scholarly journals Why Do Users of Online Mental Health Communities Get Likes and Reposts: A Combination of Text Mining and Empirical Analysis

Healthcare ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1133
Author(s):  
Jingfang Liu ◽  
Jun Kong
Keyword(s):  
Mental Health ◽  
Text Mining ◽  
Mental Disorders ◽  
Empirical Analysis ◽  
Online Community ◽  
Information Quality ◽  
Social Experience ◽  
Adoption Model ◽  
Health Communities ◽  
Information Adoption

An online community is one of the important ways for people with mental disorders to receive assistance and obtain support. This study aims to help users with mental disorders to obtain more support and communication through online communities, and to provide community managers with the possible influence mechanisms based on the information adoption model. We obtained a total of 49,047 posts of an online mental health communities in China, over a 40-day period. Then we used a combination of text mining and empirical analysis. Topic and sentiment analysis were used to derive the key variables—the topic of posts that the users care about most, and the emotion scores contained in posts. We then constructed a theoretical model based on the information adoption model. As core independent variables of information quality, on online mental health communities, the topic of social experience in posts (0.368 ***), the topic of emotional expression (0.353 ***), and the sentiment contained in the text (0.002 *) all had significant positive relationships with the number of likes and reposts. This study found that the users of online mental health communities are more attentive to the topics of social experience and emotional expressions, while they also care about the non-linguistic information. This study highlights the importance of helping community users to post on community-related topics, and gives administrators possible ways to help users gain the communication and support they need.

scholarly journals Causal Factors of Effective Psychosocial Outcomes in Online Mental Health Communities

2020 ◽  
Author(s):  
Koustuv Saha ◽  
Amit Sharma
Keyword(s):  
Mental Health ◽  
Online Community ◽  
Psychosocial Outcomes ◽  
Case Control ◽  
Case Control Studies ◽  
Causal Factors ◽  
Health Distress ◽  
Health Communities ◽  
Mental Health Distress ◽  
And Control

Online mental health communities enable people to seek and provide support, and growing evidence shows the efficacy of community participation to cope with mental health distress. However, what factors of peer support lead to favorable psychosocial outcomes for individuals is less clear. Using a dataset of over 300K posts by ∼39K individuals on an online community TalkLife, we present a study to investigate the effect of several factors, such as adaptability, diversity, immediacy, and the nature of support. Unlike typical causal studies that focus on the effect of each treatment, we focus on the outcome and address the reverse causal question of identifying treatments that may have led to the outcome, drawing on case-control studies in epidemiology. Specifically, we define the outcome as an aggregate of affective, behavioral, and cognitive psychosocial change and identify Case (most improved) and Control (least improved) cohorts of individuals. Considering responses from peers as treatments, we evaluate the differences in the responses received by Case and Control, per matched clusters of similar individuals. We find that effective support includes complex language factors such as diversity, adaptability, and style, but simple indicators such as quantity and immediacy are not causally relevant. Our work bears methodological and design implications for online mental health platforms, and has the potential to guide suggestive interventions for peer supporters on these platforms.

scholarly journals Best Paper Selection

2019 ◽  
Vol 28 (01) ◽  
pp. 179-180
Keyword(s):  
Breast Cancer ◽  
Mental Health ◽  
Text Mining ◽  
Drug Treatment ◽  
Topic Model ◽  
Web Based ◽  
Novel Approach ◽  
Health Communities ◽  
Model Approach

Abdellaoui R, Foulquié P, Texier N, Faviez C, Burgun A, Schück S. Detection of Cases of Noncompliance to Drug Treatment in Patient Forum Posts: Topic Model Approach. J Med Internet Res 2018;20(3):e85 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5874436/ Jones J, Pradhan M, Hosseini M, Kulanthaivel A, Hosseini M. Novel Approach to Cluster Patient-Generated Data Into Actionable Topics: Case Study of a Web-Based Breast Cancer. JMIR Med Inform 2018;6(4):e45 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6293240/ Park A, Conway M, Chen AT. Examining Thematic Similarity, Difference, and Membership in Three Online Mental Health Communities from Reddit: A Text Mining and Visualization Approach. Comput Human Behav 2018 Jan;78:98-112 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5810583/

Examining Online Health Community Users’ Information Adoption Intention

2022 ◽  
Vol 35 (1) ◽  
pp. 0-0
Keyword(s):  
Social Interaction ◽  
Information Quality ◽  
Source Credibility ◽  
The Sustainable Development ◽  
Argument Quality ◽  
Online Health Communities ◽  
Health Community ◽  
Health Communities ◽  
Adoption Intention ◽  
Information Adoption

Due to the doubt on information credibility, users often hesitate to adopt the health information posted on online health communities (OHC). This may undermine the sustainable development of OHC. The purpose of this research is to identify the determinants of OHC users’ information adoption intention. The results indicated that both information factors and social interaction affect the adoption intention. Information factors include argument quality and source credibility, whereas social interaction includes perceived similarity and familiarity. The results imply that OHC need to ensure information quality and support users’ interaction in order to facilitate their information adoption.

