SunForum

Guo, Li; Zhou, Xiaomu; Lu, Tun; Yang, Jiang; Gu, Ning

doi:10.1145/2818048.2819994

Cited by 43 publications

(26 citation statements)

References 28 publications

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“…Some recommend using preventative diagnostics to direct the conversation between patients or moderate their content consumption. Four papers specifically investigate cultural aspects of mental health [30,73,100,178].…”

Section: Analysis Of Reviewed Work System Functionsmentioning

confidence: 99%

HCI and Affective Health

Sanches

Janson

Karpashevich

et al. 2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

157

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In the last decade, the number of articles on HCI and health has increased dramatically. We extracted 139 papers on depression, anxiety and bipolar health issues from 10 years of SIGCHI conference proceedings. 72 of these were published in the last two years. A systematic analysis of this growing body of literature revealed that most innovation happens in automated diagnosis, and self-tracking, although there are innovative ideas in tangible interfaces. We noted an overemphasis on data production without consideration of how it leads to fruitful interventions. Moreover, we see a need to promote ethical practices for involvement of people living with affective disorders. Finally, although only 16 studies evaluate technologies in a clinical context, several forms of support and intervention illustrate how rich insights are gained from evaluations with real patients. Our findings highlight potential for growth in the design space of affective health technologies.

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Section: Analysis Of Reviewed Work System Functionsmentioning

confidence: 99%

HCI and Affective Health

Sanches

Janson

Karpashevich

et al. 2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

157

View full text Add to dashboard Cite

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“…An MDC for comparative analysis had to be selected. We referred to some literature and surveyed some communities, including the depression community on Baidu Tieba (Baidu, Beijing, China) [ 15 ], DingXiangYuan [ 16 ], Sunshine [ 43 ], and Zhihu. Among them, the DingXiangYuan and Sunshine are online community platforms operated by medical and psychological service institutions and only for patients who have been diagnosed with depression.…”

Section: Methodsmentioning

confidence: 99%

A Comparative Study of Online Depression Communities in China

Tang

2020

IJERPH

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Online communities have become a tool for researchers to understand and help individuals with depression. According to their operation mode in terms of management, communities can be divided into management depression communities (MDCs) and lacking-management depression communities (LDCs). This study aimed to investigate the characteristics and impact of LDCs in comparison with MDCs. All postings from the previous year were collected from the LDC and MDC. Keywords were extracted and coded to identify the themes, and a text classifier was built to identify the type of emotions and social support expressed in the postings. Community members were then clustered to explore their different participation patterns. We found that in the LDC, the expression of negative emotions was the most popular theme, there was a lack of information about the treatment of depression and a lack of social support providers, the level of engagement of providers was low, and support seekers did not receive attention. These results reveal the need for community management and can be used to develop more effective measures to support members of online depression communities.

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“…Textual comments from peers with informational support (IS; e.g., advice) and emotional support (ES; e.g., encouragement) are benefcial to members of OMHCs [7,21]. However, many comments are not of high quality in reality [12,15]. Inspired by the human teachers' and moderators' ways of ofering writing feedback and the design of existing writing support technologies, we summarize two types of assisting methods that an intelligent agent can use to help peers improve the comments' quality.…”

Section: A Bot Assistant For Writing Supportive Comments In Online Mental Health Communitiesmentioning

confidence: 99%