Mental illness diagnosis from social network data using effective machine learning technique

2021 ◽  
pp. 231-238
Author(s):  
Shivani Singh ◽  
Sandhya Tarar
Keyword(s):  
Machine Learning ◽  
Mental Illness ◽  
Social Network ◽  
Network Data ◽  
Machine Learning Technique ◽  
Social Network Data ◽  
Learning Technique

scholarly journals Depression detection from social network data using machine learning techniques

2018 ◽  
Vol 6 (1) ◽  
Author(s):  
Md. Rafiqul Islam ◽  
Muhammad Ashad Kabir ◽  
Ashir Ahmed ◽  
Abu Raihan M. Kamal ◽  
Hua Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Machine Learning Techniques ◽  
Network Data ◽  
Social Network Data ◽  
Learning Techniques ◽  
Depression Detection

scholarly journals A Machine Learning-based Online Social Network Analysis for 360-degree User Profiling

2019 ◽  
Vol 9 (2S2) ◽  
pp. 992-998
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Social Networking Sites ◽  
Online Social Network ◽  
User Profiling ◽  
Machine Learning Technique ◽  
The People ◽  
Learning Technique ◽  
The Government ◽  
Personal Interests

This paper aims to analyse the online social network for reconnaissance of people for finding their potentiality. The work considers one of the famous social networking sites, Twitter, where people express their thoughts and ideas, through which the people in the site knowingly or unknowingly reveal the information about themselves such as personal interests, likes and dislikes. The Machine Learning technique facilitates the work to mine the tweet data of a person to get his/her 360-degree profiling. This profiling is helpful to identify the personality type of a person, which is essential for the Government to identify the people involved in spreading the fake news in Twitter.

Abstract #1249: Novel Use of Physician Social Network Data to Map the Gap in the Endocrinology Workforce

2015 ◽  
Vol 21 ◽  
pp. 301
Author(s):  
Armand Krikorian ◽  
Lily Peng ◽  
Zubair Ilyas ◽  
Joumana Chaiban
Keyword(s):  
Social Network ◽  
Network Data ◽  
Social Network Data

The Multilevel p2 Model

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 42-47 ◽  
Author(s):  
Bonne J. H. Zijlstra ◽  
Marijtje A. J. van Duijn ◽  
Tom A. B. Snijders
Keyword(s):  
Social Network ◽  
Network Data ◽  
Mcmc Algorithm ◽  
Independent Observation ◽  
Single Observation ◽  
Multiple Networks ◽  
Social Network Data ◽  
Multiple Observations ◽  
Practical Support ◽  
The Social

The p 2 model is a random effects model with covariates for the analysis of binary directed social network data coming from a single observation of a social network. Here, a multilevel variant of the p 2 model is proposed for the case of multiple observations of social networks, for example, in a sample of schools. The multilevel p 2 model defines an identical p 2 model for each independent observation of the social network, where parameters are allowed to vary across the multiple networks. The multilevel p 2 model is estimated with a Bayesian Markov Chain Monte Carlo (MCMC) algorithm that was implemented in free software for the statistical analysis of complete social network data, called StOCNET. The new model is illustrated with a study on the received practical support by Dutch high school pupils of different ethnic backgrounds.

Sign in / Sign up

Export Citation Format

Share Document