Topic Modeling for Analyzing Patients' Perceptions and Concerns of Hearing Loss on Social Q&A Sites: Incorporating Patients' Perspective

[Structural Topic Modeling Analysis of Patient Safety Interest among Health Consumers in Social Media]

PURPOSE

This study aimed to investigate healthcare consumers' interest in patient safety on social media using structural topic modeling (STM) and to identify changes in interest over time.

METHODS

Analyzing 105,727 posts from Naver news comments, blogs, internet cafés, and Twitter between 2010 and 2022, this study deployed a Python script for data collection and preprocessing. STM analysis was conducted using R, with the documents' publication years serving as metadata to trace the evolution of discussions on patient safety.

RESULTS

The analysis identified a total of 13 distinct topics, organized into three primary communities: (1) "Demand for systemic improvement of medical accidents," underscoring the need for legal and regulatory reform to enhance accountability; (2) "Efforts of the government and organizations for safety management," highlighting proactive risk mitigation strategies; and (3) "Medical accidents exposed in the media," reflecting widespread concerns over medical negligence and its repercussions. These findings indicate pervasive concerns regarding medical accountability and transparency among healthcare consumers.

CONCLUSION

The findings emphasize the importance of transparent healthcare policies and practices that openly address patient safety incidents. There is clear advocacy for policy reforms aimed at increasing the accountability and transparency of healthcare providers. Moreover, this study highlights the significance of educational and engagement initiatives involving healthcare consumers in fostering a culture of patient safety. Integrating consumer perspectives into patient safety strategies is crucial for developing a robust safety culture in healthcare.

Social Media Utilization in Otolaryngology: A Scoping Review

OBJECTIVE

Social media (SM) is an increasingly popular medium for the medical community to engage with patients, trainees, and colleagues. This review aimed to identify reported uses of SM in otolaryngology-head and neck surgery (OHNS), assess the quality of evidence supporting these uses, and identify gaps in the literature. With the relative lack of regulatory guidelines for the development of SM content, we hypothesized that the quality of content available on SM would be highly variable.

DATA SOURCES AND METHODS

A scoping review was performed of English-language peer-reviewed studies published to date discussing SM use in any form within OHNS. Three reviewers independently screened all abstracts. Two reviewers independently extracted data of interest from the full text of articles identified from the preliminary abstract screen.

RESULTS

171 studies were included, with 94 (54.9%) studies published between 2020 and 2022. 104 (60.8%) studies were conducted in the US. 135 (78.9%) used cross-sectional or survey-based methodology; only 7 (4.1%) were controlled studies. SM was most commonly employed for professional networking (n = 37 [21%]), and within subspecialties of otology (n = 38 [22%]) and rhinology/allergy (n = 25 [15%]). Facebook was most frequently used for study recruitment (n = 23 [13.5%]), YouTube for patient education (n = 15 [14.6%]), and Twitter for professional networking (n = 16 [9.4%]).

CONCLUSION

SM use within OHNS is increasing rapidly, with applications including patient education, professional networking, and study recruitment. Despite myriad articles, there remains a paucity of well-controlled studies. As SM becomes integrated into healthcare, particularly for applications directly impacting patient care, higher levels of evidence are needed to understand its true impact. Laryngoscope, 133:2447-2456, 2023.

Ketogenic diet: Assessing YouTube video information using quality, reliability, and text analytics methods

OBJECTIVE

Patients and the general audience refer social media platforms, such as YouTube, to learn and apply contemporary dietary methods. It is difficult for users to analyze the correctness and quality of information available on open platforms. Using scientific evaluation, this study assessed the quality, reliability, and content of YouTube videos on ketogenic diet (KD).

METHODS

Three experienced medical practitioners reviewed and evaluated 95 videos. The quality and reliability of the videos were assessed using the quality criteria for consumer health information and the global quality scale (GQS). Topic modeling and sentiment analysis were employed to determine the dominant themes and polarity of the information.

