Bio, psycho, or social: supervised machine learning to classify discursive framing of depression in online health communities

Supervised machine learning on textual data has successful industrial/business applications, but it is an open question whether it can be utilized in social knowledge building outside the scope of hermeneutically more trivial cases. Combining sociology and data science raises several methodological and epistemological questions. In our study the discursive framing of depression is explored in online health communities. Three discursive frameworks are introduced: the bio-medical, psychological, and social framings of depression. ~80 000 posts were collected, and a sample of them was manually classified. Conventional bag-of-words models, Gradient Boosting Machine, word-embedding-based models and a state-of-the-art Transformer-based model with transfer learning, called DistilBERT were applied to expand this classification on the whole database. According to our experience ‘discursive framing’ proves to be a complex and hermeneutically difficult concept, which affects the degree of both inter-annotator agreement and predictive performance. Our finding confirms that the level of inter-annotator disagreement provides a good estimate for the objective difficulty of the classification. By identifying the most important terms, we also interpreted the classification algorithms, which is of great importance in social sciences. We are convinced that machine learning techniques can extend the horizon of qualitative text analysis. Our paper supports a smooth fit of the new techniques into the traditional toolbox of social sciences.

Social Media Analytics for Pharmacovigilance of Antiepileptic Drugs

Epilepsy is a common neurological disorder worldwide and antiepileptic drug (AED) therapy is the cornerstone of its treatment. It has a laudable aim of achieving seizure freedom with minimal, if any, adverse drug reactions (ADRs). Too often, AED treatment is a long-lasting journey, in which ADRs have a crucial role in its administration. Therefore, from a pharmacovigilance perspective, detecting the ADRs of AEDs is a task of utmost importance. Typically, this task is accomplished by analyzing relevant data from spontaneous reporting systems. Despite their wide adoption for pharmacovigilance activities, the passiveness and high underreporting ratio associated with spontaneous reporting systems have encouraged the consideration of other data sources such as electronic health databases and pharmaceutical databases. Social media is the most recent alternative data source with many promising potentials to overcome the shortcomings of traditional data sources. Although in the literature some attempts have investigated the validity and utility of social media for ADR detection of different groups of drugs, none of them was dedicated to the ADRs of AEDs. Hence, this paper presents a novel investigation of the validity and utility of social media as an alternative data source for the detection of AED ADRs. To this end, a dataset of consumer reviews from two online health communities has been collected. The dataset is preprocessed; the unigram, bigram, and trigram are generated; and the ADRs of each AED are extracted with the aid of consumer health vocabulary and ADR lexicon. Three widely used measures, namely, proportional reporting ratio, reporting odds ratio, and information component, are used to measure the association between each ADR and AED. The resulting list of signaled ADRs for each AED is validated against a widely used ADR database, called Side Effect Resource, in terms of the precision of ADR detection. The validation results indicate the validity of online health community data for the detection of AED ADRs. Furthermore, the lists of signaled AED ADRs are analyzed to answer questions related to the common ADRs of AEDs and the similarities between AEDs in terms of their signaled ADRs. The consistency of the drawn answers with the existing pharmaceutical knowledge suggests the utility of the data from online health communities for AED-related knowledge discovery tasks.

Examining Online Health Community Users’ Information Adoption Intention

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.

The Development of an Experience Measure for an Online Digital Mental Health Community

Online digital mental health communities can contribute to users’ mental health positively and negatively. Yet the measurement of outcomes and impact relating to digital mental health communities is difficult to capture. In this paper we demonstrate the development of an online experience measure for a specific children and young people’s community inside a digital mental health service. The development is informed by three phases: (i) item reduction through Estimate-Talk-Estimate modified Delphi methods, (ii) user testing with participatory action research and (iii) a pilot within the digital service community to explore its use. Rounds of experts talks help to reduce the items. User experience workshops helped to inform the usability and appearance, wording, and purpose of the measure. Finally, the pilot results highlight completion rates, difference in scores for age and community roles and a preference to ‘relate to others’; as a mechanism of support. Outcomes frequently selected in the measure show the importance of certain aspects of the community, such as safety, connection, and non-judgment previously highlighted in the literature. Self-reported helpfulness scales like this one could be used as indicators of meaningful engagement within the community and its content but further research is required to ascertain its acceptability and validity. Phased approaches involving stakeholders and participatory action research enhances the development of digitally enabled measurement tools.

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

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.

Characterizing and Identifying the Prevalence of Web-Based Misinformation Relating to Medication for Opioid Use Disorder: Machine Learning Approach

Background Expanding access to and use of medication for opioid use disorder (MOUD) is a key component of overdose prevention. An important barrier to the uptake of MOUD is exposure to inaccurate and potentially harmful health misinformation on social media or web-based forums where individuals commonly seek information. There is a significant need to devise computational techniques to describe the prevalence of web-based health misinformation related to MOUD to facilitate mitigation efforts. Objective By adopting a multidisciplinary, mixed methods strategy, this paper aims to present machine learning and natural language analysis approaches to identify the characteristics and prevalence of web-based misinformation related to MOUD to inform future prevention, treatment, and response efforts. Methods The team harnessed public social media posts and comments in the English language from Twitter (6,365,245 posts), YouTube (99,386 posts), Reddit (13,483,419 posts), and Drugs-Forum (5549 posts). Leveraging public health expert annotations on a sample of 2400 of these social media posts that were found to be semantically most similar to a variety of prevailing opioid use disorder–related myths based on representational learning, the team developed a supervised machine learning classifier. This classifier identified whether a post’s language promoted one of the leading myths challenging addiction treatment: that the use of agonist therapy for MOUD is simply replacing one drug with another. Platform-level prevalence was calculated thereafter by machine labeling all unannotated posts with the classifier and noting the proportion of myth-indicative posts over all posts. Results Our results demonstrate promise in identifying social media postings that center on treatment myths about opioid use disorder with an accuracy of 91% and an area under the curve of 0.9, including how these discussions vary across platforms in terms of prevalence and linguistic characteristics, with the lowest prevalence on web-based health communities such as Reddit and Drugs-Forum and the highest on Twitter. Specifically, the prevalence of the stated MOUD myth ranged from 0.4% on web-based health communities to 0.9% on Twitter. Conclusions This work provides one of the first large-scale assessments of a key MOUD-related myth across multiple social media platforms and highlights the feasibility and importance of ongoing assessment of health misinformation related to addiction treatment.

