DISCOVERING AND EXPANSION THE IRREGULAR MANNERS OF USERS IN ONLINE SOCIAL NETWORKS USING DATA MINING TECHNIQUES

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.

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Detection of Behavior Patterns through Social Networks like Twitter, using Data Mining techniques as a method to detect Cyberbullying

2018 7th International Conference On Software Process Improvement (CIMPS) ◽

10.1109/cimps.2018.8625625 ◽

2018 ◽

Cited By ~ 1

Author(s):

Freddy Tapia ◽

Cristina Aguinaga ◽

Roger Luje

Keyword(s):

Data Mining ◽

Social Networks ◽

Behavior Patterns ◽

Data Mining Techniques ◽

Using Data

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Building Context-Aware E-Commerce Systems

Encyclopedia of E-Business Development and Management in the Global Economy ◽

10.4018/978-1-61520-611-7.ch103 ◽

2010 ◽

pp. 1021-1029

Author(s):

Anahit Martirosyan ◽

Thomas Tran ◽

Azzedine Boukerche

Keyword(s):

Data Mining ◽

Social Networks ◽

Context Aware ◽

Work Context ◽

Privacy Issue ◽

Data Mining Techniques ◽

Proposed Model ◽

Use Context ◽

Using Data ◽

Different Levels

Context is any information/knowledge about an application and user that can be used by an e-commerce system to provide efficient services to the users of the system. In this article, we propose to extend usage of context as compared to previously designed context-aware e-commerce systems. While in previous work, context was mainly considered for mobile e-commerce systems, we propose to build and use context for e-commerce systems in general. The context is employed to tailor an e-commerce application to the preferences and needs of users and provide insights into purchasing activities of users and particular e-commerce stores by means of using Data Mining techniques. This article proposes a model of context that includes micro-, macro- and domain contexts that constitute knowledge about the application and its user on different levels of granularity. The article also proposes a technique for extracting groups in social networks. This knowledge is part of macro-context in the proposed model of context. Moreover, the article discusses some of the challenges of incorporating context with e-commerce systems, emphasizing on the privacy issue, with an ultimate goal of developing intelligent e-commerce systems.

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Detecting Pharmaceutical Spam in Microblog Messages

Data Mining ◽

10.4018/978-1-4666-2455-9.ch073 ◽

2013 ◽

pp. 1407-1420

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.

Download Full-text