Foursquare Check-ins Research Articles

Do people communicate about their whereabouts? Investigating the relation between user-generated text messages and Foursquare check-in places

ABSTRACTThe social functionality of places (e.g. school, restaurant) partly determines human behaviors and reflects a region’s functional configuration. Semantic descriptions of places are thus valuable to a range of studies of humans and geographic spaces. Assuming their potential impacts on human verbalization behaviors, one possibility is to link the functions of places to verbal representations such as users’ postings in location-based social networks (LBSNs). In this study, we examine whether the heterogeneous user-generated text snippets found in LBSNs reliably reflect the semantic concepts attached with check-in places. We investigate Foursquare because its available categorization hierarchy provides rich a-priori semantic knowledge about its check-in places, which enables a reliable verification of the semantic concepts identified from user-generated text snippets. A latent semantic analysis is conducted on a large Foursquare check-in dataset. The results confirm that attached text messages can represent semantic concepts by demonstrating their large correspondence to the official Foursquare venue categorization. To further elaborate the representativeness of text messages, this work also performs an investigation on the textual terms to quantify their abilities of representing semantic concepts (i.e., representativeness), and another investigation on semantic concepts to quantify how well they can be represented by text messages (i.e., representability). The results shed light on featured terms with strong locational characteristics, as well as on distinctive semantic concepts with potentially strong impacts on human verbalizations.

Reconstructing Activity Location Sequences From Incomplete Check-In Data: A Semi-Markov Continuous-Time Bayesian Network Model

Geo-location data from the check-ins made in online social media offers us information, in new ways, to understand activity-location choices of a large number of people. However, one of the major challenges of using check-in data is that it has missing activities, since users share their activities voluntarily. In this paper, we present a probabilistic modeling approach to reconstruct user activity-location sequences from this incomplete activity participation information. Specifically, we answer the question of how to predict an individual’s next activity, its duration and location given the incomplete trajectory data. The model describes the dynamics of individual activity participation behavior evolving over continuous time. A semi-Markov modeling approach is used to capture the stochastic processes involved in the activity generation mechanism. We present a particle-based Markov chain Monte Carlo sampler to run inference over the model. We further develop an expectation-maximization algorithm to learn the unknown parameters of the model from incomplete trajectory data. Finally, the method is applied to synthetically generated activity-location sequences and a data set of Foursquare check-ins of the users from New York City. Our experiments show that this method can successfully extract the true transition and duration distributions given the incomplete trajectory information. The proposed approach can help building many intelligent transportation applications using check-in data.

On the Comparison of the Social Dynamic of Large Cities through Participatory Sensing

The dynamics of cities have been studied over the years with various applications such as urban planning, disease propagation, traffic forecasting, local recommendations and studies of human social behavior. However, with the technological evolution, especially the popularization of smartphones and the Internet, a new opportunity is presented to carry out such studies: the use of social media data for the population study. In this context, this paper aims to present a new way to compare cities, using as similarity measure of the pattern of social mobility of its inhabitants. To validate the study, 1,601,323 Foursquare check-ins were used spread over 10 cities in a period of 33 days.

A large-scale study of cultural differences using urban data about eating and drinking preferences

Traditional ways to study urban social behavior, e.g. surveys, are costly and do not scale. Recently, some studies have been showing new ways of obtaining data through location-based social networks (LBSNs), such as Foursquare, which could revolutionize the study of urban social behavior. We use Foursquare check-ins to represent user preferences regarding eating and drinking habits. Considering datasets differing in terms of volume of data and observation window size, our results indicate that spatio-temporal eating and drinking habits of users voluntarily expressed in LBSNs has the potential to explain cultural habits of the users. From this, we propose a methodology to identify cultural boundaries and similarities across populations at different scales, e.g., countries, cities, or neighborhoods. This methodology is extensively evaluated in several aspects. For instance, by proposing some variations of it disregarding some of the considered dimensions, as well as analyzing the results using datasets from different periods and window of observation. The results indicate that our proposed methodology is a promising approach for automatic cultural habits separation, which could enable new urban services.

