Data Mining of Web-Based Documents on Social Networking Sites That Included Suicide-Related Words Among Korean Adolescents

Song, Jong Tae; Song, Tae Min; Seo, Dong-Chul; Jin, Jae Hyun

doi:10.1016/j.jadohealth.2016.07.025

Cited by 44 publications

(32 citation statements)

References 16 publications

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“…Similar investigations have been conducted in Korea which used web-based data. As an instance, a Korean study [1] explored the search queries on web-based social media that are commonly used with adolescents and young adults and not only analysed the risk of suicide but also its relation to the monthly employment rate, rental prices index, youth suicide rate, and number of bully victims. Woo et al [26] analysed Twitter content to explore social behaviour in response to the sinking of Sewol ship.…”

Section: -Resultsmentioning

confidence: 99%

“…IT-based solutions shave succeeded in identifying these behaviours and managed to enquire the data confidentially so as to ensure privacy matters (17). NLP-based models have proved effective in analysing large amounts of data produced in social media publically available (such as Facebook and Twitter) [1,25,26].…”

Section: -Discussionmentioning

confidence: 99%

“…Therefore, it is essential to use costeffectiveness tools and strategies to collect and interpret the required data for suicide prevention [4]. Researchers believe that thinking about suicide mostly leads to the action itself and that is why it is essential to identify those who tend to do so [1]. Computational methods such as NLP can use Electronic Health Record (EHR) data to identify those prone to suicide [4].…”

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confidence: 99%

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Using of Natural Language Processing Techniques in Suicide Research

Orooji

Langarizadeh

2017

Emerg Sci J

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It is estimated that each year many people, most of whom are teenagers and young adults die by suicide worldwide. Suicide receives special attention with many countries developing national strategies for prevention. Since, more medical information is available in text, Preventing the growing trend of suicide in communities requires analyzing various textual resources, such as patient records, information on the web or questionnaires. For this purpose, this study systematically reviews recent studies related to the use of natural language processing techniques in the area of people’s health who have completed suicide or are at risk. After electronically searching for the PubMed and ScienceDirect databases and studying articles by two reviewers, 21 articles matched the inclusion criteria. This study revealed that, if a suitable data set is available, natural language processing techniques are well suited for various types of suicide related research.

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Section: -Resultsmentioning

confidence: 99%

Section: -Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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Using of Natural Language Processing Techniques in Suicide Research

Orooji

Langarizadeh

2017

Emerg Sci J

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“…With the assist of IT, analyses of SNS shares may be a part of diagnosis and treatment of psychiatric disorders. When we look at the literature which evaluates people's shares of their psychological situations on SNSs, it is seen that there are many publications which research psychology and psychiatry through SNSs based on the big data techniques (Bazarova et al 2017, De Choudhury et al 2013, Nambisan et al 2015, Song et al 2016). These publications give insights about how we can evaluate psychiatric symptoms, in particular, with the use of data on SNSs.…”

Section: Introductionmentioning

confidence: 99%

Big Data in Adolescent Psychiatry: Do Patients Share Their Psychiatric Symptoms on Social Networking Sites?

Uçar¹,

Eray²,

Kocael³

et al. 2018

Psychiat Danub

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Background:Fascinating developments in big data technologies and unprecedented diffusion of social networking sites (SNSs) generate unseen opportunities for scientific fields, including psychiatry. This study focuses on the use of SNSs by adolescent psychiatric patients and the potential use of SNS-generated data to help medical practitioners diagnose and treat patients' mental health. Our objective is to understand and measure the psychiatric and individual conditions in which symptom-sharing occurs on SNSs and the frequency of these conditions. Based on literature, we hypothesized that the perceived value of social network sites positively affects adolescents' sharing of symptoms on these sites.Subjects and methods: An empirical test of this hypothesis was conducted with a survey of 224 adolescents admitted to a psychiatry clinic in Turkey. The hypothesis was tested using a hierarchical multiple regression analysis.Results: The perceived value of SNSs explained an additional 37.8% of variation in symptom sharing on SNSs above and beyond the control variables, which are gender, age, type of disorder, and amount of internet and SNS use. The findings suggested that adolescents share symptoms on SNSs only if they attribute value to the SNSs that they use. We also found that 72% of adolescents in our sample shared their symptoms on SNSs.Conclusions: There is an attractive opportunity for information technology companies to develop, together with health professionals; data analytics that are able to detect symptoms to support psychiatric diagnoses and pave the way for big-data enabled personalized medicine.

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“…These data are not natively part of any personal health record, but may be voluntarily delivered by the patient to public and anonymous recipients. This unstructured data is now collected, extracted, and analysed by researchers with tools used to explore massive datasets for public health research [13,14]. As in previous years, we have reviewed the literature in the area of consumer health informatics and education.…”

Section: Introductionmentioning

confidence: 99%

Secondary Use of Recorded or Self-expressed Personal Data: Consumer Health Informatics and Education in the Era of Social Media and Health Apps

Staccini¹,

Fernández-Luque²

2017

Yearb Med Inform

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SummaryObjective: To summarize the state of the art during the year 2016 in the areas related to consumer health informatics and education with a special emphasis in secondary use of patient data. Methods: We conducted a systematic review of articles published in 2016, using PubMed with a predefined set of queries. We identified over 320 potential articles for review. Papers were considered according to their relevance for the topic of the section. Using consensus, we selected the 15 most representative papers, which were submitted to external reviewers for full review and scoring. Based on the scoring and quality criteria, five papers were finally selected as best papers Results: The five best papers can be grouped in two major areas: 1) methods and tools to identify and collect formal requirements for secondary use of data, and 2) innovative topics highlighting the interest of carrying on "secondary" studies on patient data, more specifically on the data self-expressed by patients through social media tools. Regarding the formal requirements about informed consent, the selected papers report a comparison of legal aspects in European countries to find a common and unified grammar around the concept of "data donation". Regarding innovative approaches to value patient data, the selected papers report machine learning algorithms to extract knowledge from patient experience and satisfaction with health care delivery, drug and medication use, treatment compliance and barriers during cancer disease, or acceptation of public health actions such as vaccination. Conclusions: Secondary use of patient data (apart from personal health care record data) can be expressed according to many ways. Requirements to allow this secondary use have to be harmonized between countries, and social media platforms can be efficiently used to explore and create knowledge on patient experience with health problems or activities. Machine learning algorithms can explore those massive amounts of data to support health care professionals, and institutions provide more accurate knowledge about use and usage, behaviour, sentiment, or satisfaction about health care delivery. KeywordsConsumer health informatics; personal health information; social media; secondary data use; internet Yearb Med Inform 2017:172-7 http://dx

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Data Mining of Web-Based Documents on Social Networking Sites That Included Suicide-Related Words Among Korean Adolescents

Cited by 44 publications

References 16 publications

Using of Natural Language Processing Techniques in Suicide Research

Using of Natural Language Processing Techniques in Suicide Research

Big Data in Adolescent Psychiatry: Do Patients Share Their Psychiatric Symptoms on Social Networking Sites?

Secondary Use of Recorded or Self-expressed Personal Data: Consumer Health Informatics and Education in the Era of Social Media and Health Apps

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