A Systematic Review of Techniques Employed for Determining Mental Health Using Social Media in Psychological Surveillance During Disasters

Karmegam, Dhivya; Ramamoorthy, Thilagavathi; Mappillairajan, Bagavandas

doi:10.1017/dmp.2019.40

Cited by 22 publications

(24 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The framework proposed in this research covers three practical tasks that are related to each other with a common goal to develop a deep-learning system for emotion detection and analysis of informative trends from COVID-19 tweets of people’s reaction during the stay-at-home. Our final results uncovered important directions for public health policy makers [40] and decision makers [41] to pay attention to emotional issues that stemmed from those strict public health restrictions.…”

Section: Discussion Limitation and Future Workmentioning

confidence: 92%

Artificial Intelligence for Emotion-Semantic Trending and People Emotion Detection During COVID-19 Social Isolation

Jelodar

Orji

Matwin

et al. 2021

Preprint

View full text Add to dashboard Cite

Taking advantage of social media platforms, such as Twitter, this paper provides an effective framework for emotion detection among those who are quarantined. Early detection of emotional feelings and their trends help implement timely intervention strategies. Given the limitations of medical diagnosis of early emotional change signs during the quarantine period, artificial intelligence models provide effective mechanisms in uncovering early signs, symptoms and escalating trends. Novelty of the approach presented herein is a multitask methodological framework of text data processing, implemented as a pipeline for meaningful emotion detection and analysis, based on the Plutchik/Ekman approach to emotion detection and trend detection. We present an evaluation of the framework and a pilot system. Results of confirm the effectiveness of the proposed framework for topic trends and emotion detection of COVID-19 tweets. Our findings revealed Stay-At-Home restrictions result in people expressing on twitter both negative and positive emotional semantics (feelings), where negatives are “Anger” (8.5% of tweets), followed by “Fear” (5.2%), “Anticipation” (53.6%) and positive emotional semantics are “Joy” (14.7%) and “Trust” (11.7%). Semantic trends of safety issues related to staying at home rapidly decreased within the 28 days and also negative feelings related to friends dying and quarantined life increased in some days. These findings have potential to impact public health policy decisions through monitoring trends of emotional feelings of those who are quarantined. The framework presented here has potential to assist in such monitoring by using as an online emotion detection tool kit.

show abstract

Section: Discussion Limitation and Future Workmentioning

confidence: 92%

Artificial Intelligence for Emotion-Semantic Trending and People Emotion Detection During COVID-19 Social Isolation

Jelodar

Orji

Matwin

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Many people don't know who and how to turn to for help. Relevant departments could make full use of the mass media to help people who are difficult to self-adjust [28,29]. For example, providing psychological consultation services by setting up a crisis intervention hotline or opening a psychological adjustment public live broadcast platform.…”

Section: Discussionmentioning

confidence: 99%

People’s Life and Psychological Change after Quarantining Wuhan: Influence of the Epidemic of Covid-19

Chen¹,

Yu²,

Yao³

et al. 2020

Preprint

View full text Add to dashboard Cite

The epidemic of Coronavirus Disease 2019 (Covid-19) has brought many changes to people's life. This study aims to analysis Chinese people's psychological change and life after quarantining Wuhan and explore the influencing factors. Based on data from a web-survey after quarantining Wuhan (N=3268), the principal-component-analysis (PCA), multiple-linear-regression (MLR), propensity-score-matching (PSM) were used to explore the psychological change of people in China and the influencing factors. 83.3% of the respondents said that the impact of the epidemic on their life had increased after quarantining Wuhan. A considerable proportion of people's anxiety increased, being reflected in negative emotion, behavioral response and physiological response. The proportion of people who said their anxiety had increased in Wuhan was higher than that in other regions (p <0.05). The anxiety of people who were in medical isolation increased less than those who were not (p <0.05). All three aspects of people’s anxiety were positively related with time of medical isolation and degree of the attention on the epidemic (p＜0.05) except the effect of attention degree on the physiological response (p＝0.06). The measure of medical isolation at home should be advocated. Yet people should reduce the concern for the epidemic while paying attention to self-protection.

show abstract

“…Most of these studies are aimed at highlighting different machine learning algorithms in order to demonstrate a superior technique [1], [3], [4], [6]- [9], [11]. Some researchers used Hybrid Algorithm for prediction [1], [3], [4], [8], [11], while many have created a new method altogether with the help of machine learning algorithms [4], [6], [7], [9], [11]. Natural Language Process also gave very promising results [3]- [6], [8].…”

Section: Analysis Of Methods Used In the Prediction Of Mental Healthmentioning

confidence: 99%

“…Dhivya Karmegam et al [3] discussed the practicality of using data obtained from SNS for psychological well-being monitoring as well as the methods used during disasters to deduce that. The Natural Language Processing methods were used for retrieving features in SOCIALmetrics and EMOTIVEmetrics.…”

Section: Review Of Literaturementioning

confidence: 99%

A comprehensive study on Mental Health Problems caused by Online Social Networks.

Vidhya¹,

Ginu²,

Dhar³

2021

Proceedings of the First International Conference on Computing, Communication and Control System, I3CAC 2021, 7-8 June 2021, Bh

View full text Add to dashboard Cite

With the explosive rise in the development and popularization of social networking apps, Online Social Networks (OSNs) have now become a cornerstone of everyone's daily lives. Although OSNs tend to broaden the capacity of their users to increase social interactions, they may actually reduce users' real-world person-to-person dialogue. This decrease in the actual interaction can cause many mental health problems like Nomophobia, Phubbing, etc. In this paper, we try to learn about all the efforts made in this field to detect these mental health disorders. We review various literature available regarding the Mental Health Problems caused by Online Social networks and the methods used to detect them using Data Mining. These research works have utilized Regression Techniques, Support Vector Machine, Naive Bayes, etc to predict the outcomes. We further investigate their pre-processing, feature extraction, and classification processes throughout this paper.

show abstract

A Systematic Review of Techniques Employed for Determining Mental Health Using Social Media in Psychological Surveillance During Disasters

Cited by 22 publications

References 52 publications

Artificial Intelligence for Emotion-Semantic Trending and People Emotion Detection During COVID-19 Social Isolation

Artificial Intelligence for Emotion-Semantic Trending and People Emotion Detection During COVID-19 Social Isolation

People’s Life and Psychological Change after Quarantining Wuhan: Influence of the Epidemic of Covid-19

A comprehensive study on Mental Health Problems caused by Online Social Networks.

Contact Info

Product

Resources

About