On route planning by inferring visiting time, modeling user preferences, and mining representative trip patterns

Location-based services allow users to perform check-in actions, which record the geo-spatial activities and provide a plentiful source to do more accurate and useful geographical recommendation. In this paper, we present a novel Preferred Time-aware Route Planning (PTRP) problem, which aims to recommend routes whose locations are not only representative but also need to satisfy users’ preference. The central idea is that the goodness of visiting locations along a route is significantly affected by the visiting time and user preference, and each location has its own proper visiting time due to its category and population. We develop a four-stage preference-based time-aware route planning framework. First, since there is usually either noise time on existing locations or no visiting information on new locations, we devise an inference method, LocTimeInf, to predict the location visiting time on routes. Second, considering the geographical, social, and temporal information of users, we propose the GST-Clus method to group users with similar location visiting preferences. Third, we find the representative and popular time-aware location-transition behaviors by proposing Time-aware Transit Pattern Mining (TTPM) algorithm. Finally, based on the mined time-aware transit patterns, we develop a Preferred Route Search (PR-Search) algorithm to construct the final time-aware routes. Experiments on Gowalla and Foursquare check-in data exhibit the promising effectiveness and efficiency of the proposed methods, comparing to a series of competitors.

An analysis of the Gezi Park social movement tweets

PurposeTwitter usage during Gezi Park Protests, a significant large-scale connective action, is analyzed to reveal meaningful findings on individual and group tweeting characteristics. Subsequent to the Arab Spring in terms of its timing, the Gezi Park Protests began by the spread of news on construction plans to build a shopping mall at a public park in Taksim Square in Istanbul on May 26, 2013. Though started as a small-scale local protest, it emerged into a series of multi-regional social protests, also known as the Gezi Park demonstrations. The paper aims to discuss these issues.Design/methodology/approachThe authors sought answers to three important research questions: whether Twitter usage is reflective of real life events, what Twitter is actually used for, and is Twitter usage contagious? The authors have collected streamed data from Twitter. As a research methodology, the authors followed social media analytics framework proposed by Fan and Gordon (2014), which included three consecutive processes; capturing, understanding, and presenting. An analysis of 54 million publicly available tweets and 3.5 million foursquare check-ins, which account to randomly selected 1 percent of all tweets and check-ins posted from Istanbul, Turkey between March and September 2013 are presented.FindingsA perceived lack of sufficient media coverage on events taking place on the streets is believed to result in Turkish protestors’ use of Twitter as a medium to share and get information on ongoing and planned demonstrations, to learn the recent news, to participate in the debate, and to create local and global awareness.Research limitations/implicationsData collection via streamed tweets comes with certain limitations. Twitter restricts data collection on publicly available tweets and only allows randomly selected 1 percent of all tweets posted from a specific region. Therefore, the authors’ data include only tweets of publicly available Twitter profiles. The generalizability of the findings should be regarded with concerning this limitation.Practical implicationsThe authors conclude that Twitter was used mainly as a platform to exchange information to organize street demonstrations.Originality/valueThe authors conclude that Twitter usage reflected Street movements on a chronological level. Finally, the authors present that Twitter usage is contagious whereas tweeting is not necessarily.

An adaptive hawkes process formulation for estimating time-of-day zonal trip arrivals with location-based social networking check-in data

Location-Based Social Networking (LBSN) services, such as Foursquare, Facebook check-ins, and Geo-tagged Twitter tweets, have emerged as new secondary data sources for studying individual travel mobility patterns at a fine-grained level. However, the differences between human social behavioral and travel patterns can cause significant sampling bias for travel demand estimation. This paper presents a dynamic model to estimate time-of-day zonal trip arrival patterns. In the proposed model, the state propagation is formulated by the Hawkes process; the observation model implements LBSN sampling. The proposed model is applied to Foursquare check-in data collected from Austin, Texas in 2010 and calibrated with Origin-Destination (OD) data and time of day factor from Capital Area Metropolitan Planning Organization (CAMPO). The proposed model is compared with a simple aggregation model of trip purposes and time of day based on a prior daily OD estimation model using the LBSN data. The results illustrate the promising benefits of applying stochastic point process models and state-space modeling in time-of-day zonal arrival pattern estimation with the LBSN data. The proposed model can significantly reduce the number of parameters to calibrate in order to reduce the sampling bias of LBSN data for trip arrival estimation.

