Detecting health misinformation in online health communities: Incorporating behavioral features into machine learning based approaches

Prevalence of Health Misinformation on Social Media-Challenges and Mitigation Before, During, and Beyond the COVID-19 Pandemic: Scoping Literature Review

BACKGROUND

This scoping review accompanies our research study "The Experience of Health Professionals With Misinformation and Its Impact on Their Job Practice: Qualitative Interview Study." It surveys online health misinformation and is intended to provide an understanding of the communication context in which health professionals must operate.

OBJECTIVE

Our objective was to illustrate the impact of social media in introducing additional sources of misinformation that impact health practitioners' ability to communicate effectively with their patients. In addition, we considered how the level of knowledge of practitioners mitigated the effect of misinformation and additional stress factors associated with dealing with outbreaks, such as the COVID-19 pandemic, that affect communication with patients.

METHODS

This study used a 5-step scoping review methodology following Arksey and O'Malley's methodology to map relevant literature published in English between January 2012 and March 2024, focusing on health misinformation on social media platforms. We defined health misinformation as a false or misleading health-related claim that is not based on valid evidence or scientific knowledge. Electronic searches were performed on PubMed, Scopus, Web of Science, and Google Scholar. We included studies on the extent and impact of health misinformation in social media, mitigation strategies, and health practitioners' experiences of confronting health misinformation. Our independent reviewers identified relevant articles for data extraction.

RESULTS

Our review synthesized findings from 70 sources on online health misinformation. It revealed a consensus regarding the significant problem of health misinformation disseminated on social network platforms. While users seek trustworthy sources of health information, they often lack adequate health and digital literacies, which is exacerbated by social and economic inequalities. Cultural contexts influence the reception of such misinformation, and health practitioners may be vulnerable, too. The effectiveness of online mitigation strategies like user correction and automatic detection are complicated by malicious actors and politicization. The role of health practitioners in this context is a challenging one. Although they are still best placed to combat health misinformation, this review identified stressors that create barriers to their abilities to do this well. Investment in health information management at local and global levels could enhance their capacity for effective communication with patients.

CONCLUSIONS

This scoping review underscores the significance of addressing online health misinformation, particularly in the postpandemic era. It highlights the necessity for a collaborative global interdisciplinary effort to ensure equitable access to accurate health information, thereby empowering health practitioners to effectively combat the impact of online health misinformation. Academic research will need to be disseminated into the public domain in a way that is accessible to the public. Without equipping populations with health and digital literacies, the prevalence of online health misinformation will continue to pose a threat to global public health efforts.

Have we found a solution for health misinformation? A ten-year systematic review of health misinformation literature 2013-2022

BACKGROUND

Health misinformation (HM) has emerged as a prominent social issue in recent years, driven by declining public trust, popularisation of digital media platforms and escalating public health crisis. Since the Covid-19 pandemic, HM has raised critical concerns due to its significant impacts on both individuals and society as a whole. A comprehensive understanding of HM and HM-related studies would be instrumental in identifying possible solutions to address HM and the associated challenges.

METHODS

Following the PRISMA procedure, 11,739 papers published from January 2013 to December 2022 were retrieved from five electronic databases, and 813 papers matching the inclusion criteria were retained for further analysis. This article critically reviewed HM-related studies, detailing the factors facilitating HM creation and dissemination, negative impacts of HM, solutions to HM, and research methods employed in those studies.

RESULTS

A growing number of studies have focused on HM since 2013. Results of this study highlight that trust plays a significant while latent role in the circuits of HM, facilitating the creation and dissemination of HM, exacerbating the negative impacts of HM and amplifying the difficulty in addressing HM.

CONCLUSION

For health authorities and governmental institutions, it is essential to systematically build public trust in order to reduce the probability of individuals acceptation of HM and to improve the effectiveness of misinformation correction. Future studies should pay more attention to the role of trust in how to address HM.

A Serious Game ("Fight With Virus") for Preventing COVID-19 Health Rumors: Development and Experimental Study

BACKGROUND

Health rumors arbitrarily spread in mainstream social media on the internet. Health rumors emerged in China during the outbreak of COVID-19 in early 2020. Many midelders/elders (age over 40 years) who lived in Wuhan believed these rumors.

OBJECTIVE

This study focused on designing a serious game as an experimental program to prevent and control health rumors. The focus of the study was explicitly on the context of the social networking service for midelders/elders.

METHODS

This research involved 2 major parts: adopting the Transmission Control Protocol model for games and then, based on the model, designing a game named "Fight With Virus" as an experimental platform and developing a cognitive questionnaire with a 5-point Likert scale. The relevant variables for this experimental study were defined, and 10 hypotheses were proposed and tested with an empirical study. In total, 200 participants were selected for the experiments. By collecting relevant data in the experiments, we conducted statistical observations and comparative analysis to test whether the experimental hypotheses could be proved.

RESULTS

We noted that compared to traditional media, serious games are more capable of inspiring interest in research participants toward their understanding of the knowledge and learning of health commonsense. In judging and recognizing the COVID-19 health rumor, the test group that used game education had a stronger ability regarding identification of the rumor and a higher accuracy rate of identification. Results showed that the more educated midelders/elders are, the more effective they are at using serious games.

CONCLUSIONS

Compared to traditional media, serious games can effectively improve midelders'/elders' cognitive abilities while they face a health rumor. The gameplay effect is related to the individual's age and educational background, while income and gender have no impact.

Personalizing Communication of Clinicians with Chronically Ill Elders in Digital Encounters-A Patient-Centered View

BACKGROUND

Chronically ill elderly patients are concerned about losing the personal connection with clinicians in digital encounters and clinicians are concerned about missing nonverbal cues that are important for the diagnosis, thus jeopardizing quality of care.

AIMS

This study validated the expectations and preferences of chronically ill elderly patients regarding specific communication messages for communication with clinicians in telemedicine.

METHODS

The sample comprised 600 elderly chronically ill patients who use telehealth. We used a conjoint-based experimental design to test numerous messages. The outcome variable is elder patient expectations from communication with clinicians in telemedicine. The independent variables were known categories of patient-clinician communication. Respondents rated each of the 24 vignettes of messages.

RESULTS

Mathematical clustering yielded three mindsets, with statistically significant differences among them. Members of mindset 1 were most concerned with non-verbal communication, members of mindset 2 prefer communication that enhances the internal locus of control, and members of mindset 3 have an external locus of control and strongly oppose any dialogue about their expectations from communication.

