Data Mining Location-Based Social Networks for Geospatial Discovery

2013 ◽  
pp. 2006-2019 ◽  
Author(s):  
Edward Pultar
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
Geographical Location ◽  
Geographic Information ◽  
Space Time ◽  
Specific Location ◽  
Local Residents ◽  
Location Based Social Network ◽  
Location Based Social Networks

Modern, Internet-based social networks contain a wealth of information about each member. An integral part of an individual’s online profile is their Volunteered Geographic Information (VGI) such as a user’s current geographical location. Social network members in different cities, countries, or continents engage in different activities due to accessibility, economy, culture, or other factors. The work here focuses on data mining separate groups of social network profiles according to their geography in order to discover information about a place. This results in keywords associated with a specific location and provides an automated way to describe a place in an up to date fashion based upon its current local residents. Location-Based Social Network (LBSN) profiles from four different places are analyzed here and the results are presented as they relate to space, time, and activities.

Data Mining Location-Based Social Networks for Geospatial Discovery

2012 ◽  
pp. 204-218
Author(s):  
Edward Pultar
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
Geographical Location ◽  
Geographic Information ◽  
Space Time ◽  
Specific Location ◽  
Local Residents ◽  
Location Based Social Network ◽  
Location Based Social Networks

Modern, Internet-based social networks contain a wealth of information about each member. An integral part of an individual’s online profile is their Volunteered Geographic Information (VGI) such as a user’s current geographical location. Social network members in different cities, countries, or continents engage in different activities due to accessibility, economy, culture, or other factors. The work here focuses on data mining separate groups of social network profiles according to their geography in order to discover information about a place. This results in keywords associated with a specific location and provides an automated way to describe a place in an up to date fashion based upon its current local residents. Location-Based Social Network (LBSN) profiles from four different places are analyzed here and the results are presented as they relate to space, time, and activities.

scholarly journals Correlation between Triadic Closure and Homophily Formed over Location-Based Social Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Nauman Ali Khan ◽  
Wuyang Zhou ◽  
Mudassar Ali Khan ◽  
Ahmad Almogren ◽  
Ikram Ud Din
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Research Direction ◽  
Social Actors ◽  
The Social ◽  
Depth Analysis ◽  
Location Based Social Network ◽  
Triadic Closure ◽  
Location Based Social Networks ◽  
The Given

Social Internet of Things (SIoT) is a variation of social networks that adopt the property of peer-to-peer networks, in which connections between the things and social actors are automatically established. SIoT is a part of various organizations that inherit the social interaction, and these organizations include industries, institutions, and other establishments. Triadic closure and homophily are the most commonly used measures to investigate social networks’ formation and nature, where both measures are used exclusively or with statistical models. The triadic closure patterns are mapped for actors’ communication behavior over a location-based social network, affecting the homophily. In this study, we investigate triads emergence in homophilic social networks. This evaluation is based on the empirical review of triads within social networks (SNs) formed on Big Data. We utilized a large location-based dataset for an in-depth analysis, the Chinese telecommunication-based anonymized call detail records (CDRs). Two other openly available datasets, Brightkite and Gowalla, were also studied. We identified and proposed three social triad classes in a homophilic network to feature the correlation between social triads and homophily. The study opened a promising research direction that relates the variation of homophily based on closure triads nature. The homophilic triads are further categorized into transitive and intransitive groups. As our concluding research objective, we examined the relative triadic throughput within a location-based social network for the given datasets. The research study attains significant results highlighting the positive connection between homophily and a specific social triad class.

scholarly journals Location prediction in location-based social networks

2017 ◽  
Vol 7 (3) ◽  
pp. 149-156
Author(s):  
Mucahit Baydar ◽  
Songul Albayrak
Keyword(s):  
Social Networks ◽  
Wireless Networks ◽  
Social Network ◽  
Mobile Devices ◽  
New Method ◽  
Time Range ◽  
Location Prediction ◽  
The Social ◽  
Location Based Social Network ◽  
Location Based Social Networks

AbstractDevelopments in mobile devices and wireless networks have led to the increasing popularity of location-based social networks. These networks allow users to explore new places, share their location, videos and photos and make friends. They give information about the mobility of users, which can be used to improve the networks. This paper studies the problem of predicting the next check-in of users of location-based social networks. For an accurate prediction, we first analyse the datasets that are obtained from the social networks, Foursquare and Gowalla. Then we obtain some features like place popularity, place popular time range, place distance to user’s home, user’s past visits, category preferences and friendships ,which are used for prediction and deeper understanding of the user behaviours. We use each feature individually, and then in combination, using the new method. Finally, we compare the acquired results and observe the improvement with the new method.Keywords: Location prediction, location-based social network, check-in data.

scholarly journals Joint Selection of Influential Users and Locations under Target Region in Location-Based Social Networks

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 709
Author(s):  
Khurshed Ali ◽  
Cheng-Te Li ◽  
Yi-Shin Chen
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Maximization Problem ◽  
Target Region ◽  
Specific Location ◽  
Influential Users ◽  
Location Based Social Networks ◽  
Location Type ◽  
Selection Of ◽  
Product Promotion

Influence Maximization problem, selection of a set of users in a social network to maximize the influence spread, has received ample research attention in the social network analysis domain due to its practical applications. Although the problem has been extensively studied, existing works have neglected the location’s popularity and importance along with influential users for product promotion at a particular region in Location-based Social Networks. Real-world marketing companies are more interested in finding suitable locations and influential users in a city to promote their product and attract as many users as possible. In this work, we study the joint selection of influential users and locations within a target region from two complementary perspectives; general and specific location type selection perspectives. The first is to find influential users and locations at a specified region irrespective of location type or category. The second perspective is to recommend locations matching location preference in addition to the target region for product promotion. To address general and specific location recommendations and influential users, we propose heuristic-based methods that effectively find influential users and locations for product promotion. Our experimental results show that it is not always an optimal choice to recommend locations with the highest popularity values, such as ratings, check-ins, and so, which may not be a true indicator of location popularity to be considered for marketing. Our results show that not only influential users are helpful for product promotion, but suitable influential locations can also assist in promoting products in the target region.