Location-based big data analytics for guessing the next Foursquare check-ins

Location-based services on GPS-enabled smartphones are undergoing strong growth. Capitalizing on the popularity of this geo-location social media, a mobile app called Foursquare is developed to recommend its users places where they may be interested in, to travel from their current proximities. Such location data, in the form of check-ins by Foursquare, have huge business potentials including marketing, advertising and consumers' behaviors analysis. Many researchers from both academia and industries are seriously looking into this location-based big data which comes in high velocity (with millions of users and frequent geo-tagging), and wide variety (with potentially many meta-data and associations), accumulating into a huge volume. One of the fundamental analytics in such big data is to guess which check-in locations a user would move to, as a prerequisite for sequential mining and other lifestyle pattern analysis. This paper reports a novel, but simple big data analytic by sampling a portion of location data for predicting the next check-in locations. This proposed analytic does not need every individual user's history path and ID to match the history path of the current user in the database in order to infer a prediction. We show by a simulation experiment based on a Foursquare dataset that a minimum of two pairs of coordinates are required to provide a prediction. Several variables such as segment lengths, number of check-ins, and time factors are investigated in the experiment in relation to the prediction accuracy.

Context-Aware Location Recommendation Using Geotagged Photos in Social Media

Recently, the increasing availability of digital cameras and the rapid advances in social media have led to the accumulation of a large number of geotagged photos, which may reflect people’s travel experiences in different cities and can be used to generate location recommendations for tourists. Research on this aspect mainly focused on providing personalized recommendations matching a tourist’s travel preferences, while ignoring the context of the visit (e.g., weather, season and time of the day) that potentially influences his/her travel behavior. This article explores context-aware methods to provide location recommendations matching a tourist’s travel preferences and visiting context. Specifically, we apply clustering methods to detect touristic locations and extract travel histories from geotagged photos on Flickr. We then propose a novel context similarity measure to quantify the similarity between any two contexts and develop three context-aware collaborative filtering methods, i.e., contextual pre-filtering, post-filtering and modeling. With these methods, location recommendations like “in similar contexts, other tourists similar to you often visited …” can be provided to the current user. Results of the evaluation with a publicly-available Flickr photo collection show that these methods are able to provide a tourist with location recommendations matching his/her travel preferences and visiting context. More importantly, compared to other state-of-the-art methods, the proposed methods, which employ the introduced context similarity measure, can provide tourists with significantly better recommendations. While Flickr data have been used in this study, these context-aware collaborative filtering (CaCF) methods can also be extended for other kinds of travel histories, such as GPS trajectories and Foursquare check-ins, to provide context-aware recommendations.

The object of mobile spatial data, the subject in mobile spatial research

With an estimated one billion smartphones producing over 5 petabytes of data a day, the spatially aware mobile device has become a near ubiquitous presence in daily life. Cogent, excellent research in a variety of fields has explored what the spatial data these devices produce can reveal of society, such as analysis of Foursquare check-ins to reveal patterns of mobility for groups through a city. In such studies, the individual intentions, motivations, and desires behind the production of said data can become lost through computational aggregation and analysis. In this commentary, I argue for a rethinking of the epistemological leap from individual to data point through a (re)seating of the reflexive, self-eliciting subject as an object for spatial big data research. To do so, I first situate current research on spatial big data within a computational turn in social sciences that relies overly on the data produced as a stand-in for the subject producing said data. Second, I argue that a recent shift within geography and cognate disciplines toward viewing spatial big data as a form of spatial media allows for study of the sociotechnical processes that produce modern assemblages of data and society. As spatial media, the spatial big data created through mobile device use can be understood as the data of everyday life and as part of the sociotechnical processes that produce individuals, data, and space. Ultimately, to understand the data of everyday life, researchers must write thick descriptions of the stories we tell ourselves about the data we give off to others.

A contextualized and personalized model to predict user interest using location-based social networks

The accurate determination of user interest in terms of geographic information is essential to numerous mobile applications, such as recommender systems and mobile advertising. User interest is greatly influenced by the usage context and varies across individuals; therefore, a user interest model should incorporate these individual needs and propensities. In this paper, we present an approach to model user interest in a contextualized and personalized manner based on location-based social networks. Multinomial logistic regression is employed to quantify the relationship between user interest and usage context at both the aggregate and individual levels. The proposed approach is tested in a real-world application using Foursquare check-ins issued between February and June 2014 in the three major cities of Chicago, Los Angeles and New York. Results demonstrate the capability of the contextualization process for capturing contextual influences on user interest, and that such influences can be observed at a fine-grained scale at the individual level through the personalization process. The proposed approach therefore enables contextualized and personalized estimation of user interest, thereby contributing useful information to follow-up mobile applications.

