Running as a Woman? Candidate Presentation in the 2018 Midterms

2020 ◽  
Vol 73 (4) ◽  
pp. 967-987
Author(s):  
Maura McDonald ◽  
Rachel Porter ◽  
Sarah A. Treul
Keyword(s):  
Topic Model ◽  
Female Candidate ◽  
Political Work ◽  
Political Experience ◽  
Midterm Elections ◽  
Use Values ◽  
Primary Driver ◽  
Year Of The Woman ◽  
Structural Topic Model ◽  
The U.S

The record high number of women who ran for the U.S. Congress during the midterm elections led many journalists to proclaim 2018 as another “Year of the Woman.” Although not every female candidate was successful, this large number of women running for office provides the opportunity to advance our understanding of the ways in which women present themselves to their voters. Using the “Biography” pages of more than 1,500 2018 congressional campaign websites, we use a structural topic model to examine how these candidates present themselves to their constituencies. In doing this, we find great variance in the presentation styles of women running for Congress in 2018. We also find that prior political experience, more so than gender, is the primary driver in influencing how candidates (both men and women) present themselves. Experienced candidates use similar styles that highlight their past political work while amateur candidates are more likely to use “values-driven” language.

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

2020 ◽  
Author(s):  
Xiwen Bai ◽  
Xiunian Zhang ◽  
Kevin X. Li ◽  
Yaoming Zhou ◽  
Kum Fai Yuen
Keyword(s):  
Topic Model ◽  
Maritime Transport ◽  
Research Topics ◽  
Structural Topic Model

scholarly journals The Voice of Drug Consumers: Online Textual Review Analysis Using Structural Topic Model

2020 ◽  
Vol 17 (10) ◽  
pp. 3648 ◽  
Author(s):  
Lifeng He ◽  
Dongmei Han ◽  
Xiaohang Zhou ◽  
Zheng Qu
Keyword(s):  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Web Based ◽  
Pharmaceutical Manufacturers ◽  
Textual Data ◽  
Consumer Dissatisfaction ◽  
The Voice ◽  
Document Level ◽  
Structural Topic Model ◽  
Over Time

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.

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

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.

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

Water Policy ◽  
2017 ◽  
Vol 19 (3) ◽  
pp. 496-512 ◽  
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.

The End of Midterm Decline?

2003 ◽  
Vol 27 (2) ◽  
pp. 139-164
Author(s):  
Lex Renda
Keyword(s):  
Nineteenth Century ◽  
Twentieth Century ◽  
House Of Representatives ◽  
Historical Perspective ◽  
District Level ◽  
Level Data ◽  
Midterm Elections ◽  
Average Loss ◽  
Open Seat ◽  
The U.S

Variations in the loss of seats in the House of Representatives by the president's party in midterm elections between 1854 and 1998 are analyzed from a historical perspective. Whereas in the latter three-fourths of the nineteenth century the president's party lost, on average, 22% of its share of House seats, in the twentieth century the average loss was 13%. Using district-level data, the author attributes the problematization of “midterm decline” to the growing power of incumbency (a consequence of the development of the Australian ballot), the decline in the number of partisanly competitive districts in open-seat elections, and the limitation, since 1912, of the size of the U.S. House of Representatives.

Sign in / Sign up

Export Citation Format

Share Document