Search

BackgroundWeb-based question and answer (Q&A) sites have emerged as an alternative source for serving individuals’ health information needs. Although a number of studies have analyzed user-generated content in web-based Q&A sites, there is insufficient understanding of the effect of disease complexity on information-seeking needs and the types of information shared, and little research has been devoted to the questions concerning multimorbidity. ObjectiveThis study aims to investigate seeking of health information in Q&A sites at different levels of disease complexity. Specifically, this study investigates the effects of disease complexity on information-seeking needs, types of information shared, and stages of disease development. MethodsFirst, we selected a random sample of 400 questions separately from each of the Q&A sites: Yahoo Answers and WebMD Answers. The data cleaning resulted in a final set of 624 questions from the two sites. We used a mixed methods approach, including qualitative content analysis and quantitative statistical analysis. ResultsThe one-way results of ANOVA showed significant effects of disease complexity (single vs multimorbid disease questions) on two information-seeking needs: diagnosis (F1,622=5.08; P=.02) and treatment (F1,622=4.82; P=.02). There were also significant differences between the two levels of disease complexity in two stages of disease development: the general health stage (F1,622=48.02; P

Background: Health information consumers increasingly rely on question-and-answer (Q&A) communities to address their health concerns. However, the quality of questions posted significantly impacts the likelihood and relevance of received answers. Objective: This study aims to improve our understanding of the quality of health questions within web-based Q&A communities. Methods: We develop a novel framework for defining and measuring question quality within web-based health communities, incorporating content- and language-based variables. This framework leverages k-means clustering and establishes automated metrics to assess overall question quality. To validate our framework, we analyze questions related to kidney disease from expert-curated and community-based Q&A platforms. Expert evaluations confirm the validity of our quality construct, while regression analysis helps identify key variables. Results: High-quality questions were more likely to include demographic and medical information than lower-quality questions (P <.001). In contrast, asking questions at the various stages of disease development was less likely to reflect high-quality questions (P <.001). Low-quality questions were generally shorter with lengthier sentences than high-quality questions (P <.01). Conclusions: Our findings empower consumers to formulate more effective health information questions, ultimately leading to better engagement and more valuable insights within web-based Q&A communities. Furthermore, our findings provide valuable insights for platform developers and moderators seeking to enhance the quality of user interactions and foster a more trustworthy and informative environment for health information exchange. [ABSTRACT FROM AUTHOR]

Despite the increasing use of mobile web in our everyday lives, mobile web search remains a challenging task mainly due to the intensive scrolling inherent in linear presentation of search engine results (SERs) on the small screen of a mobile device. Drawing on the cognitive load theory and information foraging theory, this study aims to improve user performance in mobile web search by proposing two new artifacts--radial visualization of SERs clusters and synchronization of clusters and individual SERs presentations. These artifacts are hypothesized to improve search efficiency and/or navigation efficiency without affecting search effectiveness. This study also examines a possible moderating effect of search task type (open-ended vs. close-ended tasks). We implemented four mobile web search systems that reflect four combinations of different visualization and synchronization settings and empirically evaluated them using a controlled experiment. The results show that the radial visualization improves search efficiency and navigation efficiency, particularly for the open-ended tasks. In addition, synchronous presentation improves navigation efficiency in terms of path similarity. [ABSTRACT FROM AUTHOR]

[This corrects the article DOI: 10.2196/21642.]. [ABSTRACT FROM AUTHOR]

COVID-19 has had broad disruptive effects on economies, healthcare systems, governments, societies, and individuals. Uncertainty concerning the scale of this crisis has given rise to countless rumors, hoaxes, and misinformation. Much of this type of conversation and misinformation about the pandemic now occurs online and in particular on social media platforms like Twitter. This study analysis incorporated a data-driven approach to map the contours of misinformation and contextualize the COVID-19 pandemic with regards to socio-religious-political information. This work consists of a combined system bridging quantitative and qualitative methodologies to assess how information-exchanging behaviors can be used to minimize the effects of emergent misinformation. The study revealed that the social media platforms detected the most significant source of rumors in transmitting information rapidly in the community. It showed that WhatsApp users made up about 46% of the source of rumors in online platforms, while, through Twitter, it demonstrated a declining trend of rumors by 41%. Moreover, the results indicate the second-most common type of misinformation was provided by pharmaceutical companies; however, a prevalent type of misinformation spreading in the world during this pandemic has to do with the biological war. In this combined retrospective analysis of the study, social media with varying approaches in public discourse contributes to efficient public health responses. [ABSTRACT FROM AUTHOR]