Exploring Efficiency of Data Mining Techniques for Missing Link in Online Social Network

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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Mining of Association Patterns in Social Network Data (Face Book 100 Universities) through Data Mining Techniques and Methods

Advances in Computing and Information Technology - Advances in Intelligent Systems and Computing ◽

10.1007/978-3-642-31600-5_11 ◽

2013 ◽

pp. 107-117 ◽

Cited By ~ 3

Author(s):

P. Nancy ◽

R. Geetha Ramani ◽

Shomona Gracia Jacob

Keyword(s):

Data Mining ◽

Social Network ◽

Network Data ◽

Association Patterns ◽

Data Mining Techniques ◽

Social Network Data

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Sentiment analysis of foreign tourists to Bangkok using data mining through online social network

2017 IEEE 15th International Conference on Industrial Informatics (INDIN) ◽

10.1109/indin.2017.8104921 ◽

2017 ◽

Cited By ~ 4

Author(s):

Taweesak Kuhamanee ◽

Nattaphon Talmongkol ◽

Krit Chaisuriyakul ◽

Wimol San-Um ◽

Noppadon Pongpisuttinun ◽

...

Keyword(s):

Data Mining ◽

Social Network ◽

Sentiment Analysis ◽

Online Social Network ◽

Using Data

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Developing Churn Models Using Data Mining Techniques and Social Network Analysis

10.4018/978-1-4666-6288-9 ◽

2015 ◽

Cited By ~ 10

Author(s):

Goran Klepac ◽

Robert Kopal ◽

Leo Mršić

Keyword(s):

Data Mining ◽

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Data Mining Techniques ◽

Using Data

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Analysis of Social Network using Data Mining Techniques

2020 9th International Conference System Modeling and Advancement in Research Trends (SMART) ◽

10.1109/smart50582.2020.9337153 ◽

2020 ◽

Author(s):

Shubhi Goel ◽

R.K. Dwivedi ◽

Anu Sharma

Keyword(s):

Data Mining ◽

Social Network ◽

Data Mining Techniques ◽

Using Data

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Performance Evaluation of Social Network Using Data Mining Techniques

Computational Social Networks ◽

10.1007/978-1-4471-4054-2_2 ◽

2012 ◽

pp. 25-49 ◽

Cited By ~ 1

Author(s):

Mrutyunjaya Panda ◽

Ajith Abraham ◽

Sachidananda Dehuri ◽

Manas Ranjan Patra

Keyword(s):

Data Mining ◽

Performance Evaluation ◽

Social Network ◽

Data Mining Techniques ◽

Using Data

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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.

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A Conceptual Framework for Social Network Data Security

Data Mining in Dynamic Social Networks and Fuzzy Systems - Advances in Data Mining and Database Management ◽

10.4018/978-1-4666-4213-3.ch004 ◽

2013 ◽

pp. 58-86 ◽

Cited By ~ 1

Author(s):

Sanur Sharma ◽

Vishal Bhatnagar

Keyword(s):

Data Mining ◽

Social Network ◽

Conceptual Framework ◽

Social Networking Sites ◽

Community Analysis ◽

Network Data ◽

Data Mining Techniques ◽

Social Network Data ◽

The Social ◽

Using Data

In recent times, there has been a tremendous increase in the number of social networking sites and their users. With the amount of information posted on the public forums, it becomes essential for the service providers to maintain the privacy of an individual. Anonymization as a technique to secure social network data has gained popularity, but there are challenges in implementing it effectively. In this chapter, the authors have presented a conceptual framework to secure the social network data effectively by using data mining techniques to perform in-depth social network analysis before carrying out the actual anonymization process. The authors’ framework in the first step defines the role of community analysis in social network and its various features and temporal metrics. In the next step, the authors propose the application of those data mining techniques that can deal with the dynamic nature of social network and discover important attributes of the social network. Finally, the authors map their security requirements and their findings of the network properties which provide an appropriate base for selection and application of the anonymization technique to protect privacy of social network data.

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