CHQ- SocioEmo: Identifying Social and Emotional Support Needs in Consumer-Health Questions.

General public, often called consumers, are increasingly seeking health information online. To be satisfactory, answers to health-related questions often have to go beyond informational needs. Automated approaches to consumer health question answering should be able to recognize the need for social and emotional support. Recently, large scale datasets have addressed the issue of medical question answering and highlighted the challenges associated with question classification from the standpoint of informational needs. However, there is a lack of annotated datasets for the non-informational needs. We introduce a new dataset for non-informational support needs, called CHQ-SocioEmo. The Dataset of Consumer Health Questions was collected from a community question answering forum and annotated with basic emotions and social support needs. This is the first publicly available resource for understanding non-informational support needs in consumer health-related questions online. We benchmark the corpus against multiple state-of-the-art classification models to demonstrate the dataset's effectiveness.

Correction: Share to Seek: The Effects of Disease Complexity on Health Information-Seeking Behavior.

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

Share to Seek: The Effects of Disease Complexity on Health Information-Seeking Behavior.

BACKGROUND: Web-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. OBJECTIVE: This 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. METHODS: First, 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. RESULTS: The 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<.001) and the chronic stage (F1,622=54.01; P<.001). In addition, our results showed significant effects of disease complexity across all types of shared information: demographic information (F1,622=32.24; P<.001), medical diagnosis (F1,622=11.04; P<.001), and treatment and prevention (F1,622=14.55; P<.001). CONCLUSIONS: Our findings present implications for the design of web-based Q&A sites to better support health information seeking. Future studies should be conducted to validate the generality of these findings and apply them to improve the effectiveness of health information in Q&A sites.

How multimorbid health information consumers interact in an online community Q&A platform.

BACKGROUND: There is an increasing population of health information consumers (HIC) with multiple conditions (multimorbid). Previous studies explored the online behavior of HIC in general or HIC with a specific disease; however, the behavior of multimorbid HIC remains poorly researched. OBJECTIVES: This research aims to investigate the behaviors of the multimorbid HIC on community Q&A platforms. METHODS: Using kidney disease, a prevalent disease with high likelihood of multimorbidity as a case, we analyzed the online interaction behaviors of HIC with multimorbidity in Quora, a community Q&A platform, and compared them to those of single-disease HIC. RESULTS: The findings of this study reveal significant differences in the online interaction behavior between HIC of single vs. multimorbid diseases. Compared with single-disease HIC, multimorbid HIC are more active in multiple aspects, such as asking questions, following different topics or users, and providing suggestions for improvement of questions and answers. Additionally, multimorbid HIC are more likely to add topics to their questions, and their questions tend to attract more answers than those of single-disease HIC. On the other hand, questions and answers provided from single disease HIC had more views, followers, and upvotes than those from multimorbid HIC. CONCLUSION: The high level of activity among multimorbid HIC can be explained by their complex needs for information, driving an increased number of questions and drawing more attention from the whole community in answering them. Multimorbid HIC appear to be valuable contributors to the online community and reasons for the reduced visibility and upvoting of their answers should be investigated.