How users make judgements about the quality of online health information: a cross-sectional survey study.

BACKGROUND: People increasingly use the Internet to seek health information. However, the overall quality of online health information remains low. This situation is exacerbated by the unprecedented "infodemic", which has had negative consequences for patients. Therefore, it is important to understand how users make judgements about health information by applying different judgement criteria. OBJECTIVE: The objective of this study is to determine how patients apply different criteria in their judgement of the quality of online health information during the pandemic. In particular, we investigate whether there is consistency between the likelihood of using a particular judgement criterion and its perceived importance among different groups of users. METHODS: A cross-sectional survey was conducted in one of the leading hospitals in a coastal province of China with a population of forty million. Combined-strategy sampling was used to balance the randomness and the practicality of the recruiting process. A total of 1063 patients were recruited for this study. Chi-square and Kruskal-Wallis analyses were used to analyse the survey data. RESULTS: In general, patients make quality judgement of health information more frequently based on whether it is familiar, aesthetic, and with expertise. In comparison, they put more weights on whether health information is secure, trustworthy, and with expertise when determining its quality. Criteria that were considered more important were not always those with a higher likelihood of being used. Patients may not use particular criteria, such as familiarity, identification, and readability, more frequently than others even if they consider them to be more important than other do and vice versa. Surprisingly, patients with a primary school degree put more weight on whether health information is comprehensive than those with higher degrees do in determining its quality. However, they are less likely to use this guideline in practice. CONCLUSIONS: To the best of our knowledge, this is the first study to investigate the consistency between the likelihood of using certain quality judgement criteria and their perceived importance among patients grouped by different demographic variables and eHealth literacy levels. The findings highlight how to improve online health information services and provide fine-grained customization of information for users.

Chatbot as an emergency exist: Mediated empathy for resilience via human-AI interaction during the COVID-19 pandemic.

As a global health crisis, the COVID-19 pandemic has also made heavy mental and emotional tolls become shared experiences of global communities, especially among females who were affected more by the pandemic than males for anxiety and depression. By connecting multiple facets of empathy as key mechanisms of information processing with the communication theory of resilience, the present study examines human-AI interactions during the COVID-19 pandemic in order to understand digitally mediated empathy and how the intertwining of empathic and communicative processes of resilience works as coping strategies for COVID-19 disruption. Mixed methods were adopted to explore the using experiences and effects of Replika, a chatbot companion powered by AI, with ethnographic research, in-depth interviews, and grounded theory-based analysis. Findings of this research extend empathy theories from interpersonal communication to human-AI interactions and show five types of digitally mediated empathy among Chinese female Replika users with varying degrees of cognitive empathy, affective empathy, and empathic response involved in the information processing processes, i.e., companion buddy, responsive diary, emotion-handling program, electronic pet, and tool for venting. When processing information obtained from AI and collaborative interactions with the AI chatbot, multiple facets of mediated empathy become unexpected pathways to resilience and enhance users' well-being. This study fills the research gap by exploring empathy and resilience processes in human-AI interactions. Practical implications, especially for increasing individuals' psychological resilience as an important component of global recovery from the pandemic, suggestions for future chatbot design, and future research directions are also discussed.

The causes, impacts and countermeasures of COVID-19 "Infodemic": A systematic review using narrative synthesis.

An unprecedented infodemic has been witnessed to create massive damage to human society. However, it was not thoroughly investigated. This systematic review aims to (1) synthesize the existing literature on the causes and impacts of COVID-19 infodemic; (2) summarize the proposed strategies to fight with COVID-19 infodemic; and (3) identify the directions for future research. A systematic literature search following the PRISMA guideline covering 12 scholarly databases was conducted to retrieve various types of peer-reviewed articles that reported causes, impacts, or countermeasures of the infodemic. Empirical studies were assessed for risk of bias using the Mixed-Methods Appraisal Tool. A coding theme was iteratively developed to categorize the causes, impacts, and countermeasures found from the included studies. Social media usage, low level of health/eHealth literacy, and fast publication process and preprint service are identified as the major causes of the infodemic. Besides, the vicious circle of human rumor-spreading behavior and the psychological issues from the public (e.g., anxiety, distress, fear) emerges as the characteristic of the infodemic. Comprehensive lists of countermeasures are summarized from different perspectives, among which risk communication and consumer health information need/seeking are of particular importance. Theoretical and practical implications are discussed and future research directions are suggested.

Automatic Classification of Users' Health Information Need Context: Logistic Regression Analysis of Mouse-Click and Eye-Tracker Data.