CONCLUSIONS

The use of the predictive algorithm that we developed enables clinicians to identify the belonging of each chronically ill elderly patient in the clinic to a sample mindset, and to accordingly personalize the communication in the digital encounters while structuring the encounter with greater specificity, therefore enhancing patient-centered care.

An Environmental Uncertainty Perception Framework for Misinformation Detection and Spread Prediction in the COVID-19 Pandemic: Artificial Intelligence Approach

BACKGROUND

Amidst the COVID-19 pandemic, misinformation on social media has posed significant threats to public health. Detecting and predicting the spread of misinformation are crucial for mitigating its adverse effects. However, prevailing frameworks for these tasks have predominantly focused on post-level signals of misinformation, neglecting features of the broader information environment where misinformation originates and proliferates.

OBJECTIVE

This study aims to create a novel framework that integrates the uncertainty of the information environment into misinformation features, with the goal of enhancing the model's accuracy in tasks such as misinformation detection and predicting the scale of dissemination. The objective is to provide better support for online governance efforts during health crises.

METHODS

In this study, we embraced uncertainty features within the information environment and introduced a novel Environmental Uncertainty Perception (EUP) framework for the detection of misinformation and the prediction of its spread on social media. The framework encompasses uncertainty at 4 scales of the information environment: physical environment, macro-media environment, micro-communicative environment, and message framing. We assessed the effectiveness of the EUP using real-world COVID-19 misinformation data sets.

RESULTS

The experimental results demonstrated that the EUP alone achieved notably good performance, with detection accuracy at 0.753 and prediction accuracy at 0.71. These results were comparable to state-of-the-art baseline models such as bidirectional long short-term memory (BiLSTM; detection accuracy 0.733 and prediction accuracy 0.707) and bidirectional encoder representations from transformers (BERT; detection accuracy 0.755 and prediction accuracy 0.728). Additionally, when the baseline models collaborated with the EUP, they exhibited improved accuracy by an average of 1.98% for the misinformation detection and 2.4% for spread-prediction tasks. On unbalanced data sets, the EUP yielded relative improvements of 21.5% and 5.7% in macro-F1-score and area under the curve, respectively.

CONCLUSIONS

This study makes a significant contribution to the literature by recognizing uncertainty features within information environments as a crucial factor for improving misinformation detection and spread-prediction algorithms during the pandemic. The research elaborates on the complexities of uncertain information environments for misinformation across 4 distinct scales, including the physical environment, macro-media environment, micro-communicative environment, and message framing. The findings underscore the effectiveness of incorporating uncertainty into misinformation detection and spread prediction, providing an interdisciplinary and easily implementable framework for the field.

How to detect fake online physician reviews: A deep learning approach

Objective

The COVID-19 pandemic has spurred an increased interest in online healthcare and a surge in usage of online healthcare platforms, leading to a proliferation of user-generated online physician reviews. Yet, distinguishing between genuine and fake reviews poses a significant challenge. This study aims to address the challenges delineated above by developing a reliable and effective fake review detection model leveraging deep learning approaches based on a fake review dataset tailored to the context of Chinese online medical platforms.

Methods

Inspired by prior research, this paper adopts a crowdsourcing approach to assemble the fake review dataset for Chinese online medical platforms. To develop the fake review detection models, classical machine learning models, along with deep learning models such as Convolutional Neural Network and Bidirectional Encoder Representations from Transformers, were applied.

Results

Our experimental deep learning model exhibited superior performance in identifying fake reviews on online medical platforms, achieving a precision of 98.36% and an F2-Score of 97.97%. Compared to the traditional machine learning models (i.e., logistic regression, support vector machine, random forest, ridge regression), this represents an 8.16% enhancement in precision and a 7.7% increase in F2-Score.

Conclusion

Overall, this study provides a valuable contribution toward the development of an effective fake physician review detection model for online medical platforms.

Detecting nuance in conspiracy discourse: Advancing methods in infodemiology and communication science with machine learning and qualitative content coding

The spread of misinformation and conspiracies has been an ongoing issue since the early stages of the internet era, resulting in the emergence of the field of infodemiology (i.e., information epidemiology), which investigates the transmission of health-related information. Due to the high volume of online misinformation in recent years, there is a need to continue advancing methodologies in order to effectively identify narratives and themes. While machine learning models can be used to detect misinformation and conspiracies, these models are limited in their generalizability to other datasets and misinformation phenomenon, and are often unable to detect implicit meanings in text that require contextual knowledge. To rapidly detect evolving conspiracist narratives within high volume online discourse while identifying nuanced themes requiring the comprehension of subtext, this study describes a hybrid methodology that combines natural language processing (i.e., topic modeling and sentiment analysis) with qualitative content coding approaches to characterize conspiracy discourse related to 5G wireless technology and COVID-19 on Twitter (currently known as 'X'). Discourse that focused on correcting 5G conspiracies was also analyzed for comparison. Sentiment analysis shows that conspiracy-related discourse was more likely to use language that was analytic, combative, past-oriented, referenced social status, and expressed negative emotions. Corrections discourse was more likely to use words reflecting cognitive processes, prosocial relations, health-related consequences, and future-oriented language. Inductive coding characterized conspiracist narratives related to global elites, anti-vax sentiment, medical authorities, religious figures, and false correlations between technology advancements and disease outbreaks. Further, the corrections discourse did not address many of the narratives prevalent in conspiracy conversations. This paper aims to further bridge the gap between computational and qualitative methodologies by demonstrating how both approaches can be used in tandem to emphasize the positive aspects of each methodology while minimizing their respective drawbacks.

Autistic discussion forums: insights into the topics that clinicians don't know about

Background

User-led autism discussion forums provide a wealth of information about autistic lived experiences, albeit oriented toward those who regularly use computers. We contend that healthcare professionals should read autism discussion forums to gain insight, be informed, and in some cases, to correct assumptions about autistic persons' lives and possibilities. But experts may be dismissive of user-led forums, believing forums to be filled with myths, misinformation, and combative postings. The questions motivating our research were: Do online forums raise issues that are educational for clinicians and other stakeholders? Are forums useful for those who do empirical research?