RESULTS

Three types of publishers (doctors, educational institutions, and influencers) were identified for the study. The mean length of videos posted by doctors was high at 42.24 min. The reliability and quality scores ranging from 0 (low) to 5 (high) had an average of 3.08 ± 1.14 and 3.18 ± 1.18, respectively, for sampled videos. One-way analysis of variance reveals significant differences in DISCERN and GQS scores among doctors, educational institutions, and influencers. Topic discovery identified four themes: keto versus glucose, diabetes, KD food, and major chronic diseases. Sentiment analysis reveals positive content polarity, some content shared by doctors had a neutral sentiment.

CONCLUSION

Content creators should augment the content by citing medical information and terminology. Viewers relied more on doctors for information related to KD. The aesthetic quality is high for all types of publishers. Publishers could focus on the discovered themes to create more content. Publishers should produce high-quality videos by improving esthetics (to increase engagement), and reliable medical information (to increase impact).

Readability and topics of the German Health Web: Exploratory study and text analysis

BACKGROUND

The internet has become an increasingly important resource for health information, especially for lay people. However, the information found does not necessarily comply with the user's health literacy level. Therefore, it is vital to (1) identify prominent information providers, (2) quantify the readability of written health information, and (3) to analyze how different types of information sources are suited for people with differing health literacy levels.

OBJECTIVE

In previous work, we showed the use of a focused crawler to "capture" and describe a large sample of the "German Health Web", which we call the "Sampled German Health Web" (sGHW). It includes health-related web content of the three mostly German speaking countries Germany, Austria, and Switzerland, i.e. country-code top-level domains (ccTLDs) ".de", ".at" and ".ch". Based on the crawled data, we now provide a fully automated readability and vocabulary analysis of a subsample of the sGHW, an analysis of the sGHW's graph structure covering its size, its content providers and a ratio of public to private stakeholders. In addition, we apply Latent Dirichlet Allocation (LDA) to identify topics and themes within the sGHW.

METHODS

Important web sites were identified by applying PageRank on the sGHW's graph representation. LDA was used to discover topics within the top-ranked web sites. Next, a computer-based readability and vocabulary analysis was performed on each health-related web page. Flesch Reading Ease (FRE) and the 4th Vienna formula (WSTF) were used to assess the readability. Vocabulary was assessed by a specifically trained Support Vector Machine classifier.

RESULTS

In total, n = 14,193,743 health-related web pages were collected during the study period of 370 days. The resulting host-aggregated web graph comprises 231,733 nodes connected via 429,530 edges (network diameter = 25; average path length = 6.804; average degree = 1.854; modularity = 0.723). Among 3000 top-ranked pages (1000 per ccTLD according to PageRank), 18.50%(555/3000) belong to web sites from governmental or public institutions, 18.03% (541/3000) from nonprofit organizations, 54.03% (1621/3000) from private organizations, 4.07% (122/3000) from news agencies, 3.87% (116/3000) from pharmaceutical companies, 0.90% (27/3000) from private bloggers, and 0.60% (18/3000) are from others. LDA identified 50 topics, which we grouped into 11 themes: "Research & Science", "Illness & Injury", "The State", "Healthcare structures", "Diet & Food", "Medical Specialities", "Economy", "Food production", "Health communication", "Family" and "Other". The most prevalent themes were "Research & Science" and "Illness & Injury" accounting for 21.04% and 17.92% of all topics across all ccTLDs and provider types, respectively. Our readability analysis reveals that the majority of the collected web sites is structurally difficult or very difficult to read: 84.63% (2539/3000) scored a WSTF ≥ 12, 89.70% (2691/3000) scored a FRE ≤ 49. Moreover, our vocabulary analysis shows that 44.00% (1320/3000) web sites use vocabulary that is well suited for a lay audience.

CONCLUSIONS

We were able to identify major information hubs as well as topics and themes within the sGHW. Results indicate that the readability within the sGHW is low. As a consequence, patients may face barriers, even though the vocabulary used seems appropriate from a medical perspective. In future work, the authors intend to extend their analyses to identify trustworthy health information web sites.

Exploring patient experiences and concerns in the online Cochlear implant community: A cross-sectional study and validation of automated topic modelling

OBJECTIVE

There is a paucity of research examining patient experiences of cochlear implants. We sought to use natural language processing methods to explore patient experiences and concerns in the online cochlear implant (CI) community.

