Exploring Timelines of Confirmed Suicide Incidents Through Social Media

Huang, Xiaolei; Xing, Linzi; Brubaker, Jed R.; Paul, Michael J.

doi:10.1109/ichi.2017.47

Cited by 20 publications

(13 citation statements)

References 24 publications

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“…The included studies either concentrated only on a particular illness like depression or studied multiple illnesses. Fourteen studies focused on general mental health disorders without specifying one particular disorder 11,12,14,15,16,17,18,19,21,23,25,28,32,35 . Twelve studies were on depression 8,9,13,20,22,24,26,27,29,31,33,36 , two on schizophrenia 10,36 and four discussed suicide 29,30,34,36 .…”

Section: Mental Health Domainmentioning

confidence: 99%

“…The fourteen machine learning based studies 7,1,15,17,18,20,22,24,25,29,30,31,34,35 included ten studies that utilized machine learning techniques to predict or classify users with depression 12,20,22,24,25,30,31 , anxiety 6 , suicide ideation 29 or any cognitive distortions 35 . Two studies 23,38 used Natural Language Processing (NLP) to extract and transform information in a post or comment to predict possible post-traumatic stress disorder (PTSD), depression, bipolar disorder, and seasonal affective disorder (SAD) and two studies 18,31 described about tools with machine learning that helped in the collection and exploration of big data.…”

Section: Characteristics Of Studiesmentioning

confidence: 99%

“…One study investigated comments under videos in YouTube 8 and four study concentrated on Facebook 22,25,30,31 . Six studies analyzed the usage and benefits of different mobile applications related to mental health 6,10,11,17,23,26 while three studies collected posts from online websites that give access to posts from social media accounts and personal blogs freely 7,34,35 .…”

Section: Characteristics Of Studiesmentioning

confidence: 99%

“…Three studies that analyzed posts related to suicide found commonly that there is always an increase in the occurrence of posts or tweets nearing the time of suicide 28,31,35 and it is viable to detect and track suicide behaviors through social media.…”

Section: Study Outcomesmentioning

confidence: 99%

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Social media interventions for mental health: A scoping review

Ali¹

2019

Preprint

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Background: Social media in healthcare has been widely used over the past few years with increasing popularity worldwide. Social media platforms can potentially act as a possible mental health service provider by improving communication, awareness and education. Objective: The aim of this scoping review is to describe the use of social media interventions for mental health and to provide an understanding of the current state of knowledge. Methods: The search platforms utilized for the scoping review were PubMed and IEEE. The inclusion criteria were as follows: research-based articles, primary means for mental health service is social media, and articles that specify mental disorders (depression, anxiety, etc). Results: After applying the inclusion and exclusion criteria on 190 articles, 30 were included for the purpose of this review. Twelve studies originated from the USA while six studies were from the UK. While fourteen papers were on general mental health disorders, twelve papers focused on depression. The results generally showed that social media interventions for mental health had advantages over the conventional treatments. A lack of studies from developing countries, and fewer studies including the elderly population and the assumption of digital literacy are among the identified gaps. Conclusion: Social media has the potential to act as a service provider for users with mental health issues. However, the effectiveness of this method has not been accurately measured and more research is required for evaluating the effectiveness of using social media for treatment over conventional methods.

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Section: Mental Health Domainmentioning

confidence: 99%

Section: Characteristics Of Studiesmentioning

confidence: 99%

Section: Characteristics Of Studiesmentioning

confidence: 99%

Section: Study Outcomesmentioning

confidence: 99%

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Social media interventions for mental health: A scoping review

Ali¹

2019

Preprint

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“…Previous research has studied language use around death using computational methods in a variety of contexts, from classifying reports of death [27] to identifying suicidal signals [16,17,25,36,39], and has achieved promising results. Research of bereaved language has also revealed important patterns of sentiment and linguistic style.…”

Section: The Language Of Death and Bereavementmentioning

confidence: 99%

Tending Unmarked Graves

Jiang¹,

Brubaker²

2018

Proc. ACM Hum.-Comput. Interact.

Self Cite

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User-generated content is central to social computing scholarship. However, researchers and practitioners often presume that these users are alive. Failing to account for mortality is problematic in social media where an increasing number of profiles represent those who have died. Identifying mortality can empower designers to better manage content and support the bereaved, as well as promote high-quality data science. Based on a computational linguistic analysis of post-mortem social media profiles and content, we report on classifiers developed to detect mortality and show that mortality can be determined after the first few occurrences of post-mortem content. Applying our classifiers to content from two other platforms also provided good results. Finally, we discuss trade-offs between models that emphasize pre-vs. post-mortem precision in this sensitive context. These results mark a first step toward identifying mortality at scale, and show how designers and scientists can attend to mortality in their work.

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A Review on Human Action Recognition and Machine Learning Techniques for Suicide Detection System

Chiranjeevi¹,

Elangovan²

2019

Advances in Intelligent Systems and Computing

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Exploring Timelines of Confirmed Suicide Incidents Through Social Media

Cited by 20 publications

References 24 publications

Social media interventions for mental health: A scoping review

Social media interventions for mental health: A scoping review

Tending Unmarked Graves

A Review on Human Action Recognition and Machine Learning Techniques for Suicide Detection System

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