Dynamic user needs modeling based on social support in online health communities

Ni, Zhenni; Yao, Zhizhen; Liu, Yunmei; Qian, Yuxing

doi:10.1145/3478905.3478910

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…OHCs are important sources for patients or caregivers to share experiences, post questions, and predominantly seek and provide social support more readily and regularly from or to peers facing similar health problems [14][15][16]. Social support refers to the exchange of communication between individuals to reduce uncertainty and promote a recipient's perception and ability to cope with stressful events [17].…”

Section: Ohcs and Social Supportmentioning

confidence: 99%

Social Support in a Diabetes Online Community: Mixed Methods Content Analysis

2023

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Background Patients with diabetes may experience different needs according to their diabetes stage. These needs may be met via online health communities in which individuals seek health-related information and exchange different types of social support. Understanding the social support categories that may be more important for different diabetes stages may help diabetes online communities (DOCs) provide more tailored support to web-based users. Objective This study aimed to explore and quantify the categorical patterns of social support observed in a DOC, taking into consideration users’ different diabetes stages, including prediabetes, type 2 diabetes (T2D), T2D with insulin treatment, and T2D remission. Methods Data were collected from one of the largest DOCs in Europe: Diabetes.co.uk. Drawing on a mixed methods content analysis, a qualitative content analysis was conducted to explore what social support categories could be identified in users’ posts. A total of 1841 posts were coded by 5 human annotators according to a modified version of the Social Support Behavior Code, including 7 different social support categories: achievement, congratulations, network support, seeking emotional support, seeking informational support, providing emotional support, and providing informational support. Subsequently, quantitative content analysis was conducted using chi-square post hoc analysis to compare the most prominent social support categories across different stages of diabetes. Results Seeking informational support (605/1841, 32.86%) and providing informational support (597/1841, 32.42%) were the most frequent categories exchanged among users. The overall distribution of social support categories was significantly different across the diabetes stages (χ218=287.2; P<.001). Users with prediabetes sought more informational support than those in other stages (P<.001), whereas there were no significant differences in categories posted by users with T2D (P>.001). Users with T2D under insulin treatment provided more informational and emotional support (P<.001), and users with T2D in remission exchanged more achievement (P<.001) and network support (P<.001) than those in other stages. Conclusions This is the first study to highlight what, how, and when different types of social support may be beneficial at different stages of diabetes. Multiple stakeholders may benefit from these findings that may provide novel insights into how these categories can be strategically used and leveraged to support diabetes management.

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Section: Ohcs and Social Supportmentioning

confidence: 99%

Social Support in a Diabetes Online Community: Mixed Methods Content Analysis

2023

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Modelling Users for User Modelling: Dynamic Personas for Improved Personalisation in Digital Behaviour Change

Matias,

Farhat-ul-Ain,

Akhmetzyanova

et al. 2024

Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization

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Social Support in a Diabetes Online Community: A Mixed Methods Content Analysis (Preprint)

Semedo¹,

Bath²,

Zhang³

2022

Preprint

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BACKGROUND Diabetes patients may experience different needs according to their diabetes stage. These needs may be met via online health communities (OHCs), where individuals seek health-related information and exchange different types of social support. Understanding what social support categories that may be more important for different diabetes stages may help diabetes online communities (DOCs) to provide more tailored support to online users. OBJECTIVE This study aimed to explore and quantify the categorical patterns of social support observed on a DOC, taking into consideration users’ different diabetes stages, including Prediabetes, Type 2 Diabetes (T2D), T2D with insulin treatment, and T2D remission. METHODS Data were collected from one of the largest DOC in Europe, Diabetes.co.uk. Drawing on a mixed methods content analysis, a qualitative content analysis was conducted to explore what social support categories could be identified in users’ posts. A total of 1841 posts were coded by five human annotators according to a modified version of the Social Support Behaviour Code (SSBC), including seven different social support categories: achievement, congratulations, network support, seeking emotional support, seeking informational support, providing emotional support, and providing informational support. Then, quantitative content analysis was conducted using chi-squared post hoc analysis to compare the most prominent social support categories across the different stages of diabetes. RESULTS Overall, seeking informational support (32.9%) and providing informational support (32.4%) were the most frequent categories exchanged among users. The overall distribution of social support categories was significantly different across diabetes stages (χ218=287.2; P<.001). Prediabetes users sought more informational support than other stages (P<.001), while there were no significant differences in categories posted by T2D users (P>.001). T2D insulin users provided more informational and emotional support (P<.001), and T2D remission users exchanged more achievement (P<.001) and network support (P<.001) than other stages. CONCLUSIONS This is the first study that highlights what, how and when different types of social support may be beneficial for different stages of diabetes. These findings may provide novel insights to multiple stakeholders about how these categories can be strategically used and leveraged to support diabetes management.

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Dynamic user needs modeling based on social support in online health communities

Cited by 3 publications

References 18 publications

Social Support in a Diabetes Online Community: Mixed Methods Content Analysis

Social Support in a Diabetes Online Community: Mixed Methods Content Analysis

Modelling Users for User Modelling: Dynamic Personas for Improved Personalisation in Digital Behaviour Change

Social Support in a Diabetes Online Community: A Mixed Methods Content Analysis (Preprint)

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