Social Q&amp;A

Oh, Sanghee

doi:10.1007/978-3-319-90092-6_3

Cited by 15 publications

(12 citation statements)

References 82 publications

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“…Content analysis is widely used as a qualitative research method for analyzing questions and answers from web-based Q&A websites [ 3 ]. The content of each post is coded into a set of categories, and analysis is performed based on these categories and their frequencies.…”

Section: Methodsmentioning

confidence: 99%

“…The motives of users participating in Q&A sites, especially in the long term, have been explained based on social theories [ 3 ]. According to the social exchange theory, individuals exchange valuable resources through contacts to receive something that benefits both parties [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

“…This reciprocity encourages social interactions in a co-operative fashion between individuals, even when the rewards are not tangible. Individuals may also share information with someone believing that a third party could offer similar help in the future when needed, and this is termed generalized reciprocity [ 3 ]. Beyond finding solutions to their problems, those who seek information possess other benefits such as meta-knowledge (becoming aware of further resources such as people, databases, or documents), problem reformulation, acknowledgment and legitimation from respected people, and validation of their plans or solutions [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

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

Alasmari¹,

Zhou²

2021

J Med Internet Res

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Alasmari¹,

Zhou²

2021

J Med Internet Res

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“…ASSIST shows how these technologies can collaborate by using community browsing data to improve search, but also by using social data collected during search to improve navigation. We also consider the Aardvark [99] system, which crosses the boundary between traditional social searching and social Q&A technology, reviewed elsewhere in Chapter 3 of this book [154].…”

Section: Expanding the Borders Of Social Searchmentioning

confidence: 99%

Social Search

Brusilovsky

Smyth

Shapira

2018

Social Information Access

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Today, most people find what they are looking for online by using search engines such as Google, Bing, or Baidu. Modern web search engines have evolved from their roots in information retrieval to developing new ways to cope with the unique nature of web search. In this chapter, we review recent research that aims to make search a more social activity by combining readily available social signals with various strategies for using these signals to influence or adapt more conventional search results. The chapter begins by framing the social search landscape in terms of the sources of data available and the ways in which this can be leveraged before, during, and after search. This includes a number of detailed case studies that serve to mark important milestones in the evolution of social search research and practice.

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“…The wealth of online information found on social Q&A websites has been the center of much research. Research on social Q&A websites can be classified into user-centered studies (questioners, answers, and the community in general) (Wu & He, 2014) and content-centered studies (questions and answers) (Oh, 2018;Kim & Oh, 2009). The major perspectives of social Q&A website research include the motivation for participation (Jin, Li, Zhong, & Zhai, 2015;Zhang et al, 2014), user satisfaction (Zhang, Deng, & Yang, 2014), user behavior (Park, Reber, & Chon, 2016;Shoji, Fujita, Tajima, & Tanaka, 2015;So et al, 2016), and information quality and effectiveness (Chua & Banerjee, 2015;Li, He, Jeng, Goodwin, & Zhang, 2015;Robillard, Johnson, Hennessey, Beattie, & Illes, 2013).…”

Section: Introductionmentioning

confidence: 99%

Public health and social media: A study of Zika virus‐related posts on Yahoo! Answers

Jin

Chen

Zhao

et al. 2019

Asso for Info Science & Tech

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This study investigates the content of questions and responses about the Zika virus on Yahoo! Answers as a recent example of how public concerns regarding an international health issue are reflected in social media. We investigate the contents of posts about the Zika virus on Yahoo! Answers, identify and reveal subject patterns about the Zika virus, and analyze the temporal changes of the revealed subject topics over 4 defined periods of the Zika virus outbreak. Multidimensional scaling analysis, temporal analysis, and inferential statistical analysis approaches were used in the study. A resulting 2-layer Zika virus schema, and term connections and relationships are presented. The results indicate that consumers' concerns changed over the 4 defined periods. Consumers paid more attention to the basic information about the Zika virus, and the prevention and protection from the Zika virus at the beginning of the outbreak of the Zika virus. During the later periods, consumers became more interested in the role that the government and health organizations played in the public health emergency.

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Social Q&A

Cited by 15 publications

References 82 publications

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

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

Social Search

Public health and social media: A study of Zika virus‐related posts on Yahoo! Answers

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