scholarly journals Detecting latent topics and trends in educational technologies over four decades using structural topic modeling: A retrospective of all volumes of Computers & Education

2020 ◽  
Vol 151 ◽  
pp. 103855 ◽  
Author(s):  
Xieling Chen ◽  
Di Zou ◽  
Gary Cheng ◽  
Haoran Xie
Keyword(s):  
Topic Modeling ◽  
Educational Technologies ◽  
Latent Topics ◽  
Structural Topic Modeling

scholarly journals Exploring Latent Topics and International Research Trends in Competency-Based Education Using Topic Modeling

2021 ◽  
Vol 11 (6) ◽  
pp. 303
Author(s):  
Seungsu Paek ◽  
Taehun Um ◽  
Namhyoung Kim
Keyword(s):  
Education Reform ◽  
International Research ◽  
Topic Modeling ◽  
Modern Society ◽  
Research Trends ◽  
Competency Development ◽  
Competency Based Education ◽  
Competency Based ◽  
Latent Topics ◽  
International Research Trends

Recently, there has been growing educational interest in competency. Global organizations, such as the United Nations (UN) and Organization for Economic Co-operation and Development (OECD), which are leading the discourse on education reform, are undertaking the lead in spreading awareness regarding competency education. Since 2015, the number of published articles on competency education has been rapidly increasing. This paper aims to provide significant implications for creating a sustainable future of competency education. A topic modeling method was used to empirically analyze latent topics and international research trends in 26,532 articles published on competency-based education (CBE). As a result of the analysis, 15 topics were derived, including “approach to competency development.” In addition, five topics including “learning skills” and “teacher training” were found to be hot topics with the increasing article publication. The rapidly changing modern society is calling for a transformation in education. We hope that the results of this study paves the way for further research exploring new directions for education, such as competency education.

Analysis and Visualization Latent Topic on COVID-19 Vaccine Tweet use two-stage topic modeling (Preprint)

2021 ◽  
Author(s):  
Faizah Faizah ◽  
Bor-Shen Lin
Keyword(s):  
Topic Modeling ◽  
Public Perception ◽  
Latent Dirichlet Allocation ◽  
World Health ◽  
Two Stage ◽  
The Public ◽  
Global Pandemic ◽  
Difficult Time ◽  
Latent Topic ◽  
Latent Topics

BACKGROUND The World Health Organization (WHO) declared COVID-19 as a global pandemic on January 30, 2020. However, the pandemic has not been over yet. Furthermore, in the first quartal of 2021, some countries face the third wave of the pandemic. During the difficult time, the development of the vaccines for COVID-19 accelerates rapidly. Understanding the public perception of the COVID-19 Vaccine according to the data collected from social media can widen the perspective on the state of the global pandemic OBJECTIVE This study explores and analyzes the latent topic on COVID-19 Vaccine Tweet posted by individuals from various countries by using two-stage topic modeling. METHODS A two-stage analysis in topic modeling was proposed to investigating people’s reactions in five countries. The first stage is Latent Dirichlet Allocation that produces the latent topics with the corresponding term distributions that facilitate the investigators to understand the main issues or opinions. The second stage then performs agglomerative clustering on the latent topics based on Hellinger distance, which merges close topics hierarchically into topic clusters to visualize those topics in either tree or graph views. RESULTS In general, the topic discussion regarding the COVID-19 Vaccine in five countries is similar. Topic themes such as "first vaccine" and & "vaccine effect" dominate the public discussion. The remarkable point is that people in some countries have some topic themes, such as "politician opinion" and " stay home" in Canada, "emergency" in India, and & "blood clots" in the United Kingdom. The analysis also shows the most popular COVID-19 Vaccine, which is gaining more public interest. CONCLUSIONS With LDA and Hierarchical clustering, two-stage topic modeling is powerful for visualizing the latent topics and understanding the public perception regarding the COVID-19 Vaccine.

scholarly journals Infodemiology: Computational Methodologies for quantifying and visualizing key characteristics of the COVID-19 infodemic

2021 ◽  
Author(s):  
Dominic Ligot ◽  
Frances Claire Tayco ◽  
Mark Toledo ◽  
Carlos Nazareno ◽  
Denise Brennan-Rieder
Keyword(s):  
Social Media ◽  
Topic Modeling ◽  
Time Varying ◽  
Specific Topic ◽  
False Information ◽  
Conspiracy Theories ◽  
Heat Maps ◽  
Key Characteristics ◽  
Latent Topics ◽  
Taking Action

