users, some of which are famous businesses and brands, that seem engaged in posting tips at a large variety of venues while also receiving a great amount of user feedback on them. We also provide evidence of spamming, showing the existence of users that post tips whose contents are unrelated to the nature or domain of the venue where the tips were left. Do online consumer reviews affect restaurant demand? I investigate this question using a novel dataset combining reviews from the website Yelp.com and restaurant data from the Washington State Department of Revenue. Because Yelp prominently displays a restaurant's rounded average rating, I can identify the causal impact of Yelp ratings on demand with a regression discontinuity framework that exploits Yelp's rounding thresholds. I present three findings about the impact of consumer reviews on the restaurant industry: (1) a one-star increase in Yelp rating leads to a 5-9 percent increase in revenue, (2) this effect is driven by independent restaurants; ratings do not affect restaurants with chain affiliation, and (3) chain restaurants have declined in market share as Yelp penetration has increased. This suggests that online consumer reviews substitute for more traditional forms of reputation. I then test whether consumers use these reviews in a way that is consistent with standard learning models. I present two additional findings: (4) consumers do not use all available information and are more responsive to quality changes that are more visible and (5) consumers respond more strongly when a rating contains more information. Consumer response to a restaurant's average rating is affected by the number of reviews and whether the reviewers are certified as "elite" by Yelp, but is unaffected by the size of the reviewers' Yelp friends network. Foursquare, the currently most popular location-based social network, allows users not only to share the places (venues) they visit but also post micro-reviews (tips) about their previous experiences at specific venues as well as "like" previously posted tips. The number of "likes" a tip receives ultimately reflects its popularity among users, providing valuable feedback to venue owners and other users. In this paper, we provide an extensive analysis of the popularity dynamics of Foursquare tips using a large dataset containing over 10 million tips and 9 million likes posted by over 13,5 million users. Our results show that, unlike other types of online content such as news and photos, Foursquare tips experience very slow popularity evolution, attracting user likes through longer periods of time. Moreover, we find that the social network of the user who posted the tip plays an important role on the tip popularity throughout its lifetime, but particularly at earlier periods after posting time. We also find that most tips experience their daily popularity peaks within the first month in the system, although most of their likes are received after the peak. Moreover, compared to other types of online content (e.g., videos), we observe a weaker presence of the rich-get-richer effect in our data, demonstrating a lower correlation between the early and late popularities. Finally, we evaluate the stability of the tip popularity ranking over time, assessing to which extent the current popularity ranking of a set of tips can be used to predict their popularity ranking at a future time. To that end, we compare a prediction approach based solely on the current popularity ranking against a method that exploits a linear regression model using a multidimensional set of predictors as input. Our results show that use of the richer set of features can indeed improve the prediction accuracy, provided that enough data is available to train the regression model.