A Structural Topic Model of the Features and the Cultural Origins of the Baconian Program

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.

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News Organizations’ Selective Link Sharing as Gatekeeping : A Structural Topic Model Approach

Computational Communication Research ◽

10.5117/ccr2019.1.003.pak ◽

2019 ◽

Vol 1 (1) ◽

pp. 45-78

Author(s):

Chankyung Pak

Keyword(s):

Social Media ◽

Topic Model ◽

News Stories ◽

News Websites ◽

News Organizations ◽

Computational Data ◽

Topic Distribution ◽

Editorial Decisions ◽

Structural Topic Model ◽

Model Approach

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.

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Framing the Brahmaputra River hydropower development: different concerns in riparian and international media reporting

Water Policy ◽

10.2166/wp.2017.056 ◽

2017 ◽

Vol 19 (3) ◽

pp. 496-512 ◽

Cited By ~ 4

Author(s):

Hanchen Jiang ◽

Maoshan Qiang ◽

Peng Lin ◽

Qi Wen ◽

Bingqing Xia ◽

...

Keyword(s):

News Media ◽

Media Coverage ◽

Water Policy ◽

Topic Model ◽

Climate Change Impacts ◽

Brahmaputra River ◽

News Reporting ◽

News Reports ◽

Technology And Economy ◽

Structural Topic Model

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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Machine Learning Algorithms for Big Data Applications With Policy Implementation

Journal of Organizational and End User Computing ◽

10.4018/joeuc.287570 ◽

2022 ◽

Vol 34 (3) ◽

pp. 1-13

Author(s):

Jianzu Wu ◽

Kunxin Zhang

Keyword(s):

Policy Implementation ◽

Topic Model ◽

Comprehensive Analysis ◽

Machine Learning Algorithms ◽

Research Topics ◽

Future Studies ◽

Mining Technique ◽

Big Data Applications ◽

The Subject ◽

Structural Topic Model

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.

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