Depression detection from social network data using machine learning techniques

Depression is the world’s fourth leading disease and will be in the second in 2020 according to the statistics of World Health Organization.Depression affects many people irrespective of their age, geographic location, demographic or social position and more commonly affects females than males.Depression is a mental disorder which can impair many facets of human life. Though not easily detected it has intense and wide-ranging impressions. Although many researchers explored numerous techniques in predicting depression, still there is no improvement and the generations are facing higher rate of depression. It is believed that the depression detection algorithms can be more accurate and their performance can be better if they rely on artificial intelligence. On considering these factors, it is planned to perform a survey on the application of various machine learning techniques that have been used in the domain of sentimental analysis for depression detection.

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Social Network Modelling using tools of Statistical Mechanics

10.31219/osf.io/8we3v ◽

2020 ◽

Author(s):

Ashish Menon ◽

Nithin K Rajendran ◽

Anish Chandrachud

Keyword(s):

Machine Learning ◽

Social Network ◽

Phase Space ◽

Statistical Mechanics ◽

Disease Transmission ◽

Machine Learning Techniques ◽

Network Modelling ◽

Business Partnership ◽

Learning Techniques ◽

Statistical Mechanism

The objective of this paper is to study a treatment to social network analysis using the principles of statistical mechanics. After revisiting the popular models and random graph frameworks of complex networks, a formalism to statistical mechanism based on the conventional concepts like phase space, interactions and ensembles is devised. Specific machine learning techniques are employed for the purpose of figuring out the relevant phase-space equations. Thereafter, specific applications of the formalism is explored in the context of business partnership optimization and disease transmission. Several analogues with the statistical mechanics treatment of thermodynamics have also been made.

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Detecting Private Information in Large Social Network using mixed Machine Learning Techniques

10.20944/preprints201901.0077.v1 ◽

2019 ◽

Author(s):

Pasquale De Luca

Keyword(s):

Machine Learning ◽

Social Network ◽

Mobile Phones ◽

Private Information ◽

Current Problem ◽

Personal Information ◽

Private Life ◽

Machine Learning Techniques ◽

Learning Techniques ◽

The Web

The violation of privacy, others people or personal, is a very current problem, which concerns not only on the web but also in private life. In the years 1990 it was expected that nowadays, that any routine operation was carried out "manually", and it would be performed through mobile phones or personal computers. The problem pertains the distribution network that allows to share and bring together information and as result the network becomes unsafe, if subjected to attacks. Nowaday we put personal information on web because otherwise we are seen as “weak”. This work aims to measure and analyze how much information are shared by users of a pre-established social network and it is carried out through a set of algorithms techniques of machine learning.

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