“Why Drones for Ordinary People?” Digital Representations, Topic Clusters, and Techno-Nationalization of Drones on Zhihu

Hamm, Andrea; Lin, Zihao

doi:10.3390/info10080256

Cited by 7 publications

(3 citation statements)

References 21 publications

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“…Users also can upvote for answers expressing their support for the answer. The data from Zhihu is open to the general public, and can be used in opinion mining [35,36].…”

Section: Stage 1: Data Collectionmentioning

confidence: 99%

Investigating Fertility Intentions for a Second Child in Contemporary China Based on User-Generated Content

Qian

Fang

et al. 2020

IJERPH

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China’s two-child policy, aimed at boosting the country’s total fertility rate, has failed to achieve the desired outcomes. Previous studies on low fertility rates mainly used data obtained from demographic censuses, questionnaires, or interviews. These data-gathering methods are costly, entailing time delays and yielding limited information. User-generated content (UGC) provides an alternative data source. We propose a machine–human hybrid approach using UGC obtained from social media to assess users’ intentions to have a second child. Our results showed that couples associate a second child with high economic costs mainly through negative impacts on the mothers’ careers, with no concomitant economic benefits. A key motivation for having two children relates to the mental benefit of the joy in having children. However, raising a second child also entails considerable mental costs such as exhaustion and pressure. Couples largely seek help within their extended families, that is, their parents are major sources of child-rearing support. Therefore, the government should devise ways of reducing the negative impacts of having a second child on a woman’s career and provide child-rearing support to help increase the fertility rate. Our proposed approach can also be used to elicit the reasons for low fertility rates in other countries.

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“…Users also can upvote for answers expressing their support for the answer. The data from Zhihu is open to the general public, and can be used in opinion mining [35,36].…”

Section: Stage 1: Data Collectionmentioning

confidence: 99%

Investigating Fertility Intentions for a Second Child in Contemporary China Based on User-Generated Content

Qian

Fang

et al. 2020

IJERPH

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“…Among different online platforms for discussing and spreading science information and misinformation, Zhihu is the one with a unique social media structure where group identity plays a critical role in technology discussion (Hamm & Lin, 2019; Peng et al, 2009). Similar to its US counterpart Quora, Zhihu allows users to contribute knowledge to online communities by asking and answering questions, commenting and voting for each other’s answers (Jin et al, 2015; Wang & Shah, 2016).…”

Section: Methodsmentioning

confidence: 99%

Science Factionalism: How Group Identity Language Affects Public Engagement With Misinformation and Debunking Narratives on a Popular Q&A Platform in China

Chen

Jin

Shao

2022

Social Media + Society

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Misinformation and intergroup bias are two pathologies challenging informed citizenship. This article examines how identity language is used in misinformation and debunking messages about controversial science on the Chinese digital public spheres and their impact on how the public engage with science. We collected an 8-year time series dataset of public discussion ( N = 6,039) on one of the most controversial science issues in China (GMO) from a popular Q&A platform, Zhihu. We found that both misinformation and debunking messages use a substantial amount of group identity languages when discussing the controversial science issue, which we define as science factionalism—discussion about science is divided by factions that are formed upon science attitudes. We found that posts that use science factionalism receive more digital votes and comments, even among the science-savvy community in China. Science factionalism also increases the use of negativity in public discourse. We discussed the implications of how science factionalism interacts with the digital attention economy to affect public engagement with science misinformation.

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“…Users of Zhihu can openly and freely express their opinions on various topics. In addition, Zhihu’s data are available to the public and can be used for opinion mining [ 40 , 41 ].…”

Section: Methodsmentioning

confidence: 99%

Investigating Young Employee Stressors in Contemporary Society Based on User-Generated Contents

Wang

Hou

et al. 2021

IJERPH

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Understanding stressors is an effective measure to decrease employee stress and improve employee mental health. The extant literature mainly focuses on a singular stressor among various aspects of their work or life. In addition, the extant literature generally uses questionnaires or interviews to obtain data. Data obtained in such ways are often subjective and lack authenticity. We propose a novel machine–human hybrid approach to conduct qualitative content analysis of user-generated online content to explore the stressors of young employees in contemporary society. The user-generated online contents were collected from a famous Q&A platform in China and we adopted natural language processing and deep learning technology to discover knowledge. Our results identified three kinds of new stressors, that is, affection from leaders, affection from the social circle, and the gap between dream and reality. These new identified stressors were due to the lack of social security and regulation, frequent occurrences of social media fearmongering, and subjective cognitive bias, respectively. In light of our findings, we offer valuable practical insights and policy recommendations to relieve stress and improve mental health of young employees. The primary contributions of our work are two-fold, as follows. First, we propose a novel approach to explore the stressors of young employees in contemporary society, which is applicable not only in China, but also in other countries and regions. Second, we expand the scope of job demands-resources (JD-R) theory, which is an important framework for the classification of employee stressors.

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“Why Drones for Ordinary People?” Digital Representations, Topic Clusters, and Techno-Nationalization of Drones on Zhihu

Cited by 7 publications

References 21 publications

Investigating Fertility Intentions for a Second Child in Contemporary China Based on User-Generated Content

Investigating Fertility Intentions for a Second Child in Contemporary China Based on User-Generated Content

Science Factionalism: How Group Identity Language Affects Public Engagement With Misinformation and Debunking Narratives on a Popular Q&A Platform in China

Investigating Young Employee Stressors in Contemporary Society Based on User-Generated Contents

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