MATERIALS AND METHODS

Cross-sectional study of posts on the online Reddit r/CochlearImplants forum from 1 March 2015 to 11 November 2021. Natural language processing using the BERTopic automated topic modelling technique was employed to cluster posts into semantically similar topics. Topic categorisation was manually validated by two independent reviewers and Cohen's kappa calculated to determine inter-rater reliability between machine vs human and human vs human categorisation.

RESULTS

We retrieved 987 posts from 588 unique Reddit users on the r/CochlearImplants forum. Posts were initially categorised by BERTopic into 16 different Topics, which were increased to 23 Topics following manual inspection. The most popular topics related to CI connectivity (n = 112), adults considering getting a CI (n = 107), surgery-related posts (n = 89) and day-to-day living with a CI (n = 85). Cohen's kappa among all posts was 0.62 (machine vs. human) and 0.72 (human vs. human), and among categorised posts was 0.85 (machine vs. human) and 0.84 (human vs. human).

CONCLUSIONS

This cross-sectional study of social media discussions among the online cochlear implant community identified common attitudes, experiences and concerns of patients living with, or seeking, a cochlear implant. Our validation of natural language processing methods to categorise topics shows that automated analysis of similar Otolaryngology-related content is a viable and accurate alternative to manual qualitative approaches.

Everyday life information seeking in South Korea during the COVID-19 pandemic: daily topics of information needs in social Q&A

Purpose

This study investigated information needs on COVID-19 by identifying topics discussed on social questions and answers (Q&A) about daily routines, problems, and health issues for survival. A layered model of contexts for everyday life information seeking (ELIS) was adapted for interpreting topics to better understand the contexts in which users could relate information needs.

Design/methodology/approach

Questions and answers posted on Naver Knowledge-iN were collected and analyzed during the first nine months following the outbreak. Time distribution, topic modeling, and association rule mining were applied to examine the topics on COVID-19 and their temporal variation.

Findings

Numerous topics related to the cognitive context (symptoms and masks) and situational contexts (international affairs, financial support, study, and work) were discovered. Topics related to social context were discussed moderately, but the number of questions on this topic increased with time. Strong associations were observed between terms related to symptoms, indicating their importance as a COVID-19 topic in health.

Originality/value

This study investigated topics of information needs using social Q&A data in which not only information inquiry but also information sharing coexist. The findings can help bridge the theory of ELIS to topic modeling in practice. The insights gained from this study can be used by information service providers for developing guidance and programs about how to survive during a pandemic.

Peer review

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-10-2021-0547.

Sports Information Needs in Chinese Online Q&A Community: Topic Mining Based on BERT

The online Question and Answering (Q&A) community has grown globally, allowing users to ask, discuss, and answer questions based on shared interests. As a gathering place for people’s knowledge production, collaboration, and dissemination in the current Internet scene, the online Q&A community can intuitively reflect the public’s information needs and behavior. It also collects many sports-related data and becomes an effective vehicle for comprehending mass sports information needs and disseminating sports knowledge. However, sports-related studies on the online Q&A community have rarely been reported. This study took the sports information in Zhihu, the largest Q&A community in China, as the research object to explore the public needs for sports information in China. We introduced the BERT model through a self-compiled python program and collected 391,092 sports-topic answers in the online Q&A community of Zhihu. Then, we explored the topic content, evolution trend, and user attributes of these answers. We found that the overall trend of sports information needs in Zhihu can be divided into three cycles: the London 2012 Olympic period, the Rio 2016 Olympic period, and the Tokyo 2020 Olympic period in general. The diversified content of information needs included 40 second-level themes and eight first-level themes. Male and female users had similarities and differences in sports information needs. The male and female users had the same information needs for fitness-related information. However, men were more concerned with confrontational solid sports such as basketball and football; women were more likely to care about weight loss, shape effect, and self-protection while doing sports activities. In addition, compared with men, women preferred to emphasize their gender attributes when expressing their needs for sports information to obtain more practical knowledge. In conclusion, our finding reveals that the sports community formed by the current online Q&A community in China is still a male-dominated information field.