Method

Content analysis was conducted on 300 posts (62,000 words) from Reddit, Quora, and Wrong Planet. Forums were sampled to reflect broad topics; posts were selected sequentially from the identified forums. The authors read through posts in the Excel sheet, highlighting statements that were the main ideas of the post, to discern both broad categories of topics and more specific topics. We coded content pertinent to classic autism myths and analyzed attitudes towards myths such as 'lack emotion' and 'cannot form relationships.' To document whether forum posts discuss topics that are not widely known outside of elite experts, we compared discussion content to new material about autism contained in the March 2022 DSM 5 Text revision.

Results

Classic autism myths were discussed with examples of when elements of myths may be valid. Posters described cases where parents or therapists believed myths. Experts may believe autism myths due to rapid changes in diagnostic practices and due to their lack of knowledge regarding the characteristics of autistic people who have typical intellectual abilities. We conclude that forums contain high-value information for clinicians because all concepts in the DSM 5 text revision were discussed by posters in the years before the text revision appeared. Ideas that are only slowly becoming part of the research literature are discussed at length in forums. Reading and analyzing forums is useful for both clinicians and scientists. In addition, the relative ease of forum analysis lowers the bar for entry into the research process.

The "Loci" of Misinformation and Its Correction in Peer- and Expert-Led Online Communities for Mental Health: Content Analysis

BACKGROUND

Mental health problems are recognized as a pressing public health issue, and an increasing number of individuals are turning to online communities for mental health to search for information and support. Although these virtual platforms have the potential to provide emotional support and access to anecdotal experiences, they can also present users with large amounts of potentially inaccurate information. Despite the importance of this issue, limited research has been conducted, especially on the differences that might emerge due to the type of content moderation of online communities: peer-led or expert-led.

OBJECTIVE

We aim to fill this gap by examining the prevalence, the communicative context, and the persistence of mental health misinformation on Facebook online communities for mental health, with a focus on understanding the mechanisms that enable effective correction of inaccurate information and differences between expert-led and peer-led groups.

METHODS

We conducted a content analysis of 1534 statements (from 144 threads) in 2 Italian-speaking Facebook groups.

RESULTS

The study found that an alarming number of comments (26.1%) contained medically inaccurate information. Furthermore, nearly 60% of the threads presented at least one misinformation statement without any correction attempt. Moderators were more likely to correct misinformation than members; however, they were not immune to posting content containing misinformation, which was an unexpected finding. Discussions about aspects of treatment (including side effects or treatment interruption) significantly increased the probability of encountering misinformation. Additionally, the study found that misinformation produced in the comments of a thread, rather than as the first post, had a lower probability of being corrected, particularly in peer-led communities.

CONCLUSIONS

The high prevalence of misinformation in online communities, particularly when left uncorrected, underscores the importance of conducting additional research to identify effective mechanisms to prevent its spread. This is especially important given the study's finding that misinformation tends to be more prevalent around specific "loci" of discussion that, once identified, can serve as a starting point to develop strategies for preventing and correcting misinformation within them.

Sharing health information across online platforms: A systematic review

Advanced Web 2.0 communication technologies have facilitated health-related information (HRI) sharing on the Internet. Especially, the COVID-19 pandemic and lockdowns around the world have forced more people to turn to the Internet for HRI. A better understanding of users' sharing content and sharing behavior can help communicators improve health literacy, raise community awareness, and facilitate social support exchanges. This paper reports the results of a systematic review of online HRI sharing literature, including key research topics, theories and methods used in past studies, and key factors of sharing behavior across online platforms. Following the PRISMA procedure for a systematic review, 58 articles were identified and analyzed using keyword matching, thematic analysis, and expert review. Guided by the platform theory, our findings differentiated five types of online platforms that differently influenced online users' sharing content and sharing purposes, including micro-blogs, social network sites, online health communities, social question and answer sites, and Wikis. The findings also clarify five main research topics and applicable theories used in each topic, including personal health sharing, health-related knowledge sharing, general health message diffusion, outcomes of HRI sharing, and exploratory research. Key factors of sharing behavior and potential sharing outcomes are also reviewed and summarized in the research framework developed from the motivation theory. Our study contributes to the understanding of online sharing behavior and provides implications for health communicators to develop effective health campaigns. Potential research directions are also identified and discussed.

Understanding the role of social media usage and health self-efficacy in the processing of COVID-19 rumors: A SOR perspective

Apart from the direct health and behavioral influence of the COVID-19 pandemic itself, COVID-19 rumors as an infodemic enormously amplified public anxiety and cause serious outcomes. Although factors influencing such rumors propagation have been widely studied by previous studies, the role of spatial factors (e.g., proximity to the pandemic) on individuals' response regarding COVID-19 rumors remain largely unexplored. Accordingly, this study, drawing on the stimulus-organism-response (SOR) framework, examined how proximity to the pandemic (stimulus) influences anxiety (organism), which in turn determines rumor beliefs and rumor outcomes (response). Further, the contingent role of social media usage and health self-efficacy were tested. The research model was tested using 1246 samples via an online survey during the COVID-19 pandemic in China. The results indicate that: (1)The proximity closer the public is to the pandemic, the higher their perceived anxiety; (2) Anxiety increases rumor beliefs, which is further positively associated rumor outcomes; (3) When the level of social media usage is high, the relationship between proximity to the pandemic and anxiety is strengthened; (4) When the level of health self-efficacy is high, the effect of anxiety on rumor beliefs is strengthened and the effect of rumor beliefs on rumor outcomes is also strengthened. This study provides a better understanding of the underlying mechanism of the propagation of COVID-19 rumors from a SOR perspective. Additionally, this paper is one of the first that proposes and empirically verifies the contingent role of social media usage and health self-efficacy on the SOR framework. The findings of study can assist the pandemic prevention department in to efficiently manage rumors with the aim of alleviating public anxiety and avoiding negative outcomes cause by rumors.

Automatic detection of health misinformation: a systematic review

The spread of health misinformation has the potential to cause serious harm to public health, from leading to vaccine hesitancy to adoption of unproven disease treatments. In addition, it could have other effects on society such as an increase in hate speech towards ethnic groups or medical experts. To counteract the sheer amount of misinformation, there is a need to use automatic detection methods. In this paper we conduct a systematic review of the computer science literature exploring text mining techniques and machine learning methods to detect health misinformation. To organize the reviewed papers, we propose a taxonomy, examine publicly available datasets, and conduct a content-based analysis to investigate analogies and differences among Covid-19 datasets and datasets related to other health domains. Finally, we describe open challenges and conclude with future directions.