scholarly journals The Effect of YouTube eWOM on Consumer Buying Interest

Revista CEA ◽  
2020 ◽  
Vol 6 (12) ◽  
pp. 167-179
Author(s):  
Amar Nuriman Izudin ◽  
Endang Ruswanti ◽  
Moehammad Unggul Januarko
Keyword(s):  
Structural Equation ◽  
Information Needs ◽  
Information Quality ◽  
Reasoned Action ◽  
Equation Model ◽  
Quality Information ◽  
Analysis Techniques ◽  
Adoption Model ◽  
Acceptance Model ◽  
Information Adoption

YouTube creates valuable social media opportunities in eWOM content. Individuals currently discuss products and other matters with their friends and new acquaintances over the internet. The purpose of this study was to determine the effect of YouTube eWOM conversations on consumer buying interest. Based on the Information Adoption Model (AIM) and the Theory of Reasoned Action (TRAM), we developed a new conceptual model: The Information Acceptance Model (IACM). In this quantitative study, the population under analysis, composed of followers of a YouTube channel, was selected using purposive sampling so that the number of respondents was 200. We implemented data analysis techniques using the Structural Equation Model with Lisrel. The results show that information quality, information credibility, information needs, information usefulness, information adoption, and attitude towards information influence consumer buying interest.

scholarly journals Balancing between discourses of pathology and normality: biographical practice of autoaggression. The case of self-harm

Inter ◽  
2019 ◽  
Vol 11 (20) ◽  
pp. 77-96
Author(s):  
Oxana R. Mikhaylova
Keyword(s):  
Mental Health ◽  
Online Community ◽  
Social Scientists ◽  
Cultural Discourses ◽  
Sociological Approach ◽  
Self Injury ◽  
Self Mutilation ◽  
The Status ◽  
Self Harm ◽  
Health Communities

In this article, the construction of the biographical identity of self-harmer who belongs to the online self-injurers community in Russian social network “Vkontakte” is analyzed. We applied a poststructuralist sociological approach to self-harm, this supposed viewing self-injury as a center of discursive struggles between different social actors and institutions. Our goal was to understand how self-harming person positions herself concerning diverse cultural discourses. We wanted to identify not only the patterns of biographical work but also the place of self-mutilation in the narrative. Before the interview we analyzed the discourse of the online community to which the informant belonged, we based our guide on the literature review and the results of discourse analysis. The sequential and thematic analyses were employed to investigate the interview data. As a result of our analysis, we identified the existence of normalizing and pathologizing discourses in the narrative and the ability of discursive influence to be differently included in the narrative (on the language and logic levels). Furthermore, we came up with methodological suggestions for further studies of the Russian online self-harm communities. The discussion of the biographical structure of self-harmer and the self-injury representations in it could become part of the discussion on the status of online mental health communities that exists among social scientists. This article also helps to illustrate the ability to combine the sociological and psychological optics in the studies of mental health.

scholarly journals Drivers of egovernment adoption amidst COVID-19 pandemic: The Information Adoption Model (IAM) approach

2021 ◽  
pp. 026666692110108
Author(s):  
Isaac Kofi Mensah ◽  
Samuel Adams ◽  
Joseph Kwame Adjei ◽  
Deborah Simon Mwakapesa
Keyword(s):  
Structural Equation ◽  
Information Quality ◽  
Perceived Usefulness ◽  
Equation Modeling ◽  
Quality Information ◽  
Government Services ◽  
Intention To Adopt ◽  
Adoption Model ◽  
Practical Implications ◽  
Information Adoption

The study scrutinized the behavioral adoption of informational e-government services among Chinese citizens in the midst of the COVID-19 pandemic. A structural equation modeling (SEM) technique was applied for the data analysis using Smart PLS 3.0 statistical software. The results show that information quality, information credibility, and ease of COVID-19 informational e-government services are significant in determining citizens’ perception of the usefulness of COVID-19 information shared on e-government platforms. Also, the study revealed that the perceived usefulness of COVID-19 informational e-government services was significant in predicting citizens’ intention to adopt and recommend COVID-19 informational e-government services. The theoretical and practical implications of these findings are interrogated further.

Understanding mobile users' information adoption behaviour: an extension of the information adoption model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abdulaziz Elwalda ◽  
İsmail Erkan ◽  
Mushfiqur Rahman ◽  
Deniz Zeren
Keyword(s):  
Social Support ◽  
Mobile Communications ◽  
Structural Equation ◽  
Information Quality ◽  
Research Model ◽  
Content Type ◽  
Social Media Platforms ◽  
Adoption Model ◽  
Practical Implications ◽  
Information Adoption