Objectives. Infodemics of false information on social media is a growing societal problem, aggravated by the occurrence of the COVID-19 pandemic. The development of infodemics has characteristic resemblances to epidemics of infectious diseases. This paper presents several methodologies which aim to measure the extent and development of infodemics through the lens of epidemiology.Methods. Time varying R was used as a measure for the infectiousness of the infodemic, topic modeling was used to create topic clouds and topic similarity heat maps, while network analysis was used to create directed and undirected graphs to identify super-spreader and multiple carrier communities on social media.Results. Forty-two (42) latent topics were discovered. Reproductive trends for a specific topic were observed to have significantly higher peaks (Rt 4-5) than general misinformation (Rt 1-3). From a sample of social media misinformation posts, a total of 385 groups and 804 connections were found within the network, with the largest group having 1,643 shares and 1,063,579 interactions over a 12 month period.Conclusions. These approaches enable the measurement of the infectiousness of an infodemic, comparative analysis of infodemic topics, and identification of likely super-spreaders and multiple carriers on social media. The results of these analyses can form the basis for taking action to stem an ongoing spread of misinformation on social media and mitigate against future infodemics. The methods are not confined to health misinformation and may be applied to other infodemics, such as conspiracy theories, political disinformation, and climate change denial.

Structural Topic Modeling For Social Scientists: A Brief Case Study with Social Movement Studies Literature, 2005–2017

2019 ◽  
Vol 6 (4) ◽  
pp. 307-318 ◽  
Author(s):  
Nathan C. Lindstedt
Keyword(s):  
Social Movement ◽  
Topic Modeling ◽  
Research Process ◽  
Computer Assisted ◽  
Sociological Research ◽  
Social Scientists ◽  
Modeling Framework ◽  
Social Scientific ◽  
Structural Topic Modeling

Sociologists frequently make use of language as data in their research using methodologies including open-ended surveys, in-depth interviews, and content analyses. Unfortunately, the ability of researchers to analyze the growing amount of these data declines as the costs and time associated with the research process increases. Topic modeling is a computer-assisted technique that can help social scientists to address these data challenges. Despite the central role of language in sociological research, to date, the field has largely overlooked the promise of automated text analysis in favor of more familiar and more traditional methods. This article provides an overview of a topic modeling framework especially suited for social scientific research. By way of a case study using abstracts from social movement studies literature, a short tutorial from data preparation through data analysis is given for the method of structural topic modeling. This example demonstrates how text analytics can be applied to research in sociology and encourages academics to consider such methods not merely as novel tools, but as useful supplements that can work beside and enhance existing methodologies.

Content analysis of newspaper coverage of wolf recolonization in France using structural topic modeling

2018 ◽  
Vol 220 ◽  
pp. 254-261 ◽  
Author(s):  
Marie Chandelier ◽  
Agnès Steuckardt ◽  
Raphaël Mathevet ◽  
Sascha Diwersy ◽  
Olivier Gimenez
Keyword(s):  
Content Analysis ◽  
Topic Modeling ◽  
Newspaper Coverage ◽  
Structural Topic Modeling

Taiwan as ghost island? Ambivalent articulation of marginalized identities in computer-mediated discourses

2019 ◽  
Vol 31 (3) ◽  
pp. 285-306
Author(s):  
Yao-Tai Li ◽  
Yunya Song
Keyword(s):  
Critical Discourse Analysis ◽  
Topic Modeling ◽  
Mainland China ◽  
Critical Discourse ◽  
Bulletin Board ◽  
Social Critique ◽  
Computer Mediated ◽  
Discourse Historical Approach ◽  
Self Presentations ◽  
Structural Topic Modeling

This study examines the conflicting self-presentations when using the term ‘ghost island’ in Taiwan, a self-mocking way to belittle the homeland. While some view this term as a form of social critique, others consider it to be suggestive of a social malaise affecting contemporary Taiwanese. Drawing on online posts and comments from the most popular bulletin board system in Taiwan, this study combines topic modeling with a discourse-historical approach (DHA) to critical discourse analysis (CDA) to examine the constructions of ‘ghost island’ by Taiwanese netizens. A computer-aided content analysis was implemented using Structural Topic Modeling (STM) to identify discourse topics associated with netizens’ discourses on ghost island. Our findings suggest that the images of ‘us’ (the ordinary people) are presented as victims as against powerful ‘others’ (e.g. mainland China and local elites). Specifically, self-mockery was often invoked to project a loser image and marginalized status living on the island, whereas self-assertive narratives were invoked to affirm Taiwanese society’s democracy and freedom. The conflicting narratives – with a mixture of grudge, helplessness, pessimism, hope and pride – point to Taiwanese netizens’ ambivalent articulation of marginalized identities that operates to strengthen affective connectedness and virtual bonding.

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