Exploring the collective knowledge curation process of online health communities

2016 ◽  
Author(s):  
◽  
Wanli Xing
Keyword(s):  
Social Support ◽  
Information Flow ◽  
Flow Patterns ◽  
University Of Missouri ◽  
Online Health Communities ◽  
Health Community ◽  
Health Related ◽  
Health Communities ◽  
The University ◽  
Relationship Of

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] More and more people turn to online health communities for social support to satisfy their health-related needs. Previous studies on social support and online health communities in general have focused on the content of social support and the relationship of social support with other entities using traditional social science methods. Little is known about how social support facilitate the knowledge curation process in an online health community. Moreover, the presence of misinformation in online health communities also calls for research into the knowledge curation process in order to reduce the risk of misinformation. This study uses data mining technologies to analyze around one million posts across 23 online health communities. It aims to reveal how information, through social support, flows between the community users working as a whole to dynamically curate knowledge and further interacts with information accuracy. This data-centric research in online health communities 1) discovered that xperiphery users instead of core users dominate the quantitative and content information flow; 2) identified three temporal information flow patterns for the knowledge curation process -- each with distinct characteristics; 3) found that information accuracy differed significantly over the identified information flow patterns and time and the information accuracy variation trends with each information flow pattern was identified as well. These findings not only have important implications for social support use, delivery and social support research methodologies but also can inform future online health platform design.

scholarly journals Development and Evaluation of the Canteen Connect Online Health Community: Using a Participatory Design Approach in Meeting the Needs of Young People Impacted by Cancer

Cancers ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 50
Author(s):  
Jennifer Cohen ◽  
Pandora Patterson ◽  
Melissa Noke ◽  
Kristina Clarke ◽  
Olga Husson
Keyword(s):  
Participatory Design ◽  
Positive Impact ◽  
Familial Cancer ◽  
Online Health Communities ◽  
Implementation Evaluation ◽  
Online Health Community ◽  
Health Community ◽  
Health Related ◽  
Adolescent And Young Adults ◽  
Health Communities

Adolescent and young adults (AYAs) impacted by their own or familial cancer require information and peer support throughout the cancer journey to ameliorate feelings of isolation. Online Health Communities (OHC) provide social networks, support, and health-related content to people united by a shared health experience. Using a participatory design (PD) process, Canteen developed Canteen Connect (CC), an OHC for AYAs impacted by cancer. This manuscript outlines the process used to develop CC: (1) A mixed-methods implementation evaluation of Version I of CC (CCv.1); (2) Qualitative workshops utilizing strengths-based approaches of PD and appreciative inquiry to inform the development of CC Version 2 (CCv.2); quantitative implementation evaluation to assess the appropriateness, acceptability, and effectiveness of CCv.2. Through several iterations designed and tested in collaboration with AYAs, CCv.2 had improvements in the user experience, such as the ability to send a private message to other users and the site becoming mobile responsive. Results from the evaluation showed CCv.2 was appropriate for connecting with other AYAs. Most AYAs reported satisfaction with CCv.2 and a positive impact on their feelings of sadness, worry, and/or anxiety. CCv.2 fills an important service provision gap in providing an appropriate and acceptable OHC for AYAs impacted by cancer, with initial promising psychological outcomes.

scholarly journals IoT Based Health—Related Topic Recognition from Emerging Online Health Community (Med Help) Using Machine Learning Technique

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1469 ◽  
Author(s):  
Pradeepa Sampath ◽  
Gayathiri Packiriswamy ◽  
Nishmitha Pradeep Kumar ◽  
Vimal Shanmuganathan ◽  
Oh-Young Song ◽  
...  
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Learning Algorithm ◽  
Vital Role ◽  
Online Health Communities ◽  
Health Community ◽  
Chi Squared ◽  
Health Related ◽  
Health Communities ◽  
Processing Techniques

The unprompted patient’s and inimitable physician’s experience shared on online health communities (OHCs) contain a wealth of unexploited knowledge. Med Help and eHealth are some of the online health communities offering new insights and solutions to all health issues. Diabetes mellitus (DM), thyroid disorders and tuberculosis (TB) are chronic diseases increasing rapidly every year. As part of the project described in this article comments related to the diseases from Med Help were collected. The comments contain the patient and doctor discussions in an unstructured format. The sematic vision of the internet of things (IoT) plays a vital role in organizing the collected data. We pre-processed the data using standard natural language processing techniques and extracted the essential features of the words using the chi-squared test. After preprocessing the documents, we clustered them using the K-means++ algorithm, which is a popular centroid-based unsupervised iterative machine learning algorithm. A generative probabilistic model (LDA) was used to identify the essential topic in each cluster. This type of framework will empower the patients and doctors to identify the similarity and dissimilarity about the various diseases and important keywords among the diseases in the form of symptoms, medical tests and habits.

