Location-Based Social Networks: Users

Zheng, Yu

doi:10.1007/978-1-4614-1629-6_8

Cited by 164 publications

(98 citation statements)

References 52 publications

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“…Location Based Social Networks (Zheng, 2011), like for example Twitter or Instagram, are an important source of information for studying the geospatial and temporal behavior of a large number of users. The data that they provide include the spatio-temporal patterns that users generate while interacting with the different locations inside a geographical area and the events that occur within it.…”

Section: Introductionmentioning

confidence: 99%

Discovery of spatio-temporal patterns from location-based social networks

Béjar

Álvarez

García

et al. 2015

Journal of Experimental & Theoretical Artificial Intelligen

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Location Based Social Networks (LBSN) like Twitter or Instagram are a good source for user spatio-temporal behavior. These networks collect data from users in such a way that they can be seen as a set of collective and distributed sensors of a geographical area. A low rate sampling of user's location information can be obtained during large intervals of time that can be used to discover complex patterns, including mobility profiles, points of interest or unusual events. These patterns can be used as the elements of a knowledge base for different applications in different domains like mobility route planning, touristic recommendation systems or city planning.The aim of this paper is twofold, first to analyze the frequent spatio-temporal patterns that users share when living and visiting a city. This behavior is studied by means of frequent itemsets algorithms in order to establish some associations among visits that can be interpreted as interesting routes or spatio-temporal connections. Second, to analyze how the spatio-temporal behavior of a large number of users can be segmented in different profiles. These behavioral profiles are obtained by means of clustering algorithms that show the different patterns of behavior of visitors and citizens.The data analyzed was obtained from the public data feeds of Twitter and Instagram within an area surrounding the cities of Barcelona and Milan for a period of several months. The analysis of these data shows that these kind of algorithms can be successfully applied to data from any city (or general area) to discover useful patterns that can be interpreted on terms of singular places and areas and their temporal relationships.

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Section: Introductionmentioning

confidence: 99%

Discovery of spatio-temporal patterns from location-based social networks

Béjar

Álvarez

García

et al. 2015

Journal of Experimental & Theoretical Artificial Intelligen

View full text Add to dashboard Cite

show abstract

“…At present, many researches are focused on behavior traces Analysis, such as LBSN (Location Based on Social Networks) [7] and POI (Points of Interest) [8]. This analysis has two parameters: one is the latitude and longitude coordinates; the other one is occurrence time.…”

Section: Introductionmentioning

confidence: 99%

The Similarity Calculation of E-commerce User Behaviors with Petri Net

Pan¹,

Ma²,

Wang³

2017

dtcse

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The similarity of user behaviors is conductive to classify users, cluster different groups, and generate the behavior model. It is of great significance to achieve a finegrained analysis of user behaviors. In this paper, a novel method of computing the similarity of e-commerce user behaviors is presented based on Petri nets. This method involves the following aspects. We first use Petri nets to model user behaviors. Then, we calculate the similarities of behavioral sequences and behavioral data, respectively. Finally, we get the results by comparing the similarities of users. Our experiment presents that this method can reflect data features and behavioral sequences by places and transitions of Petri nets. Above all, it shows a higher accuracy of the similarity calculation.

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“…As such, there are many opportunities to gain fundamental knowledge about user behavior analyzing these data at various levels of spatiotemporal resolution. Researchers are realizing the potential to harness the rich information provided by the location-based data, which have already enabled many novel applications, such as recommendation system for physical locations (or activity) (Zheng et al, 2010;Chang and Sun, 2011;Bao et al, 2012), recommending potential customers or friend (Zheng, 2011;Saez-Trumper et al, 2012), determining popular travel routes in a city (Wei et al, 2012), discovering mobility and activity choice behavior (Cheng et al, 2011;Noulas et al, 2012;Hasan et al, 2013;Pianese et al, 2013), activity recognition and classification (Lian and Xie, 2011;Hasan and Ukkusuri, 2014), estimating urban travel demand and traffic flow (Hasan, 2013;Liu et al, 2014;Wu et al, 2014), and modeling the influence of friendship on mobility patterns (Cho et al, 2011;Wang et al, 2011). In this paper, we analyze a dataset from a social media check-in service to understand the extent of social influence on individual activity behavior.…”

Section: Introductionmentioning

confidence: 99%

Understanding Social Influence in Activity Location Choice and Lifestyle Patterns Using Geolocation Data from Social Media

2016

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Social media check-in services have enabled people to share their activity-related choices, providing a new source of human activity and social networks data. Geolocation data from these services offer us information, in new ways, to understand social influence on individual choices. In this paper, we investigate the extent of social influence on individual activity and lifestyle choices from social media check-in data. We first collect user check-ins and their social network information by linking two social media systems (Twitter and Foursquare) and analyze the structure of the underlying social network. We next infer user check-in and geo lifestyle patterns using topic models. We analyze the correlation between the social relationships and individual-level patterns. We investigate whether or not two individuals have similar activity choice and geo lifestyle patterns, if they are socially connected. We find that the similarity between two users, in their check-in behavior and lifestyle patterns, increases with the increase of the friendship probability.

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Location-Based Social Networks: Users

Cited by 164 publications

References 52 publications

Discovery of spatio-temporal patterns from location-based social networks

Discovery of spatio-temporal patterns from location-based social networks

The Similarity Calculation of E-commerce User Behaviors with Petri Net

Understanding Social Influence in Activity Location Choice and Lifestyle Patterns Using Geolocation Data from Social Media

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