Search

Introduction: The analysis of contents in abstracts has proven helpful to researchers for effectively screening the information they need. On the one hand, this approach can save time, while on the other hand, it can also allow researchers to discover the core values and research trends in the investigated field of study. Thus, this study took all the text contents in abstracts from the Physical Education Journal, Sports & Exercise Research, Quarterly of Chinese Physical Education, Bulletin of Sport and Exercise Psychology of Taiwan, and Chinese Journal of Sports Biomechanics for the period from March 2009 to March 2019 as the target of its investigation, in an attempt to further explain the development statuses and trends of the target journals through a rigorous and objective analysis process. Methods: Utilizing the concepts of text mining and natural language processing, this paper adopted an analysis program suitable for digital volumes of text data. The adopted program included five steps: text extraction, text segmentation, TF-IDF weight analysis, co-word analysis, and network analysis. Results: Most of the research subjects included in the studies published in the target journals were students and athletes in Taiwan, with the research scope covering the fields of social science and natural science. Conclusion: A network map in this research should fall into the category of relevant academic fields in Taiwan becoming rich and diversified. However, the research topics under the category of "sports history and philosophy" appear to be slightly weak. We suggest that relevant scholars should combine research themes using diverse ways of thinking to achieve the goal of multi-level and multi-dimensional development of sports-related academics. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between candidates’ online popularity and election results, as a step towards creating a model to forecast the results of Taiwanese elections even in the absence of reliable opinion polls on a district-by-district level. 253 of 354 legislative candidates of single-member districts in Taiwan’s 2016 general election had active public Facebook pages during the election period. Hypothesizing that the relative popularity of candidates’ Facebook posts will be positively related to their election results, I calculated each candidate’s Like Ratio (i.e. proportions of all likes on Facebook posts obtained by candidates in their district). In order to have a measure of online interest without the influence of subjective positivity, I similarly calculated the proportion of daily average page views for each candidate’s Wikipedia page. I ran a regression analysis, incorporating data on results of previous elections and available opinion poll data. I found the models could describe the result of the election well and reject the null hypothesis. My models successfully predicted 80% of winners in single-member districts and were effective in districts without local opinion polls with a predictive power approaching that of traditional opinion polls. The models also showed good accuracy when run on data for the 2014 Taiwanese municipal mayors election. [ABSTRACT FROM AUTHOR]

Burials provide valuable information to study social structures based on the assumption that burials and associated grave goods can represent social roles and relations in a society. To study social relationships, network analysis has been increasingly applied to archaeological data to infer interactions and relationships between entities. Statistical approaches to network analysis, such as exponential random graph models (ERGMs), provide a way to test hypotheses about dynamic processes of network formation. However, computational difficulties and sensitivity to uncertainties limit the application of ERGMs. In this paper, we introduce a Bayesian framework on ERGMs that enables an efficient computational process, effective quantification of uncertainty, and robust model evaluation of network properties. We tested a hypothesis of social change relative to the arrival of Europeans by studying burial data from Kiwulan, an Iron Age site in northeastern Taiwan. The results indicate a transition among the burials from network ties based on ritual objects to wealth objects, and a more centralized structure with increased social differentiation after the European presence was established in the 17th century. Our case study demonstrates the effectiveness of Bayesian network analysis for archaeological data, and expands the use of burials in understanding the impacts of colonial presence on Indigenous groups in a pericolonial context. • We present Bayesian inference on exponential random graph models (ERGMs) to model archaeological networks. • We explore social processes and network structures in archaeological data from two time periods. • Differences in network statistics imply change over time in social structures influenced by foreign presence. • Bayesian ERGMs are efficient and useful for evaluating and interpreting networks. [ABSTRACT FROM AUTHOR]