Examining online health community users’ sharing behaviour: A social influence perspective

2021 ◽  
pp. 026666692110071
Author(s):  
Tao Zhou
Keyword(s):  
Social Influence ◽  
Influence Factors ◽  
Privacy Concern ◽  
Social Influence Theory ◽  
Online Health Communities ◽  
Related Information ◽  
Health Community ◽  
The Social ◽  
Health Related ◽  
Health Communities

Online health communities (OHC) provide a platform for users to exchange health-related information and seek emotional support. However, users often lack the intention to share their knowledge, which may lead to the failure of OHC. Drawing on the social influence theory, this research examined OHC users’ sharing behaviour. The results indicated that users’ sharing intention is influenced by three social influence factors, which include subjective norm, social identity and group norm. In addition, social support and privacy concern have effects on these three social influence factors. The results imply that OHC need to leverage social influence in order to facilitate users’ sharing behaviour.

scholarly journals Impact of Patient Crowdvotes Aggregated Service Diversity on Doctors� Service Sales in Online Health Community: a Retrospective Study (Preprint)

2018 ◽  
Author(s):  
Hai-Yan Yu ◽  
Jying-Nan Wang ◽  
Ya-Ling Chiu ◽  
Hang Qiu ◽  
Ling Xiao
Keyword(s):  
Retrospective Study ◽  
Good Doctor ◽  
Lorenz Curves ◽  
Online Health Communities ◽  
Online Health Community ◽  
Health Community ◽  
Main Effect ◽  
Health Communities ◽  
Log Linear ◽  
Disease Understanding

BACKGROUND An increasing number of people visit online health communities to esquire health information with doctors. In the online health community (OHC), patient crowds tended to label and vote the doctors’ specialties with encountered disease. Understanding how patients’ online labels can help us understand the service diversity for patients in online health communities and provide constructive suggestions for doctors serving more patients online. OBJECTIVE Our goal was to understand: (1) what kind of patterns are the labels of patient crowdvotes aggregated service diversity, including encountered disease labels and online votes, in a OHC? (2) wheather the patient crowdvotes aggregated service diversity make doctors’ service sales difference in OHC? (3) how can managers in OHC perform to improve doctors’ service sales with the feedback of crowdvotes aggregated service diversity? METHODS We designed a retrospective study with data collected from the largest OHC (Good Doctor website) in China. We first used descriptive statistics to investigate the patient crowdvotes aggregated service diversity. Then a multiple log-linear relationship was adapted to investigate the main and the interaction impact of service diversity on doctors’ service sales. RESULTS Our sample consists of 9,841 doctors from 1,255 different hospitals widely distributed in China. 18,997,018 patients had been serviced by these doctors since they became members of the study OHC. 704,467 votes of doctors’ clinical specialties were labeled by patient crowds in recent two years (Aug.26, 2015-Aug. 25, 2017). Gini coefficient of serviced patients is very high, 0.626, followed by the volume of votes (0.562). Based on the regression model, we found that the coefficients of the control variables, doctor review rating and clinic title, were 0.810(0.041), and 1.735 (0.027), respectively. For the breadth of voted specialties, volume of votes and degree of voted diversity, the standardized coefficient of the main effect were 0.309 (0.038), 0.745 (0.014) and 0.073 (0.018), respectively. All of the estimates are statistically significant at a 0.1% level. CONCLUSIONS Our study provided empirical evidence that the patterns of both the labels of patient crowdvotes aggregated service diversity and doctors’ service sales were of inequality (as illustrated in Lorenz curves) in the distribution of its size of serviced patients in a OHC. Patient crowds’ online labels also leaded to differences in the doctors’ service sales online. The treads of the doctors’ service sales kept increasing as the patient crowdvotes aggregated service diversity increased. Finally, our findings suggested that the higher breadth of voted specialties and degree of voted diversity displayed a greater service sales with a higher review rating, deploying less inequality of Doctors’ service sales.

scholarly journals Social exchange approach in knowledge sharing of online communities – A Case of online health communities at Ho Chi Minh City