CNFRD: A Few-Shot Rumor Detection Framework via Capsule Network for COVID-19

In recent years, COVID-19 has become the hottest topic. Various issues, such as epidemic transmission routes and preventive measures, have “occupied” several online social media platforms. Many rumors about COVID-19 have also arisen, causing public anxiety and seriously affecting normal social order. Identifying a rumor at its very inception is crucial to reducing the potential harm of its evolution to society as a whole. However, epidemic rumors provide limited signal features in the early stage. In order to identify rumors with data sparsity, we propose a few-shot learning rumor detection model based on capsule networks (CNFRD), utilizing the metric learning framework and the capsule network to detect the rumors posted during unexpected epidemic events. Specifically, we constructively use the capsule network neural layer to summarize the historical rumor data and obtain the generalized class representation based on the historical rumor data samples. Besides, we calculate the distance between the epidemic rumor sample and the historical rumor class-wise representation according to the metric module. Finally, epidemic rumors are discriminated against according to the nearest neighbor principle. The experimental results prove that the proposed method can achieve higher accuracy with fewer epidemic rumor samples. This approach provided 88.92% accuracy on the Chinese rumor dataset and 87.07% accuracy on the English rumor dataset, which improved by 7% to 23% over existing approaches. Therefore, the CNFRD model can identify epidemic rumors in COVID-19 as early as possible and effectively improve the performance of rumor detection.

The quality of informational social support in online health communities: A content analysis of cancer-related discussions

Objective

Informational social support is one of the main reasons for patients to visit online health communities (OHCs). Calls have been made to investigate the objective quality of such support in the light of a worrying number of inaccurate online health-related information. The main aim of this study is to conceptualize the Quality of Informational Social Support (QISS) and develop and test a measure of QISS for content analysis. A further aim is to investigate the level of QISS in cancer-related messages in the largest OHC in Slovenia and examine the differences among various types of discussion forums, namely, online consultation forums, online support group forums, and socializing forums.

Methods

A multidimensional measurement instrument was developed, which included 20 items in a coding scheme for a content analysis of cancer-related messages. On a set of almost three million posts published between 2015 and 2019, a machine-learning algorithm was used to detect cancer-related discussions in the OHC. We then identified the messages providing informational social support, and through quantitative content analysis, three experts coded a random sample of 403 cancer-related messages for the QISS.

Results

The results demonstrate a good level of interrater reliability and agreement for a QISS scale with six dimensions, each demonstrating good internal consistency. The results reveal large differences among the social support, socializing, and consultation forums, with the latter recording significantly higher quality in terms of accuracy (M = 4.48, P < .001), trustworthiness (M = 4.65, P < .001), relevance (M = 3.59, P < .001), and justification (M = 3.81, P = .05) in messages providing informational social support regarding cancer-related issues.

Conclusions

This study provides the research field with a valid tool to further investigate the factors and consequences of varying quality of information exchanged in supportive communication. From a practical perspective, OHCs should dedicate more resources and develop mechanisms for the professional moderation of health-related topics in socializing forums and thereby suppress the publication and dissemination of low-quality information among OHC users and visitors.

Detecting and classifying online health misinformation with 'Content Similarity Measure (CSM)' algorithm: an automated fact-checking-based approach

Information dissemination occurs through the 'word of media' in the digital world. Fraudulent and deceitful content, such as misinformation, has detrimental effects on people. An implicit fact-based automated fact-checking technique comprising information retrieval, natural language processing, and machine learning techniques assist in assessing the credibility of content and detecting misinformation. Previous studies focused on linguistic and textual features and similarity measures-based approaches. However, these studies need to gain knowledge of facts, and similarity measures are less accurate when dealing with sparse or zero data. To fill these gaps, we propose a 'Content Similarity Measure (CSM)' algorithm that can perform automated fact-checking of URLs in the healthcare domain. Authors have introduced a novel set of content similarity, domain-specific, and sentiment polarity score features to achieve journalistic fact-checking. An extensive analysis of the proposed algorithm compared with standard similarity measures and machine learning classifiers showed that the 'content similarity score' feature outperformed other features with an accuracy of 88.26%. In the algorithmic approach, CSM showed improved accuracy of 91.06% compared to the Jaccard similarity measure with 74.26% accuracy. Another observation is that the algorithmic approach outperformed the feature-based method. To check the robustness of the algorithms, authors have tested the model on three state-of-the-art datasets, viz. CoAID, FakeHealth, and ReCOVery. With the algorithmic approach, CSM showed the highest accuracy of 87.30%, 89.30%, 85.26%, and 88.83% on CoAID, ReCOVery, FakeHealth (Story), and FakeHealth (Release) datasets, respectively. With a feature-based approach, the proposed CSM showed the highest accuracy of 85.93%, 87.97%, 83.92%, and 86.80%, respectively.

Visualizing the Interpretation of a Criteria-Driven System That Automatically Evaluates the Quality of Health News: Exploratory Study of 2 Approaches

BACKGROUND

Machine learning techniques have been shown to be efficient in identifying health misinformation, but the results may not be trusted unless they can be justified in a way that is understandable.

OBJECTIVE

This study aimed to provide a new criteria-based system to assess and justify health news quality. Using a subset of an existing set of criteria, this study compared the feasibility of 2 alternative methods for adding interpretability. Both methods used classification and highlighting to visualize sentence-level evidence.

METHODS

A total of 3 out of 10 well-established criteria were chosen for experimentation, namely whether the health news discussed the costs of the intervention (the cost criterion), explained or quantified the harms of the intervention (the harm criterion), and identified the conflicts of interest (the conflict criterion). The first step of the experiment was to automate the evaluation of the 3 criteria by developing a sentence-level classifier. We tested Logistic Regression, Naive Bayes, Support Vector Machine, and Random Forest algorithms. Next, we compared the 2 visualization approaches. For the first approach, we calculated word feature weights, which explained how classification models distill keywords that contribute to the prediction; then, using the local interpretable model-agnostic explanation framework, we selected keywords associated with the classified criterion at the document level; and finally, the system selected and highlighted sentences with keywords. For the second approach, we extracted sentences that provided evidence to support the evaluation result from 100 health news articles; based on these results, we trained a typology classification model at the sentence level; and then, the system highlighted a positive sentence instance for the result justification. The number of sentences to highlight was determined by a preset threshold empirically determined using the average accuracy.