Offline Reputation Matters: Physicians’ Online Reputation Bias in Online Health Communities (Preprint)

BACKGROUND Online health communities (OHCs) have gradually become an important service platform for patients to communicate with physicians and obtain treatment and related medical information. With the rise of OHCs, an increasing number of scholars have begun to study the incentive mechanism of a physician’s online contribution behavior. However, previous studies have ignored the limited effect of online reputation on a physician’s online contribution and the moderating effect of offline reputation. OBJECTIVE We hypothesized that online reputation shows an inverted U-shaped relation with a physician’s contribution and that offline reputation reduces the inverted U-shaped relation, thus weakening the positive impact of reputation on performance and strengthening the negative impact of reputation performance. METHODS Based on incentive theory and prior studies, we proposed three hypotheses. We collected the panel data of 6,648 physicians from Good Physician, one of the largest OHCs in China. An empirical model was built to test our hypotheses. Additionally, the variables representing online reputation were replaced and the data set was split for the robustness check. RESULTS First, our results support the hypothesis of an inverted U-shaped relationship between a physician’s online reputation and their online contribution behavior(β=-0.127, t=13.624, P<.001). Second, we identify the bipolar impact of online and offline reputation on a physician’s online contribution. Specifically, over a period of time, online reputation will promote the contribution of physicians(slope=1.514, P<.001), while offline reputation will inhibit the contribution. Third, our research shows that offline reputation can weaken the impact of online reputation on a physician’s online contribution. CONCLUSIONS We amended the bias of the positive linear correlation between online reputations and online contributions in OHCs. Moreover, by identifying the moderating effect of offline reputation, we ameliorated the interpreting mechanism of online reputation on a physician’s online contribution. The findings improve our understanding of physicians' online contribution behaviors and contribute to the literature on OHCs. Our research can provide guidance for an OHC to design effective personalized incentive mechanisms and encourage physicians to provide more high-quality services.

Data Exploration of Social Determinants of Health, Health Access and Quality in a Global Health Setting

Diseases have no borders, and global health operates from both within and beyond. Global health informatics can adopt an assets-oriented approach to mitigate concerns by maximizing global health data, principles, and resources combined with geographic information systems’ use case mapping. This exploratory study utilizes an assets-oriented approach to analyze four global social determinants of health indicators, including Skilled Birth Attendance, Measles Immunization Coverage, Education (Female), and the Healthcare Access and Quality Index in relation to countries’ income and geographical region. Data were extracted and analyzed from two publicly available datasets. Positive trends and variations were detected among all variables aggregated by countries’ income category and geographical region. These findings pinpoint potential health assets that the discipline of nursing can leverage to build healthier global health communities.

Experiences and Disease Self-Management in Individuals Living With Chronic Kidney Disease: Qualitative Analysis of the National Kidney Foundation's Online Community

Background: Self-management of chronic kidney disease (CKD) is one of the keys in improving CKD outcomes and quality of life. There has been an increased use of online health communities to share the experiences of those living with CKD. By analyzing the CKD online forum data, this study aims to: 1) understand the experiences and challenges of individuals living with CKD, and 2) explore how online communities may help CKD patients in improving CKD self-management. Methods: Publicly available posts of peer interactions on the National Kidney Foundation's online community for people affected by CKD were extracted in April 2021 using computer programming. A total of 20,436 posts were collected, of which 400 posts were analyzed using inductive thematic analysis, and saturation was reached. Two researchers coded each post independently, and discrepancies were discussed to reach consensus. Results: The analysis identified seven themes: 1) Dynamics of CKD status, 2) CKD comorbidities, 3) Managing CKD and symptoms, 4) Life participation and outlook; 5) Navigating healthcare and clinical needs, 6) Medical tests and results; and 7) Support on the forum. The results revealed that comorbidities were common in CKD patients and early-stage CKD was not communicated in a timely manner to patients by the health care community; living with CKD challenged both CKD and caregivers; some common challenges included but were not limited to the management of a diet for CKD and co-morbidities (especially co-morbid diabetes), CKD dynamics and symptoms, and fear of/ways to prevent progression. Individuals living with CKD primarily used the online forum to share and seek information and emotional support for managing CKD (including co-morbidities). Conclusions: Challenges of living with CKD were found not only in those with advanced kidney disease and those on dialysis, but also in those with early and middle stages. Information and emotional support from the online forum serves as a platform to empower CKD patients with the knowledge, skills and confidence for CKD self-management. Proactive and innovative strategies with a combination of virtual and real settings to improve self-management for individuals with all-stage CKD needs to be explored and tailored.