“Privacy” in Semantic Networks on Chinese Social Media: The Case of Sina Weibo

Yuan, Elaine J.; Miao, Feng; Danowski, James A.

doi:10.1111/jcom.12058

Cited by 63 publications

(41 citation statements)

References 23 publications

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“…First, based on extracted unstructured data related to the main subjects (FDI and R&D), I construct a co-occurrence matrix of words x words using WORDij. WORDij (http://wordij.net) is a program used to analyze various types of unstructured data, as in computational linguistics, text, and content analysis, and network visualization [52]. Second, I use the CONCOR matrix (correlation or eigenvalue) to determine the similarities and patterns of relationships between the row vectors of each node [51,61].…”

Section: Results Of Snamentioning

confidence: 99%

“…Specifically, the SNA method treats the words collected through the main keyword as nodes in the network, and the connection relations and patterns between the words are regarded as semantic social relations [51]. By interpreting the structural features, I can explore the contexts in which a given keyword is discussed and understood in public and expert discourses [52]. In this regard, network theorists have argued that clusters or patterns derived from the frequency, co-occurrence, and centrality of words occurring in a network can explore the meanings represented in the text [53,54].…”

Section: 1mentioning

confidence: 99%

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The Nexus of FDI, R&D, and Human Capital on Chinese Sustainable Development: Evidence from a Two-Step Approach

Park

2018

Sustainability

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This study examines the effect of the foreign direct investment (FDI)-human capital and R&D-human capital interactions (FDIHC and RDHHC) on Chinese development between 1991 and 2015. Based on endogenous growth theory, the study focuses on FDI, R&D, and human capital as important factors for sustained economic growth; the interactions among factors are set as the main variables affecting economic growth (GDP). In particular, this study attempts a two-step empirical analysis. First, data mining and semantic network analysis (SNA) are performed using variables as keywords; reliability and realism are reflected as variables. Second, using the vector error correction model (VECM), the study analyzes short and long run mutual influences between variables. The results show that, in data mining and SNA with FDI and R&D as keywords, words related to human capital show high frequency, centrality, and clustering. This finding implies that FDIHC and RDHHC have robustness as variables and can be used as interaction variables. According to the VECM results, FDIHC and RDHHC have positive influences on GDP in the short and long run. The results of a variance decomposition test show that RDHHC has strong mid-to long-run impacts on GDP, FDIHC, and R&D itself.

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Section: Results Of Snamentioning

confidence: 99%

Section: 1mentioning

confidence: 99%

The Nexus of FDI, R&D, and Human Capital on Chinese Sustainable Development: Evidence from a Two-Step Approach

Park

2018

Sustainability

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“…Such discourse reaches a social consensus about proactively coping with societal issues and challenges [23][24][25][26]30] by gaining more prominent status in the national energy and industrial system and consistently expands the discourse related to renewable energy [93]. In the context of a discourse on the transition to a renewable-energy-based economy, we explore and interpret the structural features of the contexts, in which the keyword is discussed and understood in discourses [111]. As one method for analyzing 'big data,' we employ semantic network analysis, which identifies patterns in a discourse based on the meaning of the text that is communicated to various actors [112,113].…”

Section: Appendix Ementioning

confidence: 99%

Who Drives the Transition to a Renewable-Energy Economy? Multi-Actor Perspective on Social Innovation

Sung

Park

2018

Sustainability

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This study examines how various actors influence the transition to a renewable-energy economy. We employ a conceptual framework derived from a literature review and text-mining analysis and establish a panel data model for an empirical test using unbalanced panel data from 25 member countries of the Organization for Economic Co-operation and Development (OECD), for the period from 1990 to 2014. We establish a panel vector autoregressive (VAR) model in the first differences and use a bias-corrected least squares dummy variable (LSDVC) estimator to test complex dynamic relationships between government, the public, markets, the traditional energy sector (i.e., the sector that uses nuclear power, oil, coal and natural gas as sources for electricity) and the contribution of renewables to the total energy supply. We also perform Wald tests on the coefficients of variables estimated by LSDVC estimator to determine causal relationships between the variables. The results of this study reveal that government and markets directly promote the transition to renewable energy, whereas the traditional energy sector negatively and directly affects the transition. By contrast, the public does not directly influence the transition to a renewable-energy economy. This study also shows that the government and public have positive indirect effects on the transition, by interacting with the market. We also find convincing evidence of significant dynamic-path dependence in all estimations. Finally, we discuss some implications based on the findings of this study.

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“…Vertexes, or nodes, are concepts; edges can either be constructed by existing paradigmatic knowledge of linguistic relationships such as thesaurus (Kozima and Furugori, 1993;Fellbaum, 1998), or by the totality of structural relationships, such as cooccurrence (Danowski, 1993;Freeman and Barnett, 1994;Doerfel, 1998). By interpreting the structural features of the semantic network of a keyword, social science researchers are able to explore the contexts in which the keyword is discussed and understood in public discourses (Yuan et al, 2013).…”

Section: Word Cooccurrencementioning

confidence: 99%

Two Diverging Roads: A Semantic Network Analysis of Chinese Social Connection (“Guanxi”) on Twitter

Yan

Yasseri

2017

Front. Digit. Humanit.

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Guanxi, roughly translated as "social connection," is a term commonly used in the Chinese language. In this study, we employed a linguistic approach to explore popular discourses on guanxi. Although sharing the same Confucian roots, Chinese communities inside and outside Mainland China have undergone different historical trajectories. Hence, we took a comparative approach to examine guanxi in Mainland China and in Taiwan, Hong Kong, and Macau (TW-HK-M). Comparing guanxi discourses in two Chinese societies aim at revealing the divergence of guanxi culture. The data for this research were collected on Twitter over a three-week period by searching tweets containing guanxi written in simplified Chinese characters (关系) and in traditional Chinese characters (關係). After building, visualizing, and conducting community detection on both semantic networks, two guanxi discourses were then compared in terms of their major concept sub-communities. This study aims at addressing two questions: Has the meaning of guanxi transformed in contemporary Chinese societies? And how do different socio-economic configurations affect the practice of guanxi? Results suggest that guanxi in interpersonal relationships has adapted to a new family structure in both Chinese societies. In addition, the practice of guanxi in business varies in Mainland China and in TW-HK-M. Furthermore, an extended domain was identified where guanxi is used in a macro-level discussion of state relations. Network representations of the guanxi discourses enabled reification of the concept and shed lights on the understanding of social connections and social orders in contemporary China.

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“Privacy” in Semantic Networks on Chinese Social Media: The Case of Sina Weibo

Cited by 63 publications

References 23 publications

The Nexus of FDI, R&D, and Human Capital on Chinese Sustainable Development: Evidence from a Two-Step Approach

The Nexus of FDI, R&D, and Human Capital on Chinese Sustainable Development: Evidence from a Two-Step Approach

Who Drives the Transition to a Renewable-Energy Economy? Multi-Actor Perspective on Social Innovation

Two Diverging Roads: A Semantic Network Analysis of Chinese Social Connection (“Guanxi”) on Twitter

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