Analysis of Early Detection of Emerging Patterns from Social Media Networks: A Data Mining Techniques Perspective

2019 ◽  
pp. 15-25
Author(s):  
Yadala Sucharitha ◽  
Y. Vijayalata ◽  
V. Kamakshi Prasad
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Early Detection ◽  
Data Mining Techniques ◽  
Social Media Networks ◽  
Emerging Patterns

scholarly journals Early detection of cyberbullying on social media networks

2021 ◽  
Vol 118 ◽  
pp. 219-229
Author(s):  
Manuel F. López-Vizcaíno ◽  
Francisco J. Nóvoa ◽  
Victor Carneiro ◽  
Fidel Cacheda
Keyword(s):  
Social Media ◽  
Early Detection ◽  
Social Media Networks

Data Mining Techniques and Applications

2018 ◽  
Vol 9 (1) ◽  
pp. 78-97 ◽  
Author(s):  
Nayem Rahman
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Unstructured Data ◽  
The Internet ◽  
Holistic View ◽  
Data Mining Techniques ◽  
Extant Literature ◽  
Business Environments ◽  
Data Volume ◽  
Complete Set

Data mining has been gaining attention with the complex business environments, as a rapid increase of data volume and the ubiquitous nature of data in this age of the internet and social media. Organizations are interested in making informed decisions with a complete set of data including structured and unstructured data that originate both internally and externally. Different data mining techniques have evolved over the last two decades. To solve a wide variety of business problems, different data mining techniques are developed. Practitioners and researchers in industry and academia continuously develop and experiment varieties of data mining techniques. This article provides an overview of data mining techniques that are widely used in different fields to discover knowledge and solve business problems. This article provides an update on data mining techniques based on extant literature as of 2018. That might help practitioners and researchers to have a holistic view of data mining techniques.

scholarly journals A Survey of Data Mining Techniques on Information Networks

2018 ◽  
Vol 7 (2.6) ◽  
pp. 293
Author(s):  
Sadhana Kodali ◽  
Madhavi Dabbiru ◽  
B Thirumala Rao
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Information Networks ◽  
Information Network ◽  
Heterogeneous Information ◽  
Data Mining Techniques ◽  
Heterogeneous Information Networks ◽  
Web Objects ◽  
The Social ◽  
Social Media Network

An Information Network is the network formed by the interconnectivity of the objects formed due to the interaction between them. In our day-to-day life we can find these information networks like the social media network, the network formed by the interaction of web objects etc. This paper presents a survey of various Data Mining techniques that can be applicable to information networks. The Data Mining techniques of both homogeneous and heterogeneous information networks are discussed in detail and a comparative study on each problem category is showcased.

scholarly journals Mining on Social Media Data: To Determine the Personality of Unrevealed Person

2019 ◽  
Vol 8 (4) ◽  
pp. 8574-8577
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Information Mining ◽  
Social Media Data ◽  
Data Mining Techniques ◽  
Network Discovery ◽  
The Social ◽  
Mining Methods ◽  
Using Data ◽  
Media Data

The unavoidable utilization of online networking like Facebook is giving exceptional measures of social information. Information mining methods have been broadly used to separate learning from such information. The character of the person is predicted whether he is good or not by using data mining techniques from user self-made data. Mining methods are being broadly using to separate learning from such information, main examples for them are network discovery and slant investigation. Notwithstanding, there is still a lot of room to investigate as far as the occasion information (i.e., occasions with timestamps, for example, posting an inquiry, altering an article in Wikipedia, and remarking on a tweet. These occasions react users' personal conduct standards and working forms in the social media websites.

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