RESULTS

The automatic evaluation of health news on the cost, harm, and conflict criteria achieved average area under the curve scores of 0.88, 0.76, and 0.73, respectively, after 50 repetitions of 10-fold cross-validation. We found that both approaches could successfully visualize the interpretation of the system but that the performance of the 2 approaches varied by criterion and highlighting the accuracy decreased as the number of highlighted sentences increased. When the threshold accuracy was ≥75%, this resulted in a visualization with a variable length ranging from 1 to 6 sentences.

CONCLUSIONS

We provided 2 approaches to interpret criteria-based health news evaluation models tested on 3 criteria. This method incorporated rule-based and statistical machine learning approaches. The results suggested that one might visually interpret an automatic criterion-based health news quality evaluation successfully using either approach; however, larger differences may arise when multiple quality-related criteria are considered. This study can increase public trust in computerized health information evaluation.

The Experience of Health Professionals With Misinformation and Its Impact on Their Job Practice: Qualitative Interview Study

Background

Misinformation is often disseminated through social media, where information is spread rapidly and easily. Misinformation affects many patients' decisions to follow a treatment prescribed by health professionals (HPs). For example, chronic patients (eg, those with diabetes) may not follow their prescribed treatment plans. During the recent pandemic, misinformed people rejected COVID-19 vaccines and public health measures, such as masking and physical distancing, and used unproven treatments.

Objective

This study investigated the impact of health-threatening misinformation on the practices of health care professionals in the United Kingdom, especially during the outbreaks of diseases where a great amount of health-threatening misinformation is produced and released. The study examined the misinformation surrounding the COVID-19 outbreak to determine how it may have impacted practitioners' perceptions of misinformation and how that may have influenced their practice. In particular, this study explored the answers to the following questions: How do HPs react when they learn that a patient has been misinformed? What misinformation do they believe has the greatest impact on medical practice? What aspects of change and intervention in HPs' practice are in response to misinformation?

Methods

This research followed a qualitative approach to collect rich data from a smaller subset of health care practitioners working in the United Kingdom. Data were collected through 1-to-1 online interviews with 13 health practitioners, including junior and senior physicians and nurses in the United Kingdom.

Results

Research findings indicated that HPs view misinformation in different ways according to the scenario in which it occurs. Some HPs consider it to be an acute incident exacerbated by the pandemic, while others see it as an ongoing phenomenon (always present) and address it as part of their daily work. HPs are developing pathways for dealing with misinformation. Two main pathways were identified: first, to educate the patient through coaching, advising, or patronizing and, second, to devote resources, such as time and effort, to facilitate 2-way communication between the patient and the health care provider through listening and talking to them.

Conclusions

HPs do not receive the confidence they deserve from patients. The lack of trust in health care practitioners has been attributed to several factors, including (1) trusting alternative sources of information (eg, social media) (2) patients' doubts about HPs' experience (eg, a junior doctor with limited experience), and (3) limited time and availability for patients, especially during the pandemic. There are 2 dimensions of trust: patient-HP trust and patient-information trust. There are 2 necessary actions to address the issue of lack of trust in these dimensions: (1) building trust and (2) maintaining trust. The main recommendations of the HPs are to listen to patients, give them more time, and seek evidence-based resources.

Improving medical experts' efficiency of misinformation detection: an exploratory study

Fighting medical disinformation in the era of the pandemic is an increasingly important problem. Today, automatic systems for assessing the credibility of medical information do not offer sufficient precision, so human supervision and the involvement of medical expert annotators are required. Our work aims to optimize the utilization of medical experts' time. We also equip them with tools for semi-automatic initial verification of the credibility of the annotated content. We introduce a general framework for filtering medical statements that do not require manual evaluation by medical experts, thus focusing annotation efforts on non-credible medical statements. Our framework is based on the construction of filtering classifiers adapted to narrow thematic categories. This allows medical experts to fact-check and identify over two times more non-credible medical statements in a given time interval without applying any changes to the annotation flow. We verify our results across a broad spectrum of medical topic areas. We perform quantitative, as well as exploratory analysis on our output data. We also point out how those filtering classifiers can be modified to provide experts with different types of feedback without any loss of performance.

Influence of information attributes on information dissemination in public health emergencies

When public health emergencies occur, relevant information containing different topics, sentiments, and emotions spread rapidly on social media. From the cognitive and emotional dimensions, this paper explores the relationship between information attributes and information dissemination behavior. At the same time, the moderating role of the media factor (user influence) and the time factor (life cycle) in information attributes and information transmission is also discussed. The results confirm differences in the spread of posts under different topic types, sentiment types, and emotion types on social media. At the same time, the study also found that posts published by users with a high number of followers and users of a media type are more likely to spread on social media. In addition, the study also found that posts with different information attributes are easier to spread on social media during the outbreak and recurrence periods. The driving effect of life cycles is more obvious, especially for topics of prayer and fact, negative sentiment, emotions of fear, and anger. Relevant findings have specific contributions to the information governance of public opinion, the development of social media theory, and the maintenance of network order, which can further weaken the negative impact of information epidemic in the occurrence of public health emergencies, maintain normal social order, and thus create favorable conditions for the further promotion of global recovery.

Linguistic features and psychological states: A machine-learning based approach

Previous research mostly used simplistic measures and limited linguistic features (e.g., personal pronouns, absolutist words, and sentiment words) in a text to identify its author’s psychological states. In this study, we proposed using additional linguistic features, that is, sentiments polarities and emotions, to classify texts of various psychological states. A large dataset of forum posts including texts of anxiety, depression, suicide ideation, and normal states were experimented with machine-learning algorithms. The results showed that the proposed linguistic features with machine-learning algorithms, namely Support Vector Machine and Deep Learning achieved a high level of performance in the detection of psychological state. The study represents one of the first attempts that uses sentiment polarities and emotions to detect texts of psychological states, and the findings may contribute to our understanding of how accuracy may be enhanced in the detection of various psychological states. Significance and suggestions of the study are also offered.

