Research Topics and Trends in the Maritime Transport: a Structural Topic Model

Author(s):  
Xiwen Bai ◽  
Xiunian Zhang ◽  
Kevin X. Li ◽  
Yaoming Zhou ◽  
Kum Fai Yuen
2022 ◽  
Vol 34 (3) ◽  
pp. 1-13
Author(s):  
Jianzu Wu ◽  
Kunxin Zhang

This article examines the policy implementation literature using a text mining technique, known as a structural topic model (STM), to conduct a comprehensive analysis of 547 articles published by 11 major journals between 2000 and 2019. The subject analyzed was the policy implementation literature, and the search included titles, keywords, and abstracts. The application of the STM not only allowed us to provide snapshots of different research topics and variation across covariates but also let us track the evolution and influence of topics over time. Examining the policy implementation literature has contributed to the understanding of public policy areas; the authors also provided recommendations for future studies in policy implementation.


2020 ◽  
Vol 125 (2) ◽  
pp. 1229-1251
Author(s):  
Andreas Rehs

AbstractThe detection of differences or similarities in large numbers of scientific publications is an open problem in scientometric research. In this paper we therefore develop and apply a machine learning approach based on structural topic modelling in combination with cosine similarity and a linear regression framework in order to identify differences in dissertation titles written at East and West German universities before and after German reunification. German reunification and its surrounding time period is used because it provides a structure with both minor and major differences in research topics that could be detected by our approach. Our dataset is based on dissertation titles in economics and business administration and chemistry from 1980 to 2010. We use university affiliation and year of the dissertation to train a structural topic model and then test the model on a set of unseen dissertation titles. Subsequently, we compare the resulting topic distribution of each title to every other title with cosine similarity. The cosine similarities and the regional and temporal origin of the dissertation titles they come from are then used in a linear regression approach. Our results on research topics in economics and business administration suggest substantial differences between East and West Germany before the reunification and a rapid conformation thereafter. In chemistry we observe minor differences between East and West before the reunification and a slightly increased similarity thereafter.


Author(s):  
Lifeng He ◽  
Dongmei Han ◽  
Xiaohang Zhou ◽  
Zheng Qu

Many web-based pharmaceutical e-commerce platforms allow consumers to post open-ended textual reviews based on their purchase experiences. Understanding the true voice of consumers by analyzing such a large amount of user-generated content is of great significance to pharmaceutical manufacturers and e-commerce websites. The aim of this paper is to automatically extract hidden topics from web-based drug reviews using the structural topic model (STM) to examine consumers’ concerns when they buy drugs online. The STM is a probabilistic extension of Latent Dirichlet Allocation (LDA), which allows the consolidation of document-level covariates. This innovation allows us to capture consumer dissatisfaction along with their dynamics over time. We extract 12 topics, and five of them are negative topics representing consumer dissatisfaction, whose appearances in the negative reviews are substantially higher than those in the positive reviews. We also come to the conclusion that the prevalence of these five negative topics has not decreased over time. Furthermore, our results reveal that the prevalence of price-related topics has decreased significantly in positive reviews, which indicates that low-price strategies are becoming less attractive to customers. To the best of our knowledge, our work is the first study using STM to analyze the unstructured textual data of drug reviews, which enhances the understanding of the aspects of drug consumer concerns and contributes to the research of pharmaceutical e-commerce literature.


2019 ◽  
Vol 1 (1) ◽  
pp. 45-78
Author(s):  
Chankyung Pak

Abstract To disseminate their stories efficiently via social media, news organizations make decisions that resemble traditional editorial decisions. However, the decisions for social media may deviate from traditional ones because they are often made outside the newsroom and guided by audience metrics. This study focuses on selective link sharing as quasi-gatekeeping on Twitter ‐ conditioning a link sharing decision about news content. It illustrates how selective link sharing resembles and deviates from gatekeeping for the publication of news stories. Using a computational data collection method and a machine learning technique called Structural Topic Model (STM), this study shows that selective link sharing generates a different topic distribution between news websites and Twitter and thus significantly revokes the specialty of news organizations. This finding implies that emergent logic, which governs news organizations’ decisions for social media, can undermine the provision of diverse news.


Water Policy ◽  
2017 ◽  
Vol 19 (3) ◽  
pp. 496-512 ◽  
Author(s):  
Hanchen Jiang ◽  
Maoshan Qiang ◽  
Peng Lin ◽  
Qi Wen ◽  
Bingqing Xia ◽  
...  

Development of the Brahmaputra River, which links China, India and Bangladesh, has been hindered by significant challenges, particularly political challenges. News reports can mirror the perceptions of political actors, but are, owing to the complexity of the issue, complicated and unstructured. We present a comparative content analysis of the overall framing in news reports of the Brahmaputra River development from major English news media. A structural topic model is established to discover latent topics in the corpus of 1,569 news articles published in 34 countries or regions. We find that politics, including domestic and international politics, dominates the news narratives. Environmental issues, such as glacier status and climate change impacts, are secondarily discussed. Technology and economy issues are less frequently presented in the media coverage. Advantages of upstream countries and dependences of downstream countries are reflected in news reporting and explicitly emerge in the structural topic model. These findings and implications are important for promoting mutual understanding and cooperation among riparian countries in developing the Brahmaputra River. The proposed approach is expected to be widely used as a methodological strategy in future water policy studies.


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