Investigation of Travel and Activity Patterns Using Location-based Social Network Data: A Case Study of Active Mobile Social Media Users

Due to its relatively high availability and low cost, location-based social network (LBSN) (e.g., Foursquare) data (a popular type of volunteered geographic information) seem to be an alternative or complement to survey data in the study of travel behavior and activity analysis. Illustrating this situation, recently, a number of studies attempted to use LBSN data (e.g., Foursquare check-ins) to investigate patterns of human travel and activity. Of particular note is that compared to other individual-level characteristics of users, such as age, profession, education, income and so forth, gender is relatively highly available in the profiles of Foursquare users. Moreover, considering gender differences in travel and activity analysis is a popular research topic and is helpful in better understanding the changes in women’s roles in family, labor force participation, society and so forth. Therefore, this paper empirically investigates how gender influences the travel and activity patterns of active local Foursquare users in New York City. Empirical investigations of gender differences in travel and activity patterns are conducted at both the individual and aggregate level. The empirical results reveal that there are gender differences in the travel and activity patterns of active local users in New York City at both the individual and aggregate level. Finally, the results of the empirical study and the extent to which LBSN data can be exploited to produce travel diary data are discussed.

Location contexts of user check-ins to model urban geo life-style patterns.

Geo-location data from social media offers us information, in new ways, to understand people's attitudes and interests through their activity choices. In this paper, we explore the idea of inferring individual life-style patterns from activity-location choices revealed in social media. We present a model to understand life-style patterns using the contextual information (e. g. location categories) of user check-ins. Probabilistic topic models are developed to infer individual geo life-style patterns from two perspectives: i) to characterize the patterns of user interests to different types of places and ii) to characterize the patterns of user visits to different neighborhoods. The method is applied to a dataset of Foursquare check-ins of the users from New York City. The co-existence of several location contexts and the corresponding probabilities in a given pattern provide useful information about user interests and choices. It is found that geo life-style patterns have similar items—either nearby neighborhoods or similar location categories. The semantic and geographic proximity of the items in a pattern reflects the hidden regularity in user preferences and location choice behavior.

Contextualized Relevance Evaluation of Geographic Information for Mobile Users in Location-Based Social Networks

The relevance of geographic information to mobile users must be evaluated by taking into account the usage context. This paper assumes that emerging Location-based Social Networks (LBSNs) contain contextual information rich enough to be used in order to contextualize such an evaluation process. This assumption is demonstrated through an exploratory analysis of a Foursquare check-in dataset, which reveals the impacts of two contextual factors—temporal and spatial—on mobility patterns. This paper then proposes an approach that may be used to contextualize the evaluation of geographic information’s relevance. The proposed algorithm links a priori relevance to the contextualized relevance using the hidden impacts of contextual factors. Improved performance from the experiments carried out confirms the validity of the proposed approach, as well as the benefits of utilizing contextual information within the relevance evaluation process.

Use of social media across US hospitals: descriptive analysis of adoption and utilization.

BackgroundUse of social media has become widespread across the United States. Although businesses have invested in social media to engage consumers and promote products, less is known about the extent to which hospitals are using social media to interact with patients and promote health.ObjectiveThe aim was to investigate the relationship between hospital social media extent of adoption and utilization relative to hospital characteristics.MethodsWe conducted a cross-sectional review of hospital-related activity on 4 social media platforms: Facebook, Twitter, Yelp, and Foursquare. All US hospitals were included that reported complete data for the Centers for Medicare and Medicaid Services Hospital Consumer Assessment of Healthcare Providers and Systems survey and the American Hospital Association Annual Survey. We reviewed hospital social media webpages to determine the extent of adoption relative to hospital characteristics, including geographic region, urban designation, bed size, ownership type, and teaching status. Social media utilization was estimated from user activity specific to each social media platform, including number of Facebook likes, Twitter followers, Foursquare check-ins, and Yelp reviews.ResultsAdoption of social media varied across hospitals with 94.41% (3351/3371) having a Facebook page and 50.82% (1713/3371) having a Twitter account. A majority of hospitals had a Yelp page (99.14%, 3342/3371) and almost all hospitals had check-ins on Foursquare (99.41%, 3351/3371). Large, urban, private nonprofit, and teaching hospitals were more likely to have higher utilization of these accounts.ConclusionsAlthough most hospitals adopted at least one social media platform, utilization of social media varied according to several hospital characteristics. This preliminary investigation of social media adoption and utilization among US hospitals provides the framework for future studies investigating the effect of social media on patient outcomes, including links between social media use and the quality of hospital care and services.

