Social Networking Data Analysis Tools and Services

2018 ◽  
pp. 19-34
Author(s):  
Gopal Krishna
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Data Analysis ◽  
Social Networking ◽  
Data Interpretation ◽  
Product Reviews ◽  
Huge Amount ◽  
Data Mining Techniques ◽  
Business Interests ◽  
Contents Sharing

Social networks have drawn remarkable attention from IT professionals and researchers in data sciences. They are the most popular medium for social interaction. Online social networking (OSN) can be defined as involving networking for fun, business, and communication. Social networks have emerged as universally accepted communication means and boomed in turning this world into a global town. OSN media are generally known for broadcasting information, activities posting, contents sharing, product reviews, online pictures sharing, professional profiling, advertisements and ideas/opinion/sentiment expression, or some other stuff based on business interests. For the analysis of the huge amount of data, data mining techniques are used for identifying the relevant knowledge from the huge amount of data that includes detecting trends, patterns, and rules. Data mining techniques, machine learning, and statistical modeling are used to retrieve the information. For the analysis of the data, three methods are used: data pre-processing, data analysis, and data interpretation.

Social Networking Data Analysis Tools and Services

2021 ◽  
pp. 406-421
Author(s):  
Gopal Krishna
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Data Analysis ◽  
Social Networking ◽  
Data Interpretation ◽  
Product Reviews ◽  
Huge Amount ◽  
Data Mining Techniques ◽  
Business Interests ◽  
Contents Sharing

Social networks have drawn remarkable attention from IT professionals and researchers in data sciences. They are the most popular medium for social interaction. Online social networking (OSN) can be defined as involving networking for fun, business, and communication. Social networks have emerged as universally accepted communication means and boomed in turning this world into a global town. OSN media are generally known for broadcasting information, activities posting, contents sharing, product reviews, online pictures sharing, professional profiling, advertisements and ideas/opinion/sentiment expression, or some other stuff based on business interests. For the analysis of the huge amount of data, data mining techniques are used for identifying the relevant knowledge from the huge amount of data that includes detecting trends, patterns, and rules. Data mining techniques, machine learning, and statistical modeling are used to retrieve the information. For the analysis of the data, three methods are used: data pre-processing, data analysis, and data interpretation.

scholarly journals Research of various Data Mining Techniques for IoT Applications

2019 ◽  
Vol 8 (2S11) ◽  
pp. 1083-1086
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Internet Of Things ◽  
Important Task ◽  
The Internet ◽  
Huge Amount ◽  
Data Mining Techniques ◽  
Amount Of Information ◽  
Iot Applications ◽  
Iot Devices

In recent years everything is connected and passing through the internet, but Internet of Things (IOT), which will change all aspects of our lives and future. While the things are connected to the internet, they will generate the huge amount of information which has to be processed. The information that gathered from various IoT devices has to be recognized and organized according to the environments of their type. To recognize and organize the data gathered from different things, the important task to be played is making things passing through different Data Mining Techniques (DMT). In this article, we mainly focus on analysis of various Data Mining Techniques over the data that has been generated by the IOT Devices which are connected over the internet using DBSCAN Technique. And also performed review over different Data Mining Techniques for Data Analysis

scholarly journals Data Mining Technics and Cyber Hygiene Behaviors in Social Media

2021 ◽  
Vol 2 (2) ◽  
pp. 2503-2515
Author(s):  
Hanna Martyniuk ◽  
Valeriy Kozlovskiy ◽  
Serhii Lazarenko ◽  
Yuriy Balanyuk
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Media ◽  
Social Networking ◽  
Social Networking Sites ◽  
Personal Information ◽  
Data Sets ◽  
Personal Privacy ◽  
Data Mining Techniques ◽  
Social Network Data