Pretrained Transformer Language Models Versus Pretrained Word Embeddings for the Detection of Accurate Health Information on Arabic Social Media: Comparative Study

Background

In recent years, social media has become a major channel for health-related information in Saudi Arabia. Prior health informatics studies have suggested that a large proportion of health-related posts on social media are inaccurate. Given the subject matter and the scale of dissemination of such information, it is important to be able to automatically discriminate between accurate and inaccurate health-related posts in Arabic.

Objective

The first aim of this study is to generate a data set of generic health-related tweets in Arabic, labeled as either accurate or inaccurate health information. The second aim is to leverage this data set to train a state-of-the-art deep learning model for detecting the accuracy of health-related tweets in Arabic. In particular, this study aims to train and compare the performance of multiple deep learning models that use pretrained word embeddings and transformer language models.

Methods

We used 900 health-related tweets from a previously published data set extracted between July 15, 2019, and August 31, 2019. Furthermore, we applied a pretrained model to extract an additional 900 health-related tweets from a second data set collected specifically for this study between March 1, 2019, and April 15, 2019. The 1800 tweets were labeled by 2 physicians as accurate, inaccurate, or unsure. The physicians agreed on 43.3% (779/1800) of tweets, which were thus labeled as accurate or inaccurate. A total of 9 variations of the pretrained transformer language models were then trained and validated on 79.9% (623/779 tweets) of the data set and tested on 20% (156/779 tweets) of the data set. For comparison, we also trained a bidirectional long short-term memory model with 7 different pretrained word embeddings as the input layer on the same data set. The models were compared in terms of their accuracy, precision, recall, F₁ score, and macroaverage of the F₁ score.

Results

We constructed a data set of labeled tweets, 38% (296/779) of which were labeled as inaccurate health information, and 62% (483/779) of which were labeled as accurate health information. We suggest that this was highly efficacious as we did not include any tweets in which the physician annotators were unsure or in disagreement. Among the investigated deep learning models, the Transformer-based Model for Arabic Language Understanding version 0.2 (AraBERTv0.2)-large model was the most accurate, with an F₁ score of 87%, followed by AraBERT version 2–large and AraBERTv0.2-base.

Conclusions

Our results indicate that the pretrained language model AraBERTv0.2 is the best model for classifying tweets as carrying either inaccurate or accurate health information. Future studies should consider applying ensemble learning to combine the best models as it may produce better results.

New Insights Into the Social Rumor Characteristics During the COVID-19 Pandemic in China

Background

In the early stage of the COVID-19 outbreak in China, several social rumors in the form of false news, conspiracy theories, and magical cures had ever been shared and spread among the general public at an alarming rate, causing public panic and increasing the complexity and difficulty of social management. Therefore, this study aims to reveal the characteristics and the driving factors of the social rumors during the COVID-19 pandemic.

Methods

Based on a sample of 1,537 rumors collected from Sina Weibo's debunking account, this paper first divided the sample into four categories and calculated the risk level of all kinds of rumors. Then, time evolution analysis and correlation analysis were adopted to study the time evolution characteristics and the spatial and temporal correlation characteristics of the rumors, and the four stages of development were also divided according to the number of rumors. Besides, to extract the key driving factors from 15 rumor-driving factors, the social network analysis method was used to investigate the driver-driver 1-mode network characteristics, the generation driver-rumor 2-mode network characteristics, and the spreading driver-rumor 2-mode characteristics.

Results

Research findings showed that the number of rumors related to COVID-19 were gradually decreased as the outbreak was brought under control, which proved the importance of epidemic prevention and control to maintain social stability. Combining the number and risk perception levels of the four types of rumors, it could be concluded that the Creating Panic-type rumors were the most harmful to society. The results of rumor drivers indicated that panic psychology and the lag in releasing government information played an essential role in driving the generation and spread of rumors. The public's low scientific literacy and difficulty in discerning highly confusing rumors encouraged them to participate in spreading rumors.

Conclusion

The study revealed the mechanism of rumors. In addition, studies involving rumors on different emergencies and social platforms are warranted to enrich the findings.

Determining Factors Affecting the Users' Participation of Online Health Communities: An Integrated Framework of Social Capital and Social Support

As the national awareness of health keeps deepening, online health communities (OHCs) have achieved rapid development. Users' participation is critically important to the sustainable development of OHCs. Nevertheless, users usually lack the motive for participation. Based on the social capital theory, this research examines factors influencing users' participation in OHCs. The purpose of this research is to find out decisive factors that influence users' participation in OHCs, enrich the understanding of users' participation in OHCs, and help OHCs address the issue of sustainable development. The research model was empirically tested using 1277 responses from an online survey conducted in China. Data was analyzed using the structural equation modeling (SEM). We found informational support and emotional support to have significant direct effects over the structural capital, relational capital and cognitive capital of OHCs. Meanwhile, it is observed that relational capital and cognitive capital degree have a significant influence on knowledge acquisition and knowledge contribution of OHCs. For researchers this study provides a basis for further refinement of individual models of users' participation. For practitioners, understanding the social capital is crucial to users' knowledge acquisition and knowledge contribution that achieve high participation in OHCs.

Understanding How and by Whom COVID-19 Misinformation is Spread on Social Media: Coding and Network Analyses

Background

During global health crises such as the COVID-19 pandemic, rapid spread of misinformation on social media has occurred. The misinformation associated with COVID-19 has been analyzed, but little attention has been paid to developing a comprehensive analytical framework to study its spread on social media.

Objective

We propose an elaboration likelihood model–based theoretical model to understand the persuasion process of COVID-19–related misinformation on social media.

Methods

The proposed model incorporates the central route feature (content feature) and peripheral features (including creator authority, social proof, and emotion). The central-level COVID-19–related misinformation feature includes five topics: medical information, social issues and people’s livelihoods, government response, epidemic spread, and international issues. First, we created a data set of COVID-19 pandemic–related misinformation based on fact-checking sources and a data set of posts that contained this misinformation on real-world social media. Based on the collected posts, we analyzed the dissemination patterns.

Results

Our data set included 11,450 misinformation posts, with medical misinformation as the largest category (n=5359, 46.80%). Moreover, the results suggest that both the least (4660/11,301, 41.24%) and most (2320/11,301, 20.53%) active users are prone to sharing misinformation. Further, posts related to international topics that have the greatest chance of producing a profound and lasting impact on social media exhibited the highest distribution depth (maximum depth=14) and width (maximum width=2355). Additionally, 97.00% (2364/2437) of the spread was characterized by radiation dissemination.

