Is It Good to Be Like Wikipedia?

Li, Guo; Zhu, Haiyi; Lu, Tun; Ding, Xianghua; Gu, Ning

doi:10.1145/2675133.2675155

Cited by 33 publications

(12 citation statements)

References 30 publications

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“…Fourth, we examined the question–answer interaction and asker acceptance rates to identify any potential information gaps that were not addressed by the Q&A community. These criteria were used in previous research to evaluate how questions were addressed on social Q&A websites (Burghardt et al, 2017; Jin et al, 2017; Li et al, 2015; Sin et al, 2016; Teevan et al, 2011; Tian et al, 2013). The question–answer interaction rate was defined as the ratio of the number of answers on each topic (dependent variable) to the popularity of questions, represented by the question view count on each topic (independent variable) (Borghol et al, 2012; Sin et al, 2016; Szabo & Huberman, 2010).…”

Section: Methodsmentioning

confidence: 99%

Topics of questions and community interaction in social Q&A during the COVID‐19 pandemic

2023

Health Info Libraries J

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BackgroundConsidering social distancing requirements and isolation during the COVID‐19 pandemic and the widespread health mis/disinformation on social media, filling the pandemic‐related information gap is critical.ObjectivesThis study explored COVID‐19‐related question topics, topic correlation, evolving trends, and community interactions by leveraging data from a health question‐answering (Q&A) community (public and health professionals) over a 2‐year period.MethodsA total of 828 questions were collected and manually reviewed from a health Q&A and classified into 2492 codes. Question topic correlations, trends, and community interactions were generated using different statistical techniques.ResultsThirty‐seven topics were identified, and strong topic correlations were observed between the cognitive layer and other layers. These topics changed as the pandemic developed. Community interaction analyses revealed topics that were popular but received comparatively fewer responses.DiscussionThe findings could illuminate the dynamic changes in people's interests and concerns related to COVID‐19 and their experiences of exchanging information in social Q&A communities.ConclusionThis study examined the practical significance of understanding topics of interest during a pandemic. The findings can inform the design of social health Q&A platforms that could reduce the risks of misinformation and disinformation.

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Section: Methodsmentioning

confidence: 99%

Topics of questions and community interaction in social Q&A during the COVID‐19 pandemic

2023

Health Info Libraries J

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“…Negative binomial regression (nbreg) models are used to model count outcome variables with great variability ( Cameron & Trivedi, 2013 ) and have been applied in different social computing tasks (Q. Chen et al, 2020 ; G. Li et al, 2015 ). Considering the over-dispersed distribution of all engagement dimensions as shown in Table 2 , we adopted nbreg model to estimate the impact of content topic, emotion valence, video category and government-citizen collaboration on youth engagement.…”

Section: Methodsmentioning

confidence: 99%

“…Collaboration in content generation, as a common practice in social media websites, has been proved to be beneficial in enhancing content quality and engaging social media users. For instance, Li et al found that collaborative editing in Stack Overflow increased the number of positive votes by 181% for the questions and 119% for the answers (G. Li, Zhu, Lu, Ding, & Gu, 2015 ). During times of crisis, government-citizen collaboration could further fill the information gap between government and citizens, and thus serve as a significant approach to facilitate information sharing ( Pipek, Liu, & Kerne, 2014 ).…”

Section: Theoretical Foundations and Hypothesismentioning

confidence: 99%

More collaboration, less seriousness: Investigating new strategies for promoting youth engagement in government-generated videos during the COVID-19 pandemic in China

Liu

et al. 2022

Computers in Human Behavior

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Effectively engaging citizens during crises is critical for governments to disseminate timely information and help the public to adjust to the constantly changing conditions. In particular, promoting youth engagement not only enhances crisis awareness and resilience among the young generation, but also has a positive impact on youths' social participation and responsibility. With the increasing popularity of online video services, leveraging online videos to disseminate authoritative information has become a method widely adopted by government. To enhance youth awareness and engagement, two new video-based crisis communication strategies have been utilized on a popular youth-targeted video platform Bilibili in China: creating recreational videos such as animation and music videos, and collaborating with individual video-uploaders in video making. However, their impacts and results are largely unknown, which motivates our study. Guided by Entertainment Education (EE) and Collaborative Governance (CG), we report, to our best knowledge, the first systematic study on how recreational video category and government-citizen collaboration would influence youth engagement focusing on 3347 COVID-19-related government-generated videos on Bilibili. This study reveals that recreational videos successfully promote youth engagement including interaction, feedback and sharing. Collaboration with individual uploaders in video making also has a substantially positive impact on youth engagement. Through an in-depth qualitative content analysis of user-generated commentaries, we further unpacked the unique values (e.g., trust work for youth participation) as well as latent limitations (e.g., imbalanced topic distribution) of the two new strategies. We discuss how the findings enrich EE and CG theoretically, and provide practical implications to effective and engaging communication strategies during crises.

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“…The finding also showed that malpractices such as plagiarism were also common on Stack Overflow. Li, Zhu, Lu, Ding, and Gu (2015) examined the data of editing behaviour extracted from Stack Overflow. Their analysis indicates that the benefits of collaborative editing outweigh its negative effect.…”

Section: Related Workmentioning

confidence: 99%

Domain problem‐solving expert identification in community question answering

Tang

et al. 2020

Expert Systems

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Question-Answering (Q&A) services provide internet users with platforms to exchange knowledge and ideas. The development of Q&A sites, or Community Question Answering (CQA), mainly depends on the high-quality content continuously contributed by users with high-level expertise, who can be recognized as experts. Expert finding is an important task for the authorities of Q&A communities to encourage commitment. In a highly competitive market environment, CQA managers have to take measures to retain and nurture users, especially superior contributors. However, current expertise scoring techniques adopted in CQA often give much credit to very active users and fail to identify real experts. This study aims to develop a robust and practical expert identification framework for Q&A communities, by combining welldesigned expertise scoring technique and probabilistic clustering model. With regard to expert identification, a numerical metric of users' expertise is developed as the optimal expert finding strategy, and a clustering algorithm based on Gaussian-Gamma mixture model (GGMM) is proposed to efficiently distinguish experts from nonexperts. In the experiments, the proposed method is applied to real-world datasets collected from subcommunities of Stack Exchange Q&A networks. Results obtained from comparative experiments show that our method achieves better performance than the state-of-the-art methods and demonstrate the effectiveness of the proposed framework. The analysis shows that the framework which combines the proposed expertise scoring technique and Gaussian-Gamma mixture clustering model is capable of detecting excellent domain problem-solving experts who exhibit both domain interest and expertise.

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Is It Good to Be Like Wikipedia?

Cited by 33 publications

References 30 publications

Topics of questions and community interaction in social Q&A during the COVID‐19 pandemic

Topics of questions and community interaction in social Q&A during the COVID‐19 pandemic

More collaboration, less seriousness: Investigating new strategies for promoting youth engagement in government-generated videos during the COVID-19 pandemic in China

Domain problem‐solving expert identification in community question answering

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