An analysis of mental health of social media users using unsupervised approach
With the shift of population toward digital lifestyle, it is becoming increasingly far easier to express opinions, behavior and mindset online – instantly and openly due to majorly following three very important reasons: firstly the online disinhibition effect/anonymity, second is the psychological...
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Elsevier
2020
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oai:doaj.org-article:f611b400f3ed4e2aaa5c33be2722b2e62021-12-01T05:03:32ZAn analysis of mental health of social media users using unsupervised approach2451-958810.1016/j.chbr.2020.100036https://doaj.org/article/f611b400f3ed4e2aaa5c33be2722b2e62020-08-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2451958820300361https://doaj.org/toc/2451-9588With the shift of population toward digital lifestyle, it is becoming increasingly far easier to express opinions, behavior and mindset online – instantly and openly due to majorly following three very important reasons: firstly the online disinhibition effect/anonymity, second is the psychological distance and the third the emotional contagion (Lieberman and Schroeder, 2020). The myriads of data originating from social media platforms provide a major insight into the life of people. This insight underlines the mental health and emotional conditions of the users, chiefly the young population. The challenge is to identify the users who are showing signs of succumbing to mental illness at its onset (prodrome period). In this proposed method, we applied unsupervised algorithms on the data, signaling behavior change for psychological analysis and identified the probability of users showing at-risk behavior. By at-risk behavior, we mean users who are on the verge of acquiring some mental illness. In this study, we analyzed posts and tweets from the social media platform namely Twitter and developed an unsupervised model to classify users based on the scale of change in their behavior. Our model has achieved 76.12% accuracy.Deepali JoshiDr.Manasi PatwardhanElsevierarticleMental health of social media usersPsychological analysis of tweetsAnalysis of behavior changeSocial media users’ posts reveal mental healthTweets and posts becoming tools for psychiatristsElectronic computers. Computer scienceQA75.5-76.95PsychologyBF1-990ENComputers in Human Behavior Reports, Vol 2, Iss , Pp 100036- (2020) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Mental health of social media users Psychological analysis of tweets Analysis of behavior change Social media users’ posts reveal mental health Tweets and posts becoming tools for psychiatrists Electronic computers. Computer science QA75.5-76.95 Psychology BF1-990 |
| spellingShingle |
Mental health of social media users Psychological analysis of tweets Analysis of behavior change Social media users’ posts reveal mental health Tweets and posts becoming tools for psychiatrists Electronic computers. Computer science QA75.5-76.95 Psychology BF1-990 Deepali Joshi Dr.Manasi Patwardhan An analysis of mental health of social media users using unsupervised approach |
| description |
With the shift of population toward digital lifestyle, it is becoming increasingly far easier to express opinions, behavior and mindset online – instantly and openly due to majorly following three very important reasons: firstly the online disinhibition effect/anonymity, second is the psychological distance and the third the emotional contagion (Lieberman and Schroeder, 2020). The myriads of data originating from social media platforms provide a major insight into the life of people. This insight underlines the mental health and emotional conditions of the users, chiefly the young population. The challenge is to identify the users who are showing signs of succumbing to mental illness at its onset (prodrome period). In this proposed method, we applied unsupervised algorithms on the data, signaling behavior change for psychological analysis and identified the probability of users showing at-risk behavior. By at-risk behavior, we mean users who are on the verge of acquiring some mental illness. In this study, we analyzed posts and tweets from the social media platform namely Twitter and developed an unsupervised model to classify users based on the scale of change in their behavior. Our model has achieved 76.12% accuracy. |
| format |
article |
| author |
Deepali Joshi Dr.Manasi Patwardhan |
| author_facet |
Deepali Joshi Dr.Manasi Patwardhan |
| author_sort |
Deepali Joshi |
| title |
An analysis of mental health of social media users using unsupervised approach |
| title_short |
An analysis of mental health of social media users using unsupervised approach |
| title_full |
An analysis of mental health of social media users using unsupervised approach |
| title_fullStr |
An analysis of mental health of social media users using unsupervised approach |
| title_full_unstemmed |
An analysis of mental health of social media users using unsupervised approach |
| title_sort |
analysis of mental health of social media users using unsupervised approach |
| publisher |
Elsevier |
| publishDate |
2020 |
| url |
https://doaj.org/article/f611b400f3ed4e2aaa5c33be2722b2e6 |
| work_keys_str_mv |
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