BACKGROUND: Users searching for health information on the Internet may be searching for their own health issue, searching for someone else's health issue, or browsing with no particular health issue in mind. Previous research has found that these three categories of users focus on different types of health information. However, most health information websites provide static content for all users. If the three types of user health information need contexts can be identified by the Web application, the search results or information offered to the user can be customized to increase its relevance or usefulness to the user. OBJECTIVE: The aim of this study was to investigate the possibility of identifying the three user health information contexts (searching for self, searching for others, or browsing with no particular health issue in mind) using just hyperlink clicking behavior; using eye-tracking information; and using a combination of eye-tracking, demographic, and urgency information. Predictive models are developed using multinomial logistic regression. METHODS: A total of 74 participants (39 females and 35 males) who were mainly staff and students of a university were asked to browse a health discussion forum, Healthboards.com. An eye tracker recorded their examining (eye fixation) and skimming (quick eye movement) behaviors on 2 types of screens: summary result screen displaying a list of post headers, and detailed post screen. The following three types of predictive models were developed using logistic regression analysis: model 1 used only the time spent in scanning the summary result screen and reading the detailed post screen, which can be determined from the user's mouse clicks; model 2 used the examining and skimming durations on each screen, recorded by an eye tracker; and model 3 added user demographic and urgency information to model 2. RESULTS: An analysis of variance (ANOVA) analysis found that users' browsing durations were significantly different for the three health information contexts (P<.001). The logistic regression model 3 was able to predict the user's type of health information context with a 10-fold cross validation mean accuracy of 84% (62/74), followed by model 2 at 73% (54/74) and model 1 at 71% (52/78). In addition, correlation analysis found that particular browsing durations were highly correlated with users' age, education level, and the urgency of their information need. CONCLUSIONS: A user's type of health information need context (ie, searching for self, for others, or with no health issue in mind) can be identified with reasonable accuracy using just user mouse clicks that can easily be detected by Web applications. Higher accuracy can be obtained using Google glass or future computing devices with eye tracking function.

The Criteria People Use in Relevance Decisions on Health Information: An Analysis of User Eye Movements When Browsing a Health Discussion Forum.

BACKGROUND: People are increasingly accessing health-related social media sites, such as health discussion forums, to post and read user-generated health information. It is important to know what criteria people use when deciding the relevance of information found on health social media websites, in different situations. OBJECTIVE: The study attempted to identify the relevance criteria that people use when browsing a health discussion forum, in 3 types of use contexts: when seeking information for their own health issue, when seeking for other people's health issue, and when browsing without a particular health issue in mind. METHODS: A total of 58 study participants were self-assigned to 1 of the 3 use contexts or information needs and were asked to browse a health discussion forum, HealthBoards.com. In the analysis, browsing a discussion forum was divided into 2 stages: scanning a set of post surrogates (mainly post titles) in the summary result screen and reading a detailed post content (including comments by other users). An eye tracker system was used to capture participants' eye movement behavior and the text they skim over and focus (ie, fixate) on during browsing. By analyzing the text that people's eyes fixated on, the types of health information used in the relevance judgment were determined. Post-experiment interviews elicited participants' comments on the relevance of the information and criteria used. RESULTS: It was found that participants seeking health information for their own health issue focused significantly more on the poster's symptoms, personal history of the disease, and description of the disease (P=.01, .001, and .02). Participants seeking for other people's health issue focused significantly more on cause of disease, disease terminology, and description of treatments and procedures (P=.01, .01, and .02). In contrast, participants browsing with no particular issue in mind focused significantly more on general health topics, hot topics, and rare health issues (P=.01, .01, and .01). CONCLUSION: Users browsing for their own health issues used mainly case-based relevance criteria to relate the poster's health situation to their own. Participants seeking for others' issues used mostly general knowledge-based criteria, whereas users with no particular issue in mind used general interest- and curiosity-based criteria.

Characterizing the COVID-19 Infodemic on Chinese Social Media: Exploratory Study.

BACKGROUND: The COVID-19 infodemic has been disseminating rapidly on social media and posing a significant threat to people's health and governance systems. OBJECTIVE: This study aimed to investigate and analyze posts related to COVID-19 misinformation on major Chinese social media platforms in order to characterize the COVID-19 infodemic. METHODS: We collected posts related to COVID-19 misinformation published on major Chinese social media platforms from January 20 to May 28, 2020, by using PythonToolkit. We used content analysis to identify the quantity and source of prevalent posts and topic modeling to cluster themes related to the COVID-19 infodemic. Furthermore, we explored the quantity, sources, and theme characteristics of the COVID-19 infodemic over time. RESULTS: The daily number of social media posts related to the COVID-19 infodemic was positively correlated with the daily number of newly confirmed (r=0.672, P<.01) and newly suspected (r=0.497, P<.01) COVID-19 cases. The COVID-19 infodemic showed a characteristic of gradual progress, which can be divided into 5 stages: incubation, outbreak, stalemate, control, and recovery. The sources of the COVID-19 infodemic can be divided into 5 types: chat platforms (1100/2745, 40.07%), video-sharing platforms (642/2745, 23.39%), news-sharing platforms (607/2745, 22.11%), health care platforms (239/2745, 8.71%), and Q&A platforms (157/2745, 5.72%), which slightly differed at each stage. The themes related to the COVID-19 infodemic were clustered into 8 categories: "conspiracy theories" (648/2745, 23.61%), "government response" (544/2745, 19.82%), "prevention action" (411/2745, 14.97%), "new cases" (365/2745, 13.30%), "transmission routes" (244/2745, 8.89%), "origin and nomenclature" (228/2745, 8.30%), "vaccines and medicines" (154/2745, 5.61%), and "symptoms and detection" (151/2745, 5.50%), which were prominently diverse at different stages. Additionally, the COVID-19 infodemic showed the characteristic of repeated fluctuations. CONCLUSIONS: Our study found that the COVID-19 infodemic on Chinese social media was characterized by gradual progress, videoization, and repeated fluctuations. Furthermore, our findings suggest that the COVID-19 infodemic is paralleled to the propagation of the COVID-19 epidemic. We have tracked the COVID-19 infodemic across Chinese social media, providing critical new insights into the characteristics of the infodemic and pointing out opportunities for preventing and controlling the COVID-19 infodemic.