#suicidal - A Multipronged Approach to Identify and Explore Suicidal Ideation in Twitter

Sinha, Pradyumna Prakhar; Mishra, Rohan; Sawhney, Ravinder Singh; Mahata, Debanjan; Shah, Rajiv Ratn; Liu, Huan

doi:10.1145/3357384.3358060

Cited by 62 publications

(26 citation statements)

References 28 publications

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“…In terms of explicit reference to ethics, 10 studies included a specific section on the ethical issues raised by their work [ 64 , 73 , 80 , 87 , 92 , 98 , 99 , 105 , 163 , 187 ] and 10 others included a broad reference to key ethical issues [ 64 , 70 , 122 , 124 , 126 , 133 , 140 , 152 , 184 ]. The latter material varied from one or two sentences to a paragraph or more.…”

Section: Resultsmentioning

confidence: 99%

“…The active involvement of mental health service users and representative groups for persons with psychosocial disabilities has become a prominent ethos in mental health and disability policies worldwide [ 194 ] and is imperative in international human rights law [ 195 ]. A study in our survey included an acknowledgment of the limitations of not working with affected populations [ 105 ]. However, the authors referred to study populations as research subjects rather than active contributors to technological development.…”

Section: Discussionmentioning

confidence: 99%

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Ethics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping Review

Gooding¹,

Kariotis²

2021

JMIR Ment Health

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Background Uncertainty surrounds the ethical and legal implications of algorithmic and data-driven technologies in the mental health context, including technologies characterized as artificial intelligence, machine learning, deep learning, and other forms of automation. Objective This study aims to survey empirical scholarly literature on the application of algorithmic and data-driven technologies in mental health initiatives to identify the legal and ethical issues that have been raised. Methods We searched for peer-reviewed empirical studies on the application of algorithmic technologies in mental health care in the Scopus, Embase, and Association for Computing Machinery databases. A total of 1078 relevant peer-reviewed applied studies were identified, which were narrowed to 132 empirical research papers for review based on selection criteria. Conventional content analysis was undertaken to address our aims, and this was supplemented by a keyword-in-context analysis. Results We grouped the findings into the following five categories of technology: social media (53/132, 40.1%), smartphones (37/132, 28%), sensing technology (20/132, 15.1%), chatbots (5/132, 3.8%), and miscellaneous (17/132, 12.9%). Most initiatives were directed toward detection and diagnosis. Most papers discussed privacy, mainly in terms of respecting the privacy of research participants. There was relatively little discussion of privacy in this context. A small number of studies discussed ethics directly (10/132, 7.6%) and indirectly (10/132, 7.6%). Legal issues were not substantively discussed in any studies, although some legal issues were discussed in passing (7/132, 5.3%), such as the rights of user subjects and privacy law compliance. Conclusions Ethical and legal issues tend to not be explicitly addressed in empirical studies on algorithmic and data-driven technologies in mental health initiatives. Scholars may have considered ethical or legal matters at the ethics committee or institutional review board stage. If so, this consideration seldom appears in published materials in applied research in any detail. The form itself of peer-reviewed papers that detail applied research in this field may well preclude a substantial focus on ethics and law. Regardless, we identified several concerns, including the near-complete lack of involvement of mental health service users, the scant consideration of algorithmic accountability, and the potential for overmedicalization and techno-solutionism. Most papers were published in the computer science field at the pilot or exploratory stages. Thus, these technologies could be appropriated into practice in rarely acknowledged ways, with serious legal and ethical implications.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Ethics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping Review

Gooding¹,

Kariotis²

2021

JMIR Ment Health

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“…This approach is consistent with similar studies examining healthcare related data from Twitter. [13,14]…”

Section: Methodsmentioning

confidence: 99%

Investigating mental and physical disorders associated with COVID-19 in online health forums

Patel

Smeraldi

Abdollahyan

et al. 2020

Preprint

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Objectives: Online health forums provide rich and untapped real-time data on population health. Through novel data extraction and natural language processing (NLP) techniques, we characterise the evolution of mental and physical health concerns relating to the COVID-19 pandemic among online health forum users. Setting and design: We obtained data from 739,434 posts by 53,134 unique users of three leading online health forums: HealthBoards, Inspire and HealthUnlocked, from the period 1st January 2020 to 31st May 2020. Using NLP, we analysed the content of posts related to COVID-19. Primary outcome measures: (i) Proportion of forum posts containing COVID-19 keywords (ii) Proportion of forum users making their very first post about COVID-19 (iii) Number of COVID-19 related posts containing content related to physical and mental health comorbidities Results: Posts discussing COVID-19 and related comorbid disorders spiked in early- to mid-March around the time of global implementation of lockdowns prompting a large number of users to post on online health forums for the first time. The pandemic and corresponding public response has had a significant impact on posters' queries regarding mental health. Conclusions: We demonstrate it is feasible to characterise the content of online health forum user posts regarding COVID-19 and measure changes over time. Social media data sources such as online health forums can be harnessed to strengthen population-level mental health surveillance.

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“…Lack of interpretability is its primary drawback that drops its efficiency. However, MRC models are introduced to enhance MRC systems' interpretability, such as HotpotQA [35] .…”

Section: Difficulty In Interpretationmentioning

confidence: 99%

Machine Reading Comprehension: Methods and Trends of Low Resource Languages

2021

jecet

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Natural language processing (NLP) has been used to establish human-like communication with computers. Machine Reading Comprehension (MRC) has made a significant development; it has been crucial for machines to comprehend substantial language facets, like semantics, syntax, pragmatics, and phonology. Multiple studies have reported MRC models in high-resource languages. However, these models are unable to provide significant performance in MRC models in low resource languages. This is due to the unavailability of large-scale training datasets in low resource languages. Several studies on Machine Reading Comprehension (MRC) have proposed MRC models based on English. Nonetheless, these models provide insignificant performance on low-resource languages. However, limited research has been done using low resource languages, particularly Arabic, Urdu, and Hindi. This study presents a survey on trends and methods of Machine Reading Comprehension (MRC) in these low-resource languages. The survey demonstrates that available MRC models are ineffective in low resource languages, such as Hindi, Arabic, and Urdu, mainly due to large datasets and language structure unavailability. Finally, the study describes open issues in available MRC systems and provides direction for future research.

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#suicidal - A Multipronged Approach to Identify and Explore Suicidal Ideation in Twitter

Cited by 62 publications

References 28 publications

Ethics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping Review

Ethics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping Review

Investigating mental and physical disorders associated with COVID-19 in online health forums

Machine Reading Comprehension: Methods and Trends of Low Resource Languages

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