2017 ◽  
Vol 1 (Q4) ◽  
pp. 13-24
Author(s):  
Phong Thanh Nguyen ◽  
Tuan Manh Nguyen
Keyword(s):  
Social Exchange ◽  
Positive Impact ◽  
Sense Of Self ◽  
Online Health Communities ◽  
Online Health Community ◽  
Writing Group ◽  
Health Community ◽  
Self Worth ◽  
Face Concern ◽  
Health Communities

The demand to look for information and share information in nowaday society are a huge needed, especially in the internet revolution are developing more and more. The studies proposed the model that includes the benefit factors (sense of self-worth, face concern, reputation and social support) and cost factors (executional costs, cognitive costs) with the points of view of Social Exchange Theory that influences to knowledge donating behavior, knowledge collecting behavior and community promotion among members. The studies will be verified in health care member of the online health communities in Ho Chi Minh City. Quantitative research also was conducted 336 samples were used to evaluate and test the research. The results of the Structural Equation Modeling (SEM) show that the theoretical models are suited the market data and hypotheses of the research model are supported. In particular, factors of the benifit group (sense of self-worth, face concern, reputation and social support) have a positive impact on the knowledge donating behavior and knowledge collecting behavior. In addition, factors of the cost group (executional costs, cognitive costs) have a negative impact the knowledge donating behavior and knowledge collecting behavior. Knowledge donating behavior and knowledge collecting behavior have a positive impact on community promotion to the online health community. In addition, the results of multi-group analysis that there is no difference between knowledge sharing’s writing group and no knowledge sharing’s writing group. The results may be usefull for online health community, hospitals, doctors, individuals and businesses.

scholarly journals Antecedents and consequences of the perceived usefulness of smoking cessation online health communities

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chenglong Li ◽  
Hongxiu Li ◽  
Reima Suomi
Keyword(s):  
Social Support ◽  
Smoking Cessation ◽  
Knowledge Sharing ◽  
Emotional Support ◽  
Perceived Usefulness ◽  
Informational Support ◽  
Continuance Intention ◽  
Content Type ◽  
Online Health Communities ◽  
Health Communities

PurposeAn empirical study investigated the antecedents to perceived usefulness (PU) and its consequences in the context of smoking cessation online health communities (OHCs).Design/methodology/approachTo validate a research model for perceived informational support, perceived emotional support and perceived esteem support, the authors conducted a partial-least-squares analysis of empirical data from an online survey (N = 173) of users of two smoking cessation OHCs. The proposed model articulates these as antecedents to PU from a social support perspective, and knowledge sharing and continuance intention are expressed as consequences of PU.FindingsThe empirical study identified that the PU of smoking cessation OHCs is influenced by perceived emotional support and perceived esteem support, and perceived informational support indirectly affects PU via these factors. In turn, PU exerts a positive influence on both knowledge sharing and continuance intention. Also, knowledge sharing positively affects continuance intention.Originality/valueThe study contributes to scholarship on users' postadoption behavior in the context of smoking cessation OHCs by disentangling the antecedents to PU from a social support perspective and pinpointing some important consequences of PU. The research also has practical implications for managing smoking cessation OHCs.

scholarly journals Study on the Evolution of Information Sharing Strategy for Users of Medical Cloud

2019 ◽  
Author(s):  
Panpan Zhu ◽  
Jiang Shen ◽  
Man Xu
Keyword(s):  
Information Sharing ◽  
Scale Free ◽  
Online Health Communities ◽  
Online Health Community ◽  
Health Community ◽  
Community Information ◽  
Free Network ◽  
Health Communities ◽  
Sharing Behavior ◽  
Community Patients

Abstract Technological advances are driving the growth of online health communities. However, there are some problems such as low user participation and insignificant social benefits in online health communities. This paper discusses the evolution law of information sharing behavior of members of online health community to study the influence of different behaviors on health information sharing results and explore the ways to improve the level of community information sharing. Based on BA scale-free network (Albert-László Barabás and Réka Albert scale-free network) , this paper established an information sharing behavior model for members of online health community with the evolutionary game theory method, and discussed the influence of different game parameters and initial conditions on the evolution results of information sharing behavior of community patients with the method of numerical experiment.Results: It is found that the key to improve the level of community information sharing is to improve the benefit of patients' information sharing, the proportion of patients sharing information at the initial moment, the degree of network nodes, and reduce the sharing cost. Community managers should improve the information conversion ability and information absorption ability of community patients through offline activities, professional guidance and other forms. At the same time, it can reduce the difficulty and risk of information sharing and strengthen the connection among members, thus comprehensively enhancing the value of the community.

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