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Chia-Lee Yang
National Center for High-Performance Computing, Hsinchu 300, Taiwan

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Journal article
Published: 25 August 2021 in Mathematics
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The huge volume of user-generated data on social media is the result of the aggregation of users’ personal backgrounds, past experiences, and daily activities. This huge size of the generated data, the so-called “big data,” has been studied and investigated intensively during the past few years. In spite of the impression one may get from the media, a great deal of data processing has not been uncovered by existing techniques of data engineering and processing. However, very few scholars have tried to do so, especially from the perspective of multiple-criteria decision-making (MCDM). These MCDM methods can derive influence relationships and weights associated with aspects and criteria, which can hardly be achieved by traditional data analytics and statistical approaches. Therefore, in this paper, we aim to propose an analytic framework to mine social networks, feed the meaningful information via MCDM methods based on a theoretical framework, derive causal relationships among the aspects of the theoretical framework, and finally compare the causal relationships with a social theory. Latent Dirichlet allocation (LDA) will be adopted to derive topic models based on the data retrieved from social media. By clustering the topics into aspects of the social theory, the probability associated with each aspect will be normalized and then transformed to a Likert-type 5-point scale. Afterwards, for every topic, the feature importance of all other topics will be derived using the random forest (RF) algorithm. The feature importance matrix will be transformed to the initial influence matrix of the decision-making trial and evaluation laboratory (DEMATEL). The influence relationships among the aspects and criteria and influence weights can then be derived by using the DEMATEL-based analytic network process (DANP). The influence weight versus each criterion can be derived by using DANP. To verify the feasibility of the proposed framework, Taiwanese users’ attitudes toward air pollution will be analyzed based on the value–belief–norm (VBN) theory by using social media data retrieved from Dcard (dcard.tw). Based on the analytic results, the causal relationships are fully consistent with the VBN framework. Further, the mutual influences derived in this work that were seldom discussed by earlier works, i.e., the mutual influences between altruistic concerns and egoistic concerns, as well as those between altruistic concerns and biosphere concerns, are worth further investigation in future.

ACS Style

Chi-Yo Huang; Chia-Lee Yang; Yi-Hao Hsiao. A Novel Framework for Mining Social Media Data Based on Text Mining, Topic Modeling, Random Forest, and DANP Methods. Mathematics 2021, 9, 2041 .

AMA Style

Chi-Yo Huang, Chia-Lee Yang, Yi-Hao Hsiao. A Novel Framework for Mining Social Media Data Based on Text Mining, Topic Modeling, Random Forest, and DANP Methods. Mathematics. 2021; 9 (17):2041.

Chicago/Turabian Style

Chi-Yo Huang; Chia-Lee Yang; Yi-Hao Hsiao. 2021. "A Novel Framework for Mining Social Media Data Based on Text Mining, Topic Modeling, Random Forest, and DANP Methods." Mathematics 9, no. 17: 2041.

Journal article
Published: 15 May 2021 in International Journal of Environmental Research and Public Health
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With growing scientific evidence showing the harmful impact of air pollution on the environment and individuals’ health in modern societies, public concern about air pollution has become a central focus of the development of air pollution prevention policy. Past research has shown that social media is a useful tool for collecting data about public opinion and conducting analysis of air pollution. In contrast to statistical sampling based on survey approaches, data retrieved from social media can provide direct information about behavior and capture long-term data being generated by the public. However, there is a lack of studies on how to mine social media to gain valuable insights into the public’s pro-environmental behavior. Therefore, research is needed to integrate information retrieved from social media sites into an established theoretical framework on environmental behaviors. Thus, the aim of this paper is to construct a theoretical model by integrating social media mining into a value-belief-norm model of public concerns about air pollution. We propose a hybrid method that integrates text mining, topic modeling, hierarchical cluster analysis, and partial least squares structural equation modelling (PLS-SEM). We retrieved data regarding public concerns about air pollution from social media sites. We classified the topics using hierarchical cluster analysis and interpreted the results in terms of the value-belief-norm theoretical framework, which encompasses egoistic concerns, altruistic concerns, biospheric concerns, and adaptation strategies regarding air pollution. Then, we used PLS-SEM to confirm the causal relationships and the effects of mediation. An empirical study based on the concerns of Taiwanese social media users about air pollution was used to demonstrate the feasibility of the proposed framework in general and to examine gender differences in particular. Based on the results of the empirical studies, we confirmed the robust effects of egoistic, altruistic, and biospheric concerns of public impact on adaptation strategies. Additionally, we found that gender differences can moderate the causal relationship between egoistic concerns, altruistic concerns, and adaptation strategies. These results demonstrate the effectiveness of enhancing perceptions of air pollution and environmental sustainability by the public. The results of the analysis can serve as a basis for environmental policy and environmental education strategies.

ACS Style

Chia-Lee Yang; Chi-Yo Huang; Yi-Hao Hsiao. Using Social Media Mining and PLS-SEM to Examine the Causal Relationship between Public Environmental Concerns and Adaptation Strategies. International Journal of Environmental Research and Public Health 2021, 18, 5270 .

AMA Style

Chia-Lee Yang, Chi-Yo Huang, Yi-Hao Hsiao. Using Social Media Mining and PLS-SEM to Examine the Causal Relationship between Public Environmental Concerns and Adaptation Strategies. International Journal of Environmental Research and Public Health. 2021; 18 (10):5270.