Conclusions

Our proposed model and findings could help to combat the spread of misinformation by detecting suspicious users and identifying propagation characteristics.

Navigating credibility of online information during COVID-19: using mnemonics to empower the public to spot red flags in health information online

Misinformation creates challenges for the general public in differentiating truth from fiction in web-based content. During the COVID-19 pandemic, this issue has been amplified due to high volumes of news and changing information. Evidence on misinformation largely focuses on understanding the psychology of misinformation and debunking strategies but neglects to explore critical thinking education for the general public. This viewpoint outlines the science of misinformation and the current resources available to the public. This paper describes the development and theoretical underpinnings of a mnemonic (Conflict of Interest, References, Author, Buzzwords, Scope of Practice [CRABS]) for identifying misinformation in web-based health content. Leveraging evidence-based educational strategies may be a promising approach for empowering the public with the confidence needed to differentiate truth from fiction in an infodemic.

Factors influencing fake news rebuttal acceptance during the COVID-19 pandemic and the moderating effect of cognitive ability

Fake news is spreading rapidly on social media and poses a serious threat to the COVID-19 outbreak response. This study thus aims to reveal the factors influencing the acceptance of fake news rebuttals on Sina Weibo. Drawing on the elaboration likelihood model (ELM), we used text mining and the econometrics method to investigate the relationships among the central route (rebuttal's information readability and argument quality), peripheral route (rebuttal's source credibility, including authority and influence), and rebuttal acceptance, as well as the moderating effect of receiver's cognitive ability on these relationships. Our findings suggest that source authority had a negative effect on rebuttal acceptance, while source influence had a positive effect. Second, both information readability and argument quality had positive effects on rebuttal acceptance. In addition, individuals with low cognitive abilities relied more on source credibility and argument quality to accept rebuttals, while individuals with high cognitive abilities relied more on information readability. This study can provide decision support for practitioners to establish more effective fake news rebuttal strategies; it is especially valuable to reduce the negative impact of fake news related to major public health emergencies and safeguard the implementation of anti-epidemic strategies.

Twitter use by the dementia community during COVID-19: a user classification and social network analysis

Purpose

The study aimed to examine how different communities concerned with dementia engage and interact on Twitter.

Design/methodology/approach

A dataset was sampled from 8,400 user profile descriptions, which was labelled into five categories and subjected to multiple machine learning (ML) classification experiments based on text features to classify user categories. Social network analysis (SNA) was used to identify influential communities via graph-based metrics on user categories. The relationship between bot score and network metrics in these groups was also explored.

Findings

Classification accuracy values were achieved at 82% using support vector machine (SVM). The SNA revealed influential behaviour on both the category and node levels. About 2.19% suspected social bots contributed to the coronavirus disease 2019 (COVID-19) dementia discussions in different communities.

Originality/value

The study is a unique attempt to apply SNA to examine the most influential groups of Twitter users in the dementia community. The findings also highlight the capability of ML methods for efficient multi-category classification in a crisis, considering the fast-paced generation of data.

Peer review

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2021-0208.

The Impact of Health Information Privacy Concerns on Engagement and Payment Behaviors in Online Health Communities

Online health communities (OHCs) have enjoyed increasing popularity in recent years, especially in the context of the COVID-19 pandemic. However, several concerns have been raised regarding the privacy of users’ personal information in OHCs. Considering that OHCs are a type of data-sharing or data-driven platform, it is crucial to determine whether users’ health information privacy concerns influence their behaviors in OHCs. Thus, by conducting a survey, this study explores the impact of users’ health information privacy concerns on their engagement and payment behavior (Paid) in OHCs. The empirical results show that users’ concerns about health information privacy reduce their Paid in OHCs by negatively influencing their OHC engagement. Further analysis reveals that if users have higher benefit appraisals (i.e., perceived informational and emotional support from OHCs) and lower threat appraisals (i.e., perceived severity and vulnerability of information disclosure from OHCs), the negative effect of health information privacy concerns on users’ OHC engagement will decrease.

Health Misinformation Detection in the Social Web: An Overview and a Data Science Approach

The increasing availability of online content these days raises several questions about effective access to information. In particular, the possibility for almost everyone to generate content with no traditional intermediary, if on the one hand led to a process of “information democratization”, on the other hand, has negatively affected the genuineness of the information disseminated. This issue is particularly relevant when accessing health information, which impacts both the individual and societal level. Often, laypersons do not have sufficient health literacy when faced with the decision to rely or not rely on this information, and expert users cannot cope with such a large amount of content. For these reasons, there is a need to develop automated solutions that can assist both experts and non-experts in discerning between genuine and non-genuine health information. To make a contribution in this area, in this paper we proceed to the study and analysis of distinct groups of features and machine learning techniques that can be effective to assess misinformation in online health-related content, whether in the form of Web pages or social media content. To this aim, and for evaluation purposes, we consider several publicly available datasets that have only recently been generated for the assessment of health misinformation under different perspectives.

Do Informational and Emotional Elements Differ between Online Psychological and Physiological Disease Communities in China? A Comparative Study of Depression and Diabetes

Disease-specific online health communities provide a convenient and common platform for patients to share experiences, change information, provide and receive social support. This study aimed to compare differences between online psychological and physiological disease communities in topics, sentiment, participation, and emotional contagion patterns using multiple methods as well as to discuss how to satisfy the users' different informational and emotional needs. We chose the online depression and diabetes communities on the Baidu Tieba platform as the data source. Topic modeling and theme coding were employed to analyze discussion preferences for various topic categories. Sentiment analysis was used to identify the sentiment polarity of each post and comment. The social network was used to represent the users' interaction and emotional flows to discover the differences in participation and emotional contagion patterns between psychological and physiological disease communities. The results revealed that people affected by depression focused more on their symptoms and social relationships, while people affected by diabetes were more likely to discuss treatment and self-management behavior. In the depression community, there were obvious interveners spreading positive emotions and more core users in the negative emotional contagion network. In the diabetes community, emotional contagion was less prevalent and core users in positive and negative emotional contagion networks were basically the same. The study reveals insights into the differences between online psychological and physiological disease communities, providing a greater understanding of the users' informational and emotional needs expressed online. These results are helpful for society to provide actual medical assistance and deploy health interventions based on disease types.