An Application of Machine Learning Techniques to Analyze Patient Information to Improve Oral Health Outcomes

Objective

Various health-related fields have applied Machine learning (ML) techniques such as text mining, topic modeling (TM), and artificial neural networks (ANN) to automate tasks otherwise completed by humans to enhance patient care. However, research in dentistry on the integration of these techniques into the clinic arena has yet to exist. Thus, the purpose of this study was to: introduce a method of automating the reviewing patient chart information using ML, provide a step-by-step description of how it was conducted, and demonstrate this method's potential to identify predictive relationships between patient chart information and important oral health-related contributors.

Methods

A secondary data analysis was conducted to demonstrate the approach on a set of anonymized patient charts collected from a dental clinic. Two ML applications for patient chart review were demonstrated: (1) text mining and Latent Dirichlet Allocation (LDA) were used to preprocess, model, and cluster data in a narrative format and extract common topics for further analysis, (2) Ordinal logistic regression (OLR) and ANN were used to determine predictive relationships between the extracted patient chart data topics and oral health-related contributors. All analysis was conducted in R and SPSS (IBM, SPSS, statistics 22).

Results

Data from 785 patient charts were analyzed. Preprocessing of raw data (data cleaning and categorizing) identified 66 variables, of which 45 were included for analysis. Using LDA, 10 radiographic findings topics and 8 treatment planning topics were extracted from the data. OLR showed that caries risk, occlusal risk, biomechanical risk, gingival recession, periodontitis, gingivitis, assisted mouth opening, and muscle tenderness were highly predictable using the extracted radiographic and treatment planning topics and chart information. Using the statistically significant predictors obtained from OLR, ANN analysis showed that the model can correctly predict >72% of all variables except for bruxism and tooth crowding (63.1 and 68.9%, respectively).

Conclusion

Our study presents a novel approach to address the need for data-enabled innovations in the field of dentistry and creates new areas of research in dental analytics. Utilizing ML methods and its application in dental practice has the potential to improve clinicians' and patients' understanding of the major factors that contribute to oral health diseases/conditions.

A Comparison of the Use of Smart Devices, Apps, and Social Media Between Adults With and Without Hearing Impairment: Cross-sectional Web-Based Study

Background

eHealth and social media could be of particular benefit to adults with hearing impairment, but it is unknown whether their use of smart devices, apps, and social media is similar to that of the general population.

Objective

Our aim is to study whether adults with normal hearing and those with impaired hearing differ in their weekly use of smart devices, apps, and social media; reasons for using social media; and benefits from using social media.

Methods

We used data from a Dutch cohort, the National Longitudinal Study on Hearing. Data were collected from September 2016 to April 2020 using a web-based questionnaire and speech-in-noise test. The results from this test were used to categorize normal hearing and hearing impairment. Outcomes were compared using (multiple) logistic regression models.

Results

Adults with impaired hearing (n=384) did not differ from normal hearing adults (n=341) in their use of a smartphone or tablet. They were less likely to make use of social media apps on a smartphone, tablet, or smartwatch (age-adjusted odds ratio [OR] 0.67, 95% CI 0.48-0.92; P=.02). Use of social media on all devices and use of other apps did not differ. Adults with hearing impairment were more likely to agree with using social media to stay in touch with family members (OR 1.54, 95% CI 1.16-2.07; P=.003) and friends (age-adjusted OR 1.35, 95% CI 1.01-1.81; P=.046). Furthermore, they were more likely to agree with using social media to perform their work (age-adjusted OR 1.51, 95% CI 1.04-2.18; P=.03). There were no differences in the experienced benefits from social media.

Conclusions

The potential for eHealth is confirmed because adults with hearing impairment are not less likely to use smart devices than their normal hearing peers. Adults with hearing impairment are less likely to use social media apps on a smart device but not less likely to use social media on all types of internet-connected devices. This warrants further research on the types of social media platforms that adults with hearing impairment use and on the type of device on which they prefer to use social media. Given that participants with hearing impairment are more likely than their normal hearing peers to use social media to perform their work, use of social media may be seen as an opportunity to enhance vocational rehabilitation services for persons with hearing impairment.