User's Home Location Research Articles

Online Social Network User Home Location Inference Based on Heterogeneous Networks

Online Social Network User Home Location Inference Based on Heterogeneous Networks

An overview of microblog user geolocation methods

Geographical locations of microblog users are essential for user profiling, event localization and target advertising, to name a few. Automatic identification of user locations from the corresponding microblogs has been explored for decades. This paper presents a survey of existing studies on the problem of microblog user geolocalisation. Specifically, we present a general framework for the geolocalisation of microblog users. In this framework, the location-related information is presented along with the overview of the microblogging platforms. Furthermore, the main steps of the framework are introduced in detail. The state-of-the-art geolocalisation methods are categorized into three classes: the text-based methods, the network-based methods, and the multiview-based methods. Moreover, the advantages and limitations of the existing methods are analyzed theoretically and experimentally. Survey findings conclude that multiview-based methods are superior to the text-based methods as well as the network-based methods. Besides that, existing user geolocalisation methods cannot capture the user's home location change, resulting in the misjudged results. Also, how to locate users from multiple social platforms is relatively unaddressed. Hence, the geolocalization of users across multiple social platforms is one of the problems that deserve further research.

Computing value of spatiotemporal information

Location data from mobile devices is a sensitive yet valuable commodity for location-based services and advertising. We investigate the intrinsic value of location data in the context of strong privacy, where location information is only available from end users via purchase. We present an algorithm to compute the expected value of location data from a user, without access to the specific coordinates of the location data point. We use decision-theoretic techniques to provide a principled way for a potential buyer to make purchasing decisions about private user location data. We illustrate our approach in three scenarios: the delivery of targeted ads specific to a user's home location, the estimation of traffic speed, and the prediction of location. In all three cases, the methodology leads to quantifiably better purchasing decisions than competing approaches.

Finding cultural heritage traces from modern social media

Finding cultural heritage traces from modern social media

A Survey of Location Prediction on Twitter

Locations, e.g., countries, states, cities, and point-of-interests, are central to news, emergency events, and people's daily lives. Automatic identification of locations associated with or mentioned in documents has been explored for decades. As one of the most popular online social network platforms, Twitter has attracted a large number of users who send millions of tweets on daily basis. Due to the world-wide coverage of its users and real-time freshness of tweets, location prediction on Twitter has gained significant attention in recent years. Research efforts are spent on dealing with new challenges and opportunities brought by the noisy, short, and context-rich nature of tweets. In this survey, we aim at offering an overall picture of location prediction on Twitter. Specifically, we concentrate on the prediction of user home locations, tweet locations, and mentioned locations. We first define the three tasks and review the evaluation metrics. By summarizing Twitter network, tweet content, and tweet context as potential inputs, we then structurally highlight how the problems depend on these inputs. Each dependency is illustrated by a comprehensive review of the corresponding strategies adopted in state-of-the-art approaches. In addition, we also briefly review two related problems, i.e., semantic location prediction and point-of-interest recommendation. Finally, we list future research directions.

The Impact of Geographic Distance on Online Social Interactions

Online social networking services entice millions of users to spend hours every day interacting with each other. The focus of this work is to explain the effect that geographic distance has on online social interactions and, simultaneously, to understand the interplay between the social characteristics of friendship ties and their spatial properties. We analyze data from a large-scale online social network, Tuenti, with about 10 million active users: our sample includes user profiles, user home locations and online social interactions among Tuenti members. Our findings support the idea that spatial distance constraints whom users interact with, but not the intensity of their social interactions. Furthermore, friendship ties belonging to denser connected groups tend to arise at shorter spatial distances than social ties established between members belonging to different groups. Finally, we show that our findings mostly do not depend on the age of the users, although younger users seem to be slightly more constrained to shorter geographic distances. Augmenting social structure with geographic information adds a new dimension to social network analysis and a large number of theoretical investigations and practical applications can be pursued for online social systems, with many promising outcomes. As the amount of available location-based data is increasing, our findings and results open the door to future possibilities: researchers would benefit from these insights when studying online social services, while developers should be aware of these additional possibilities when building systems and applications related to online social platforms.

