A Structural Topic Model Analysis of Privacy in Mandarin Chinese News: 2010–2019

2021 ◽  
Vol 58 (1) ◽  
pp. 792-794
Author(s):  
Yuanye Ma
Keyword(s):  
Mandarin Chinese ◽  
Topic Model ◽  
Model Analysis ◽  
Structural Topic Model

scholarly journals Communicating Corporate Social Responsibility through Twitter: a topic model analysis on selected companies

2020 ◽  
Author(s):  
Annamaria Bianchi ◽  
Camilla Salvatore ◽  
Silvia Biffignandi
Keyword(s):  
Corporate Social Responsibility ◽  
Social Responsibility ◽  
Topic Model ◽  
Model Analysis ◽  
Topic Detection ◽  
Dow Jones Industrial Average ◽  
Corporate Social ◽  
Model Algorithm ◽  
Document Level ◽  
Structural Topic Model

Social media are fundamental in creating new opportunities for firms and they represent a relevant tool for the communication and the engagement with customers. The purpose of this paper is to analyse the communication of Corporate Social Responsibility (CSR) activities on Twitter. We consider the listed companies included in the Dow Jones Industrial Average Index and we implement a topic model analysis on their timelines. In order to identify the topic discussed, their correlation, and their evolution over time and sectors,we apply the Structural Topic Model algorithm, which allows estimating the model including document-level metadata. This model proves to be a powerful tool for topic detection and for estimating the effects of document-level metadata. Indeed, we find that the topics are overall well identified, and the model allows catching signals from the data. Finally, we discuss issues related to the validity of the analysis, including data quality problems.

scholarly journals Structural Topic Model Analysis of Mask-Wearing Issue Using International News Big Data

2021 ◽  
Vol 18 (12) ◽  
pp. 6432
Author(s):  
Kyeo Re Lee ◽  
Byungjun Kim ◽  
Dongyan Nan ◽  
Jang Hyun Kim
Keyword(s):  
Big Data ◽  
Topic Model ◽  
Model Analysis ◽  
Big Data Analysis ◽  
World Health ◽  
International News ◽  
The World ◽  
Before And After ◽  
The Face ◽  
Structural Topic Model

Media plays an important role in the acquisition of health information worldwide. This was particularly evident in the face of the COVID-19 epidemic. Relatedly, it is practical and desirable for people to wear masks for health, fashion, and religious regions. However, depending on cultural differences, people naturally accept wearing a mask, or they look upon it negatively. In 2020, the COVID-19 pandemic led to widespread mask-wearing mandates worldwide. In the case of COVID-19, wearing a mask is strongly recommended, so by analyzing the news data before and after the spread of the epidemic, it is possible to see how the direction of crisis management is being structured. In particular, by utilizing big data analysis of international news data, discourses around the world can be analyzed more deeply. This study collected and analyzed 58,061 international news items related to mask-wearing from 1 January 2019 to 31 December 2020. The collected dataset was compared before and after the World Health Organization’s pandemic declaration by applying structural topic model analysis. The results revealed that prior to the declaration, issues related to the COVID-19 outbreak were emphasized, but afterward, issues related to movement restrictions, quarantine management, and local economic impacts emerged.

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.

Sign in / Sign up

Export Citation Format

Share Document