Patients' Self-Disclosure Positively Influences the Establishment of Patients' Trust in Physicians: An Empirical Study of Computer-Mediated Communication in an Online Health Community

With the development of telemedicine and e-health, usage of online health communities has grown, with such communities now representing convenient sources of information for patients who have geographical and temporal constraints regarding visiting physical health-care institutions. Many previous studies have examined patient-provider communication and health-care service delivery in online health communities; however, there is a dearth of research exploring the relationship between patients' level of self-disclosure and the establishment of patients' trust in physicians. Consequently, this study aims to explore how patients' self-disclosure affects the establishment of patients' trust in physicians. "Good Doctor," which is a China-based online health community, was used as a data source, and a computer program was developed to download data for patient-physician communication on this community. Then, data for communications between 1,537 physicians and 63,141 patients were obtained. Ultimately, an empirical model was built to test our hypotheses. The results showed that patients' self-disclosure positively influences their establishment of trust in physicians. Further, physicians' provision of social support to patients showed a complete mediating effect on the relationship between patients' self-disclosure and patients' establishment of trust in physicians. Finally, evidence of "hope-for-help" motivation in patients' messages weakened the effect of patients' self-disclosure when physicians' social support was text-based, but strengthened it when physicians' social support was voice-based.

Impacts of physician promotion on the online healthcare community: Using a difference-in-difference approach

In this study, we use a difference-in-difference approach to explore how physician promotion, the advancement of a physician's offline reputation, affects patient behavior toward physicians in online healthcare communities; this allows us to explore how patients interpret the signals created by physician promotion. The study sample was collected from over 140,000 physician online profiles after 25 months of continuous observation, with 280 physicians who were promoted at month 13 as the treatment group and a control group obtained by propensity score matching. Our results show that a physician's promotion causes more patients to choose that physician, makes patients willing to give more psychological rewards, and makes them tend to give that physician a higher online rating. This implies that patient behavior is susceptible to the signal of physician promotion because the quality of the physician is unlikely to have changed significantly in the short term. These findings extend prior research on reputation in online communities and have crucial implications for theory and practice.

A rumor reversal model of online health information during the Covid-19 epidemic

The development of the Internet and social media has expanded the speed and scope of information dissemination, but not all widely disseminated information is true. Especially during the public health emergencies, the endogenous health information demand generated by the lack of scientific knowledge of health information among online users stimulates the dissemination of health information by mass media while providing opportunities for rumor mongers to publish and spread online rumors. Invalid scientific knowledge and rumors will have a serious negative impact and disrupt social order during epidemic outbreaks such as COVID-19. Therefore, it is extremely important to construct an effective online rumor reversal model. The purpose of this study is to build an online rumor reversal model to control the spread of online rumors and reduce their negative impact. From the perspective of internal and external factors, based on the SIR model, this study constructed a G-SCNDR online rumor reversal model by adopting scientific knowledge level theory and an external online rumor control strategy. In this study, the G-SCNDR model is simulated, and a sensitivity analysis of the important parameters of the model is performed. The reversal efficiency of the G-SCNDR model can be improved by properly adopting the isolation-conversion strategy as the external control approach to online rumors with improving the popularization rate of the level of users' scientific knowledge and accelerating the transformation efficiency of official nodes. This study can help provide a better understanding of the process of online rumor spreading and reversing, as well as offering ceritain guidance and countermeasures for online rumor control during public health emergencies.

Characterizing the dissemination of misinformation on social media in health emergencies: An empirical study based on COVID-19

The dissemination of misinformation in health emergencies poses serious threats to public health and increases health anxiety. To understand the underlying mechanism of the dissemination of misinformation regarding health emergencies, this study creatively draws on social support theory and text mining. It also explores the roles of different types of misinformation, including health advice and caution misinformation and health help-seeking misinformation, and emotional support in affecting individuals' misinformation dissemination behavior on social media and whether such relationships are contingent on misinformation ambiguity and richness. The theoretical model is tested using 12,101 textual data about COVID-19 collected from Sina Weibo, a leading social media platform in China. The empirical results show that health caution and advice, help seeking misinformation, and emotional support significantly increase the dissemination of misinformation. Furthermore, when the level of ambiguity and richness regarding misinformation is high, the effect of health caution and advice misinformation is strengthened, whereas the effect of health help-seeking misinformation and emotional support is weakened, indicating both dark and bright misinformation ambiguity and richness. This study contributes to the literature on misinformation dissemination behavior on social media during health emergencies and social support theory and provides implications for practice.

Feasibility of using an automated analysis of formulation effort in patients' spoken seizure descriptions in the differential diagnosis of epileptic and nonepileptic seizures

OBJECTIVE

There are three common causes of Transient Loss of Consciousness (TLOC), syncope, epileptic and psychogenic nonepileptic seizures (PNES). Many individuals who have experienced TLOC initially receive an incorrect diagnosis and inappropriate treatment. Whereas syncope can be distinguished relatively easily with a small number of "yes"/"no" questions, the differentiation of the other two causes of TLOC is more challenging. Previous qualitative research based on the methodology of Conversation Analysis has demonstrated that the descriptions of epileptic seizures contain more formulation effort than accounts of PNES. This research investigates whether features likely to reflect the level of formulation effort can be automatically elicited from audio recordings and transcripts of speech and used to differentiate between epileptic and nonepileptic seizures.

METHOD

Verbatim transcripts of conversations between patients and neurologists were manually produced from video and audio recordings of 45 interactions (21 epilepsy and 24 PNES). The subsection of each transcript containing the person's account of their first seizure was manually extracted for the analysis. Seven automatically detectable features were designed as markers of formulation effort. These features were used to train a Random Forest machine learning classifier.

RESULT

There were significantly more hesitations and repetitions in descriptions of epileptic than nonepileptic seizures. Using a nested leave-one-out cross validation approach, 71% of seizures were correctly classified by the Random Forest classifier.

DISCUSSION

This pilot study provides proof of principle that linguistic features that have been automatically extracted from audio recordings and transcripts could be used to distinguish between epileptic seizures and PNES and thereby contribute to the differential diagnosis of TLOC. Future research should explore whether additional observations can be incorporated into a diagnostic stratification tool and compare the performance of these features when they are combined with additional information provided by patients and witnesses about seizure manifestations and medical history.