Home location profiling for users in social media

In this paper, we focus on the problem of estimating the home locations of users in the Twitter network. We propose a Social Tie Factor Graph (STFG) model to estimate a Twitter user's city-level location based on the user's following network, user-centric data, and tie strength. In STFG, relationships between users and locations are modeled as nodes, while attributes and correlations are modeled as factors. An efficient algorithm is proposed to learn model parameters and predict unknown relationships. We evaluate our proposed method by investigating Twitter networks. The experimental results demonstrate that our proposed method significantly outperforms several state-of-the-art methods.

Modeling Location-Based User Rating Profiles for Personalized Recommendation

This article proposes LA-LDA, a location-aware probabilistic generative model that exploits location-based ratings to model user profiles and produce recommendations. Most of the existing recommendation models do not consider the spatial information of users or items; however, LA-LDA supports three classes of location-based ratings, namely spatial user ratings for nonspatial items, nonspatial user ratings for spatial items, and spatial user ratings for spatial items. LA-LDA consists of two components, ULA-LDA and ILA-LDA, which are designed to take into account user and item location information, respectively. The component ULA-LDA explicitly incorporates and quantifies the influence from local public preferences to produce recommendations by considering user home locations, whereas the component ILA-LDA recommends items that are closer in both taste and travel distance to the querying users by capturing item co-occurrence patterns, as well as item location co-occurrence patterns. The two components of LA-LDA can be applied either separately or collectively, depending on the available types of location-based ratings. To demonstrate the applicability and flexibility of the LA-LDA model, we deploy it to both top- k recommendation and cold start recommendation scenarios. Experimental evidence on large-scale real-world data, including the data from Gowalla (a location-based social network), DoubanEvent (an event-based social network), and MovieLens (a movie recommendation system), reveal that LA-LDA models user profiles more accurately by outperforming existing recommendation models for top- k recommendation and the cold start problem.

Custodians and Custodianship in Urban Neighborhoods

The paper introduces a methodology for using a city’s administrative data to study custodianship, or those behaviors aimed at preventing physical disorder in the public space. Custodianship was operationalized through requests for city services regarding public maintenance (e.g., potholes), provided by the database generated by Boston, MA’s hotline for requesting city services (i.e., 311). Users can register with the system, permitting analysis of individual differences in custodianship ( N = 12,361), including frequency of reports, variety of issues reported, and geographical range of reports. Home location (available for N = 7,433) was combined with Census statistics to infer demographic characteristics. Most (76%) reported one case. There was no evidence that individuals specialize on a single type of issue. Most reported issues over a narrow geographic range (80% within 2 blocks of home). Homeowners were three times more likely than renters to report public issues. A technique for estimating the home locations of users is also tested.

Incentivizing the global wireless village

The wireless community networking paradigm shows great promise in achieving a global status. However, in creating a “global wireless village”, both user participation and support from traditional Internet Service Providers (ISPs) are key ingredients; for this end a viable incentive system is essential. In this paper we investigate the economic interactions in global wireless community networks with regard to users, ISPs and community providers (called mediators) with both analysis and data-driven simulation. The main contribution of this paper is threefold. First, we develop a model of the global wireless community concept as a Stackelberg game of participation at two levels (the mediator as leader, and the users and ISPs as followers). Second, we analyze equilibrium properties of the game for users, ISPs and mediator. Our main finding is that the heterogeneity of user home location relevance is necessary for an economically feasible system. Third, we support our analytical claims with simulation results on the evolution of the user population. We show that the emergence of a truly global wireless community network is indeed possible.

Mining User Home Location and Gender from Flickr Tags

Personal photos and their associated metadata reveal different aspects of our lives and, when shared online, let others have an idea about us. Automating the extraction of personal information is an arduous task but it contributes to better understanding and serving users. Here we present methods for analyzing textual metadata associated to Flickr photos that unveil users’ home location and gender. We test our techniques on a sample of 30,000 people coming from six different countries, allowing us to compare results across cultures and point out similarities and differences.