The authors present in this work information about social media and data mining usage for that. It is represented information about social networking sites, where Facebook dominates the industry by boasting an account of 85% of the internet user’s worldwide. Applying data mining techniques to large social media data sets has the potential to continue to improve search results for everyday search engines, realize specialized target marketing for businesses, help psychologist study behavior, provide new insights into social structure for sociologists, personalize web services for consumers, and even help detect and prevent spam for all of us. The most common data mining applications related to social networking sites is represented. Authors have also gave information about different data mining techniques and list of these techniques. It is important to protect personal privacy when working with social network data. Recent publications highlight the need to protect privacy as it has been shown that even anonymizing this type of data can still reveal personal information when advanced data analysis techniques are used. A whole range of different threat of social networks is represented. Authors explain cyber hygiene behaviors in social networks, such as backing up data, identity theft and online behavior.

scholarly journals A Survey of Data Mining Techniques for Social Media Analysis

2014 ◽  
Vol 2014 ◽  
Author(s):  
Mariam Adedoyin-Olowe ◽  
Mohamed Medhat Gaber ◽  
Frederic Stahl
Keyword(s):  
Data Mining ◽  
Social Network ◽  
Data Analysis ◽  
Social Network Sites ◽  
Statistical Modelling ◽  
Data Interpretation ◽  
Media Analysis ◽  
Data Mining Techniques ◽  
Useful Knowledge ◽  
Wide Range

Social network has gained remarkable attention in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for information, news and opinion of other users on diverse subject matters. The heavy reliance on social network sites causes them to generate massive data characterised by three computational issues namely; size, noise and dynamism. These issues often make social network data very complex to analyse manually, resulting in the pertinent use of computational means of analysing them. Data mining provides a wide range of techniques for detecting useful knowledge from massive datasets like trends, patterns and rules [44]. Data mining techniques are used for information retrieval, statistical modelling and machine learning. These techniques employ data pre-processing, data analysis, and data interpretation processes in the course of data analysis. This survey discusses different data mining techniques used in mining diverse aspects of the social network over decades going from the historical techniques to the up-to-date models, including our novel technique named TRCM. All the techniques covered in this survey are listed in the Table.1 including the tools employed as well as names of their authors. Comment: 25 pages, 9 figures

A Systematic Review About Prediction of Academic Behavior Through Data Mining Techniques

2020 ◽  
Vol 17 (11) ◽  
pp. 5162-5166
Author(s):  
Puninder Kaur ◽  
Amandeep Kaur ◽  
Rajwinder Kaur
Keyword(s):  
Systematic Review ◽  
Data Mining ◽  
Data Analysis ◽  
Educational Data Mining ◽  
Prediction Methods ◽  
Time Data ◽  
Academic Behavior ◽  
Data Mining Techniques ◽  
Real Time Data ◽  
Extract Information

In the IT world, predicting the academic performance of the huge student population poses a big challenge. Educational data mining techniques significantly contribute in providing solution to this problem. There are several prediction methods available for data classification and clustering, to extract information and provide accurate results. In this paper, different prediction methodologies are highlighted for the prediction of real-time data analysis of dynamic academic behavior of the students. The main focus is to provide brief knowledge about all data mining techniques and highlight dissimilarities among various methods in order to provide the best results for the students.

A Classification Framework for Data Mining Applications in Criminal Science and Investigations

2020 ◽  
pp. 277-293
Author(s):  
Mahima Goyal ◽  
Vishal Bhatnagar ◽  
Arushi Jain
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Data Mining Techniques ◽  
Road Map ◽  
Classification Framework ◽  
Challenges And Opportunities ◽  
Crucial Information ◽  
Tools And Techniques ◽  
Day By Day ◽  
Mining Tools

The importance of data analysis across different domains is growing day by day. This is evident in the fact that crucial information is retrieved through data analysis, using different available tools. The usage of data mining as a tool to uncover the nuggets of critical and crucial information is evident in modern day scenarios. This chapter presents a discussion on the usage of data mining tools and techniques in the area of criminal science and investigations. The application of data mining techniques in criminal science help in understanding the criminal psychology and consequently provides insight into effective measures to curb crime. This chapter provides a state-of-the-art report on the research conducted in this domain of interest by using a classification scheme and providing a road map on the usage of various data mining tools and techniques. Furthermore, the challenges and opportunities in the application of data mining techniques in criminal investigation is explored and detailed in this chapter.

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.

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