Chicago/Turabian Style

Chia-Lee Yang; Chi-Yo Huang; Yi-Hao Hsiao. 2021. "Using Social Media Mining and PLS-SEM to Examine the Causal Relationship between Public Environmental Concerns and Adaptation Strategies." International Journal of Environmental Research and Public Health 18, no. 10: 5270.

Journal article
Published: 12 September 2020 in Sustainability
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During the past two decades, open source learning platforms (OSLPs) have become a dominant part of modern education. OSLPs are free for usage and customization—unlike proprietary software restricted by copyright licenses. By utilizing OSLPs, users can download and use the source code, write new features, fix bugs, improve performances, or learn from others how specific problems can be solved. Albeit dominant, the frequency of usage and motivation of OSLPs by students is not high; however, there has been very little research about this, and the problem is significant. Therefore, this research aimed to derive the factors that affect the adoption and diffusion of OSLPs. The factors on the diffusion and adoption were defined based on the innovation diffusion theory (IDT) and the technology acceptance model (TAM), where the integrated theoretical framework is called the IDT-TAM. Partial Least Square structural equation modeling was used to confirm the hypothesized IDT-TAM. An empirical study was based on the sample data collected from 340 Taiwanese technical university students to demonstrate the feasibility of the analytical framework and derive the factors related to the adoption and diffusion of the OSLP for students. Based on the results of the empirical study, through the mediation of perceived attitude (PA) and perceived usefulness (PU), trialability (TL), observability (OS), ease of use (EU), and relative advantage (RA) are the factors most related to the diffusion and acceptance of the OSLP innovations. The analytical results can serve as the basis for the design, development, and enhancement of acceptance and diffusion of OSLP.

ACS Style

Chi-Yo Huang; Hui-Ya Wang; Chia-Lee Yang; Steven Shiau. A Derivation of Factors Influencing the Diffusion and Adoption of an Open Source Learning Platform. Sustainability 2020, 12, 7532 .

AMA Style

Chi-Yo Huang, Hui-Ya Wang, Chia-Lee Yang, Steven Shiau. A Derivation of Factors Influencing the Diffusion and Adoption of an Open Source Learning Platform. Sustainability. 2020; 12 (18):7532.

Chicago/Turabian Style

Chi-Yo Huang; Hui-Ya Wang; Chia-Lee Yang; Steven Shiau. 2020. "A Derivation of Factors Influencing the Diffusion and Adoption of an Open Source Learning Platform." Sustainability 12, no. 18: 7532.

Journal article
Published: 27 September 2018 in Sustainability
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Geographic information is a confluence of knowledge from spatial science, information technologies, engineering, and mathematics, etc. Effective spatial training can enhance achievement in science, technology, engineering, and mathematics (STEM) education. Therefore, the geographic information system (GIS) plays a daily role in modern STEM education. Volunteered Geographical Information (VGI) is characterized by the openness of the geographic information being generated and accumulated by volunteers. Within the VGI sphere, OpenStreetMap (OSM) is one of the most well-known VGI due to its openness, flexibility, cost-effectiveness, and web-based mapping capability, making it one of the best alternatives for use as the mapping application for STEM education. However, very few or no prior works have investigated the factors influencing the innovation diffusion of OSM in STEM education. Therefore, to fill this gap, this work aims to investigate these factors. To achieve this purpose, the authors have defined an analytic framework based on innovation diffusion theory (IDT) and the technology acceptance model (TAM). The factors influencing students’ acceptance and intention to continue using and diffusing OSM in STEM education will be investigated. Partial least squares structural equation modeling (PLS-SEM) was used to confirm the hypothesized IDT–TAM integrated model. An empirical study based on sample data collected from 145 Taiwanese undergraduate and graduate students from engineering-related institutes was used to demonstrate the feasibility of the proposed analytic framework and to derive the factors related to the adoption and diffusion of OSM in STEM education. The proposed theoretical framework forged in this study was proven to be successful. Based on the empirical study results, ease of use, observability, and compatibility are the most influential factors in OSM diffusion. Therefore, activities that enhance the ease of use, observability, and compatibility of OSM should be emphasized so that STEM students’ perception of the usefulness of the technology and their perceived attitude towards it leads to the intention to continue the use of OSM. The analytic results can serve as a foundation for the design, development, and accelerated adoption and diffusion of OSM in STEM education.

ACS Style

Steven J. H. Shiau; Chi-Yo Huang; Chia-Lee Yang; Jer-Nan Juang. A Derivation of Factors Influencing the Innovation Diffusion of the OpenStreetMap in STEM Education. Sustainability 2018, 10, 3447 .

AMA Style

Steven J. H. Shiau, Chi-Yo Huang, Chia-Lee Yang, Jer-Nan Juang. A Derivation of Factors Influencing the Innovation Diffusion of the OpenStreetMap in STEM Education. Sustainability. 2018; 10 (10):3447.

Chicago/Turabian Style

Steven J. H. Shiau; Chi-Yo Huang; Chia-Lee Yang; Jer-Nan Juang. 2018. "A Derivation of Factors Influencing the Innovation Diffusion of the OpenStreetMap in STEM Education." Sustainability 10, no. 10: 3447.