scholarly journals Topic Model Analysis of Research Themes and Trends in the Journal of Economic and Environmental Geology

2021 ◽  
Vol 54 (3) ◽  
pp. 353-364
Author(s):  
Taeyong Kim ◽  
Hyemin Park ◽  
Junyong Heo ◽  
Minjune Yang
Keyword(s):  
Topic Model ◽  
Model Analysis ◽  
Environmental Geology ◽  
Research Themes

Topics and topical phases in German social media communication during a disaster

2018 ◽  
Vol 24 (2) ◽  
pp. 221-264 ◽  
Author(s):  
SABINE GRÜNDER-FAHRER ◽  
ANTJE SCHLAF ◽  
GREGOR WIEDEMANN ◽  
GERHARD HEYER
Keyword(s):  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Disaster Management ◽  
Language Processing ◽  
Topic Model ◽  
Model Analysis ◽  
Media Content ◽  
Media Communication ◽  
Social Media Communication

AbstractSocial media are an emerging new paradigm in interdisciplinary research in crisis informatics. They bring many opportunities as well as challenges to all fields of application and research involved in the project of using social media content for an improved disaster management. Using the Central European flooding 2013 as our case study, we optimize and apply methods from the field ofnatural language processingand unsupervised machine learning to investigate the thematic and temporal structure of German social media communication. By means of topic model analysis, we will investigate which kind of content was shared on social media during the event. On this basis, we will, furthermore, investigate the development of topics over time and apply temporal clustering techniques to automatically identify different characteristic phases of communication. From the results, we, first, want to reveal properties of social media content and show what potential social media have for improving disaster management in Germany. Second, we will be concerned with the methodological issue of finding and adapting natural language processing methods that are suitable for analysing social media data in order to obtain information relevant for disaster management. With respect to the first, application-oriented focal point, our study reveals high potential of social media content in the factual, organizational and psychological dimension of the disaster and during all stages of the disaster management life cycle. Interestingly, there appear to be systematic differences in thematic profile between the different platforms Facebook and Twitter and between different stages of the event. In context of our methodological investigation, we claim that if topic model analysis is combined with appropriate optimization techniques, it shows high applicability for thematic and temporal social media analysis in disaster management.

From Hating Places to Hating People: A Topic Model Analysis of Hate-Related Discourses in South Korean News Reports

2020 ◽  
Vol 28 (4) ◽  
pp. 5-27
Author(s):  
Youngwook Kim ◽  
Seungkyung Ham ◽  
Ji-Myung Choi ◽  
Hyejung Kim
Keyword(s):  
Topic Model ◽  
Model Analysis ◽  
News Reports ◽  
South Korean

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.

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