A content-driven framework for geolocating microblog users

Cheng, Zhiyuan; Caverlee, James; Lee, Kyumin

doi:10.1145/2414425.2414427

Cited by 35 publications

(60 citation statements)

References 21 publications

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“…Recent advancements in semantic analysis [Joachims, 1998] and probabilistic topic models [Blei et al, 2003;Ramage et al, 2009] have made it feasible to infer and measure similarities between documents. These topic-based approaches have emerged in the geospatial science literature as well with researchers geolocating individuals based on the content of their social contributions [Cheng et al, 2013;Hecht et al, 2011;Li et al, 2008] and building location recommendation systems [Bao et al, 2012;Matyas and Schlieder, 2009;McKenzie et al, 2013a], to name a few. It has been shown in previous work that individual words and topics in place descriptions are indicative of geospatial location [Adams and Janowicz, 2012].…”

Section: Related Workmentioning

confidence: 99%

A weighted multi-attribute method for matching user-generated Points of Interest

McKenzie

Janowicz

Adams

2014

Cartography and Geographic Information Science

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To a large degree, the attraction of Big Data lies in the variety of its heterogeneous multi-thematic and multidimensional data sources and not merely its volume. To fully exploit this variety, however, requires conflation. This is a two step process. First, one has to establish identity relations between information entities across the different data sources; and second, attribute values have to be merged according to certain procedures which avoid logical contradictions. The first step, also called matching, can be thought of as a weighted combination of common attributes according to some similarity measures. In this work, we propose such a matching based on multiple attributes of Points of Interests (POI) from the Location-based Social Network Foursquare and the Yelp local directory service. While both contain overlapping attributes that can be use for matching, they have specific strengths and weaknesses which makes their conflation desirable. For instance, Foursquare offers information about user check-ins to places, while Yelp specializes in user-contributed reviews. We present a weighted multi-attribute matching strategy, evaluate its performance, and discuss application areas that benefit from a successful matching. Finally, we also outline how the established POI matches can be stored as Linked Data on the Semantic Web. Our strategy can automatically match 97% of randomly selected Yelp POI to their corresponding Foursquare entities.

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Section: Related Workmentioning

confidence: 99%

A weighted multi-attribute method for matching user-generated Points of Interest

McKenzie

Janowicz

Adams

2014

Cartography and Geographic Information Science

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“…Three temporal attributes of periodicity, consecutiveness, and non-uniformness [8,10,22,85,93,18] have already been used in location recommendation systems. Periodicity studies the recurrent trips in users' mobility patterns (e.g.…”

Section: Motivationmentioning

confidence: 99%

“…home-work trips). The successive attribute [89,8] states that there are some locations which are visited sequentially (e.g. going to the bar after the restaurant on Saturday night).…”

Section: Motivationmentioning

confidence: 99%

“…Intuitively, user generated content containing location names are more useful than other messages that lack spatial information; however, as the microblog messages are limited in size, extremely noisy, and lexically varied, the extraction of location information from such data is quite challenging. Based on prior studies [8,2], it is assumed that there are an adequate number of local terms (e.g. Aussie for the people of Australia) dedicated to any region which gain a high frequency in their geographical focal points while the frequency shrinks with distance from the central focal point.…”

Section: Motivationmentioning

confidence: 99%

“…CF methods have two categories; memory-based and model-based [8] and study a user's interest regarding each proposed POI. In reality, numerous approaches [79,22,38,78] attempt to demote sparsity in user-location matrix.…”

Section: Location Recommendation Using Lbsn Datamentioning

confidence: 99%

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Location Inference and Recommendation in Social Networks

Hosseini¹

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With the ubiquity of GPS-enabled smartphones, Location-Based Service (LBS) as a prominent product of social networks has become an essential part of our daily lives. People can easily socialize and share their check-in data (location, time, text, photo and etc.) via Location-Based Social Networks (LBSN). Through mining the check-in dataset, Point-Of-Interest(POI) recommendation systems assist users in exploring new attractive venues.The primary challenge regarding POI recommendation is to suggest a list of new interesting locations to the query user. Specifically, the excessive sparsity observed in user-location matrices is the main problem. The well known Collaborative Filtering (CF) methods are commonly used with other factors like geographical, social, context-oriented (e.g. text contents) and temporal effects to promote the effectiveness of the location recommendation systems. Despite the vital role of temporal influence, an insufficient amount of research has been devoted to considering the time factor in location recommendation. While we have an insufficient number of records regarding a user's check-in at a particular location, predicting the time of the visit seems more problematic. We have dedicated part of our research to study various aspects of the temporal influence in the recommendation.Additionally, we use Twitter textual contents to extract a set of spatial phrases associated with each region. Such an act enriches the textual contents of the local POIs. This process enhances location recommendation systems, as it facilitates textual similarity among POI tags and the tweet history of the query user.In short, we aim to address the problems involved with both aspects of Location Inference and Recommendation in social networks. Our research in this thesis has four parts. Firstly, we define a problem which merely considers a single temporal aspect to enhance the performance of a location recommendation task. Subsequently, we develop a probabilistic model which detects a user's temporal orientation based on visibility weights of POIs visited by her during weekday/weekend cycles. While this method is limited to a single temporal scale, the idea can be adapted to other time-related periodic cycles (e.g. daily home-work return trips). Secondly, we argue that the majority of existing methods merely concentrate on a limited number of temporal scales and neglect others. We propose a probabilistic generative model, named after Multi-aspect Time-related Influence (MATI) which employs the user's check-in log to detect her temporal mobility pattern under various scales (e.g. minute, hour, day and so on). It then performs recommendation using multi-aspect temporal correlations between the query user and proposed locations. Thirdly, we further study the role of the time factor in recommendation models. We define a new problem to jointly model a pair of heterogeneous time-related 2 effects (recency and the subset feature) in location recommendation. To address the challenges, we propose a generative model ...

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Users' location analysis based on Chinese mobile social media

Wang

Guo

Zheng

et al. 2018

Concurrency and Computation

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Summary After the rapid development for more than 20 years, Internet has gradually become the main carrier of people's information and behaviors in people's daily life. In addition, the innovation and popularization of smartphone GPS makes user location information much more available and accurate, helping it to create remarkable values by which people are attracted to focus on social media–related data mining and applications. However, because of the sparsity of social media geographical information, direct inferences of locations have plenty of difficulties. Under the background of big data, this research has revised the UGC‐LI model in the preprocess of texts and the creation of the local dictionaries in which we take existed local dictionaries from the Internet into consideration, with the purpose of the inferences for users' and texts' locations. At the time of writing, through the crawler, we acquire users' personal information, the blog content, and customer relationships' (follows, fans) information more than 410 331 pieces from Sina Weibo. The experimental results show that the recall rate of the user location inference is 86.0%, whereas the precise rate is 77.4%, and the accuracy of text posted location inference is 66.8%. Compared with some other related algorithms, this revised model has comparatively better results in location inference for users and text publication.

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A content-driven framework for geolocating microblog users

Cited by 35 publications

References 21 publications

A weighted multi-attribute method for matching user-generated Points of Interest

A weighted multi-attribute method for matching user-generated Points of Interest

Location Inference and Recommendation in Social Networks

Users' location analysis based on Chinese mobile social media

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