Identifying Trendsetters in Online Social Networks – A Machine Learning Approach

2020 ◽  
pp. 3-9
Author(s):  
Martina Fricke ◽  
Freimut Bodendorf
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Online Social Networks ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Machine Learning Approach for User Accounts Identification with Unwanted Information and data

2018 ◽  
Vol 2 (3) ◽  
pp. 119-127 ◽  
Author(s):  
Abhishek Kumar ◽  
TVM SAIRAM
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Online Social Networks ◽  
Social Media Analytics ◽  
Learning Approach ◽  
Efficient Manner ◽  
Implementation Phase ◽  
Large Numbers ◽  
The Social ◽  
Machine Learning Approach

Machine Learning used for many real-time issues in many organizations and the purpose of social media analytics machine learning models are used most prominently and to identify the genuine accounts and the information in the social media we are here with a new pattern of identification. In this pattern of the model, we are proposing some words which are hidden to identify the accounts with fake data and the some of the steps we are proposing will help to identify the fake and unwanted accounts in Facebook in an efficient manner. Clustering in machine learning will be used, and before that, we are proposing a suitable architecture and the process flow which can identify the fake and suspicious accounts in the social media. This article will be on machine learning implementations and will be working on OSN (online social networks). Our work will be more on Facebook which is maintaining more amount of accounts and identifying which are overruling the rules on privacy and protection of the user content. Machine learning supervised models will be used for text classification, and CNN of unsupervised learning performs the image classification, and the explanation will be given in the implementation phase. There are large numbers of algorithms we can consider for machine learning implementations in the social networking and here we considered mainly on CNN because of having the feasibility of implementation in different rules and we can eliminate the features we don’t need. Feature extraction is quite simple using CNN.

Detecting spammer and fake user on social networks using machine learning approach

2022 ◽  
Author(s):  
T. SudalaiMuthu ◽  
C. Dheeraj Kumar Reddy ◽  
B. Sairam Reddy ◽  
M. Lakshmi Sahithya ◽  
S. Visalaxi
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Learning Approach ◽  
Machine Learning Approach

Predicting customer quality in e-commerce social networks: a machine learning approach

2018 ◽  
Vol 13 (3) ◽  
pp. 589-603 ◽  
Author(s):  
María Teresa Ballestar ◽  
Pilar Grau-Carles ◽  
Jorge Sainz
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Learning Approach ◽  
Machine Learning Approach
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