Communicating Through Location: The Understood Meaning of the Foursquare Check-In

Location-based social networks LBSNs are mobile applications that allow people to share their physical location with friends through their mobile device. The sharing of location is a relatively new form of computer-mediated communication, and there is a lack of existing research examining the coordination practices of people using social location sharing services. This article reports on qualitative interviews with frequent users of the LBSN Foursquare to show both how LBSNs complicate views of the relative placelessness of traditional mobile communication and how the design of Foursquare complicates its utility as a mobile coordination tool.

A Tale of Cities: Urban Biases in Volunteered Geographic Information

Geotagged tweets, Foursquare check-ins and other forms of volunteered geographic information (VGI) play a critical role in numerous studies and a large range of intelligent technologies. We show that three of the most commonly used sources of VGI – Twitter, Flickr, and Foursquare – are biased towards urban perspectives at the expense of rural ones. Utilizing a geostatistics-based approach, we demonstrate that, on a per capita basis, these important VGI datasets have more users, more information, and higher quality information within metropolitan areas than outside of them. VGI is a subset of user-generated content (UGC) and we discuss how our results suggest that urban biases might exist in non-geographically referenced UGC as well. Finally, because Foursquare is exclusively made up of VGI, we argue that Foursquare (and possibly other location-based social networks) has fundamentally failed to appeal to rural populations.

You Are What You Eat (and Drink): Identifying Cultural Boundaries by Analyzing Food and Drink Habits in Foursquare

Food and drink are two of the most basic needs of human beings. However, as society evolved, food and drink became also a strong cultural aspect, being able to describe strong differences among people. Traditional methods used to analyze cross-cultural differences are mainly based on surveys and, for this reason, they are very difficult to represent a significant statistical sample at a global scale. In this paper, we propose a new methodology to identify cultural boundaries and similarities across populations at different scales based on the analysis of Foursquare check-ins. This approach might be useful not only for economic purposes, but also to support existing and novel marketing and social applications. Our methodology consists of the following steps. First, we map food and drink related check-ins extracted from Foursquare into users' cultural preferences. Second, we identify particular individual preferences, such as the taste for a certain type of food or drink, e.g., pizza or sake, as well as temporal habits, such as the time and day of the week when an individual goes to a restaurant or a bar. Third, we show how to analyze this information to assess the cultural distance between two countries, cities or even areas of a city. Fourth, we apply a simple clustering technique, using this cultural distance measure, to draw cultural boundaries across countries, cities and regions.

The Semantics of Microposts

The call for this special issue on the Semantics of Microposts resulted from discussions initiated by the first Making Sense of Microposts workshop. The workshop was born out of the recognition of the invaluable knowledge contained in the vast, heterogeneous, distributed, continuously updating data store comprised of microposts – very small chunks of typically instantaneously published data, posted via social media and other communication channels that restrict publication unit size. Microposts continue to grow in popularity as a means of communication. This is despite, or maybe because of, the restrictions in post size (typically 140/160 characters – Twitter/SMS, and/or associated, small multi-media elements, e.g., short YouTube videos, Instagram photos, Pinterest pins, Foursquare check-ins, g+ chats, Facebook likes). Near permanent connectivity and the advent of inexpensive smartphones, and specialised apps and short codes for microblogging via even feature phones, has resulted in an explosion in the generation of microposts, as they make possible near real-time dissemination of news and events, both public and personal. Twitter, for instance, reported as at 2014, 78% of its active users tweeting from a phone.1 Facebook reported for Mar 2014, 609 million users active via a mobile device, out of a total of 802 million active daily.2

Capturing Urban Dynamics with Scarce Check-In Data

The authors describe their analysis of three months' worth of collected human-physical environment interactions (Foursquare check-in) data for a medium-sized city in Greece. Their analysis shows that although the data generated by the citizens is scarce (on average, fewer than 10 users checked in at any time during the day), they could use it to build a good model of the city's dynamics. The authors discuss how this information can be used to guide visitors in the city or to provide innovative services for the city's inhabitants using the cloud.