Knowledge Discovery from Online Communities

Data Mining ◽  
2013 ◽  
pp. 1230-1252
Author(s):  
Luca Cagliero ◽  
Alessandro Fiori
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
User Behavior ◽  
Network Models ◽  
Social Model ◽  
User Generated Content ◽  
Growth Trend ◽  
User Behaviors ◽  
Application Systems

This chapter presents an overview of social network features such as user behavior, social models, and user-generated content to highlight the most notable research trends and application systems built over such appealing models and online media data. It first describes the most popular social networks by analyzing the growth trend, the user behaviors, the evolution of social groups and models, and the most relevant types of data continuously generated and updated by the users. Next, the most recent and valuable applications of data mining techniques to social network models and user-generated content are presented. Discussed works address both social model extractions tailored to semantic knowledge inference and automatic understanding of the user-generated content. Finally, prospects of data mining research on social networks are provided as well.

Detecting Pharmaceutical Spam in Microblog Messages

2011 ◽  
pp. 101-115
Author(s):  
Kathy J. Liszka ◽  
Chien-Chung Chan ◽  
Chandra Shekar
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Cell Phones ◽  
Pharmaceutical Products ◽  
Spam Detection ◽  
Data Mining Techniques ◽  
Flow Through ◽  
Special Category

Microblogs are one of a growing group of social network tools. Twitter is, at present, one of the most popular forums for microblogging in online social networks, and the fastest growing. Fifty million messages flow through servers, computers, and cell phones on a wide variety of topics exchanged daily. With this considerable volume, Twitter is a natural and obvious target for spreading spam via the messages, called tweets. The challenge is how to determine if a tweet is a spam or not, and more specifically a special category advertising pharmaceutical products. The authors look at the essential characteristics of spam tweets and what makes microblogging spam unique from email or other types of spam. They review methods and tools currently available to identify general spam tweets. Finally, this work introduces a new methodology of applying text mining and data mining techniques to generate classifiers that can be used for pharmaceutical spam detection in the context of microblogging.

Emergent Data Mining Tools for Social Network Analysis

2015 ◽  
pp. 1539-1556
Author(s):  
Dhiraj Murthy ◽  
Alexander Gross ◽  
Alex Takata
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Online Social Networks ◽  
Data Extraction ◽  
Future Directions ◽  
Effective Development ◽  
Mining Tools

This chapter identifies a number of the most common data mining toolkits and evaluates their utility in the extraction of data from heterogeneous online social networks. It introduces not only the complexities of scraping data from the diverse forms of data manifested in these sources, but also critically evaluates currently available tools. This analysis is followed by a presentation and discussion on the development of a hybrid system, which builds upon the work of the open-source Web-Harvest framework, for the collection of information from online social networks. This tool, VoyeurServer, attempts to address the weaknesses of tools identified in earlier sections, as well as prototype the implementation of key functionalities thought to be missing from commonly available data extraction toolkits. The authors conclude the chapter with a case study and subsequent evaluation of the VoyeurServer system itself. This evaluation presents future directions, remaining challenges, and additional extensions thought to be important to the effective development of data mining tools for the study of online social networks.

The Alt-social Network of Natalie Bookchin’s Testament

2016 ◽  
Vol 18 (5) ◽  
pp. 459-477
Author(s):  
Sarah Whitcomb Laiola
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Online Social Networks ◽  
Social Web ◽  
User Interactions ◽  
Media Criticism ◽  
Direct Translation ◽  
The Social

This article addresses issues of user precarity and vulnerability in online social networks. As social media criticism by Jose van Dijck, Felix Stalder, and Geert Lovink describes, the social web is a predatory system that exploits users’ desires for connection. Although accurate, this critical description casts the social web as a zone where users are always already disempowered, so fails to imagine possibilities for users beyond this paradigm. This article examines Natalie Bookchin’s composite video series, Testament, as it mobilizes an alt-(ernative) social network of vernacular video on YouTube. In the first place, the alt-social network works as an iteration of “tactical media” to critically reimagine empowered user-to-user interactions on the social web. In the second place, it obfuscates YouTube’s data-mining functionality, so allows users to socialize online in a way that evades their direct translation into data and the exploitation of their social labor.

Data Mining for Social Network Analysis Using a CLIQUE Algorithm

2019 ◽  
pp. 160-187
Author(s):  
Phu Ngoc Vo ◽  
Tran Vo Thi Ngoc
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
The Social ◽  
The World ◽  
Commercial Applications ◽  
Clique Algorithm

Many different areas of computer science have been developed for many years in the world. Data mining is one of the fields which many algorithms, methods, and models have been built and applied to many commercial applications and research successfully. Many social networks have been invested and developed in the strongest way for the recent years in the world because they have had many big benefits as follows: they have been used by lots of users in the world and they have been applied to many business fields successfully. Thus, a lot of different techniques for the social networks have been generated. Unsurprisingly, the social network analysis is crucial at the present time in the world. To support this process, in this book chapter we have presented many simple concepts about data mining and social networking. In addition, we have also displayed a novel model of the data mining for the social network analysis using a CLIQUE algorithm successfully.

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