2021
DOI: 10.4103/jehp.jehp_446_20
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Social media data analysis to predict mental state of users using machine learning techniques

Abstract: BACKGROUND: Social media platforms such as Facebook, WhatsApp, and Instagram etc., are becoming very popular now not only for youth but for all walks of life. People are more often seen in busy in tweeting, chatting, or putting selfies. No one actually knows the mental state of a person in the online platform. In this article, we will be focusing on how social media is affecting issues such as road accident, murder, and suicide. The research is done by three parts. MATERIALS AND MET… Show more

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Cited by 5 publications
(4 citation statements)
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“…An increasing body of research has explored the nuanced capabilities of ML in uncovering complex patterns within multifaceted health-related datasets, often revealing insights that traditional statistical methods may overlook. Studies have demonstrated ML’s effectiveness in diagnosing psychiatric conditions by analyzing diverse data sources, such as genetic profiles, neuroimaging, patient-reported histories, and social media data [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. Notably, machine learning algorithms like Support Vector Machines (SVMs) and Gradient Boosting Machines (GBMs) have been extensively investigated for their predictive capabilities in mental health applications, reflecting a growing interest in the integration of technology and psychology [ 24 , 25 , 28 , 30 , 31 , 32 ].…”
Section: Related Reviewmentioning
confidence: 99%
“…An increasing body of research has explored the nuanced capabilities of ML in uncovering complex patterns within multifaceted health-related datasets, often revealing insights that traditional statistical methods may overlook. Studies have demonstrated ML’s effectiveness in diagnosing psychiatric conditions by analyzing diverse data sources, such as genetic profiles, neuroimaging, patient-reported histories, and social media data [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. Notably, machine learning algorithms like Support Vector Machines (SVMs) and Gradient Boosting Machines (GBMs) have been extensively investigated for their predictive capabilities in mental health applications, reflecting a growing interest in the integration of technology and psychology [ 24 , 25 , 28 , 30 , 31 , 32 ].…”
Section: Related Reviewmentioning
confidence: 99%
“…Although the Matthews correlation coefficient was originally introduced in biochemistry [ 5 ], it has gained popularity in several scientific disciplines, including software error prediction [ 6 ], pattern recognition [ 7 ], and medicine [ 8 , 9 ]. In addition, binary classification problems are prevalent in social and data sciences, such as classifying social media users [ 10 ] and predicting mental health [ 11 ]. Accurate risk prediction models are crucial to making informed decisions in various areas, including medicine and epidemiology.…”
Section: Introductionmentioning
confidence: 99%
“…Machine learning (ML) is an essential branch of AI that clinicians can use to diagnose critically ill patients and optimal utilization of limited hospital resources. [ 16 17 ] Therefore, ML can support the decrease of COVID-19 mortalities and lessen the economic burden on health care systems. [ 18 ] Previous studies developed several ML-based predictive models to predict the COVID-19 severity and patient health deterioration,[ 19 20 ] the need for ICU hospitalization[ 20 21 22 23 24 ] and mechanical ventilation,[ 25 ] and mortality.…”
Section: Introductionmentioning
confidence: 99%