PurposeMobile messaging applications (MMAs) have surpassed top social media platforms. Recent and rapid use of MMAs has made it extremely difficult to ignore the existence of customer-to-customer (C2C) mobile information. This study, therefore, aims to expand the knowledge of customers' adoption behaviour of such information.Design/methodology/approachThrough applying and utilizing social support theory (SST) and the information adoption model (IAM), this study introduces a holistic theoretical model, explaining customers' adoption of information derived from MMAs and exploring the antecedents of IAM. Based on the data collected from 305 UK MMA users, this study empirically tests the research model using structural equation modelling estimation.FindingsThe results of this study reveal that social support is a key antecedent of information quality and credibility and support IAM in terms of its ability to explain MMAs' information adoption.Practical implicationsThe insights are valuable for businesses and marketers to understand customers' mobile communications and be socially support-oriented while developing marketing communication strategies.Originality/valueThe study integrates SST and IAM to improve the understanding of customers' information adoption behaviour. It is the first attempt that establishes that social support is a key antecedent of IAM.

scholarly journals Harnessing Reddit to Understand the Written-Communication Challenges Experienced by Individuals With Mental Health Disorders: Analysis of Texts From Mental Health Communities (Preprint)

2017 ◽  
Author(s):  
Albert Park ◽  
Mike Conway
Keyword(s):  
Mental Health ◽  
Bipolar Disorder ◽  
Mental Disorders ◽  
Positive Emotion ◽  
P Value ◽  
Written Communication ◽  
Lexical Diversity ◽  
Online Health Communities ◽  
Communication Challenges ◽  
Health Communities

BACKGROUND Mental disorders such as depression, bipolar disorder, and schizophrenia are common, incapacitating, and have the potential to be fatal. Despite the prevalence and gravity of mental disorders, our knowledge concerning everyday challenges associated with them is relatively limited. One of the most studied deficits related to everyday challenges is language impairment, yet we do not know how mental disorders can impact common forms of written communication, for example, social media. OBJECTIVE The aims of this study were to investigate written communication challenges manifest in online mental health communities focusing on depression, bipolar disorder, and schizophrenia, as well as the impact of participating in these online mental health communities on written communication. As the control, we selected three online health communities focusing on positive emotion, exercising, and weight management. METHODS We examined lexical diversity and readability, both important features for measuring the quality of writing. We used four well-established readability metrics that consider word frequencies and syntactic complexity to measure writers’ written communication ability. We then measured the lexical diversity by calculating the percentage of unique words in posts. To compare lexical diversity and readability among communities, we first applied pairwise independent sample t tests, followed by P value adjustments using the prespecified Hommel procedure to adjust for multiple comparison. To measure the changes, we applied linear least squares regression to the readability and lexical diversity scores against the interaction sequence for each member, followed by pairwise independent sample t tests and P value adjustments. Given the large sample of members, we also report effect sizes and 95% CIs for the pairwise comparisons. RESULTS On average, members of depression, bipolar disorder, and schizophrenia communities showed indications of difficulty expressing their ideas compared with three other online health communities. Our results also suggest that participating in these platforms has the potential to improve members’ written communication. For example, members of all three mental health communities showed statistically significant improvement in both lexical diversity and readability compared with members of the OHC focusing on positive emotion. CONCLUSIONS We provide new insights into the written communication challenges faced by individuals suffering from depression, bipolar disorder, and schizophrenia. A comparison with three other online health communities suggests that written communication in mental health communities is significantly more difficult to read, while also consisting of a significantly less diverse lexicon. We contribute practical suggestions for utilizing our findings in Web-based communication settings to enhance members’ communicative experience. We consider these findings to be an important step toward understanding and addressing everyday written communication challenges among individuals suffering from mental disorders.

scholarly journals Stopping Antidepressants and Anxiolytics as Major Concerns Reported in Online Health Communities: A Text Mining Approach

2017 ◽  
Vol 4 (4) ◽  
pp. e48 ◽  
Author(s):  
Adeline Abbe ◽  
Bruno Falissard
Keyword(s):  
Text Mining ◽  
Online Social Networks ◽  
Online Community ◽  
Self Esteem ◽  
Face To Face ◽  
Online Health Communities ◽  
Self Help ◽  
Patient Profiles ◽  
Mining Methods ◽  
Health Communities

Background Internet is a particularly dynamic way to quickly capture the perceptions of a population in real time. Complementary to traditional face-to-face communication, online social networks help patients to improve self-esteem and self-help. Objective The aim of this study was to use text mining on material from an online forum exploring patients’ concerns about treatment (antidepressants and anxiolytics). Methods Concerns about treatment were collected from discussion titles in patients’ online community related to antidepressants and anxiolytics. To examine the content of these titles automatically, we used text mining methods, such as word frequency in a document-term matrix and co-occurrence of words using a network analysis. It was thus possible to identify topics discussed on the forum. Results The forum included 2415 discussions on antidepressants and anxiolytics over a period of 3 years. After a preprocessing step, the text mining algorithm identified the 99 most frequently occurring words in titles, among which were escitalopram, withdrawal, antidepressant, venlafaxine, paroxetine, and effect. Patients’ concerns were related to antidepressant withdrawal, the need to share experience about symptoms, effects, and questions on weight gain with some drugs. Conclusions Patients’ expression on the Internet is a potential additional resource in addressing patients’ concerns about treatment. Patient profiles are close to that of patients treated in psychiatry.

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