Dynamic Topic Model Research Articles

The modern synthesis and "Progress" in evolution: a view from the journal literature.

The concept of "progress" in evolutionary theory and its relationship to a putative notion of "Progress" in a global, normatively loaded sense of "change for the better" have been the subject of debate since Darwin admonished himself in a marginal note to avoid using the terms 'higher' and 'lower.' While an increase in some kind of complexity in the natural world might seem self-evident, efforts to explicate this trend meet notorious philosophical difficulties. Numerous historians pin the Modern Synthesis as a pivotal moment in this history; Michael Ruse even provocatively hypothesizes that Ernst Mayr and other "architects" of the Synthesis worked actively to eliminate Progress from evolutionary biology's scientific purview. I evaluate these claims here with a textual analysis of the journals Evolution and Proceedings of the Royal Society B (a corpus of 27,762 documents), using a dynamic topic modeling approach to track the fate of the term 'progress' across the Modern Synthesis. The claim that this term declines in importance for evolutionary theorizing over this period can, indeed, be supported; more tentative evidence is also provided that the discussion of 'progress' is largely absent from the British context, emphasizing the role of American paleontology in the rise and fall of 'progress' in 20th-century evolutionary biology.

Bi-channel dynamic topic model for quality monitoring considering initial and additional online customer reviews

E-commerce provides a vast space for user-generated content, including user reviews from the initial use of products or services to subsequent usage. Existing methods mainly focus on unmodified reviews and ignore customer perception after more experience with products. To address this, we propose a Markov chain-based bi-channel dynamic topic model (BDTM), which extends the sequential structure of the dynamic topic model and incorporates initial customer reviews and additional reviews to reflect topic shifts caused by customer experience perception. Then, a control chart based on word mover’s distance (WMD), called a BDTM-WMD (B-W) chart, is proposed to monitor topic shifts under BDTM. An alternative multiscale dynamic topic model (M-K) chart is constructed for comparison. Using a simulation approach, we find that compared with the existing sequential reverse joint sentiment-topic (SRJST) chart and joint sentiment topic-rating meets review (JSTRMR) chart, the proposed B-W chart is more sensitive to small shifts. Case study 1, using real data, shows the advantages of the proposed BDTM, B-W chart and M-K chart under both initial and additional reviews. Case study 2 shows that with additional reviews, the proposed B-W chart triggers out-of-control signals earlier than those by the existing joint sentiment topic-rating meets review (JSTRMR) chart.

동적 토픽 모델링(DTM)을 활용한 국내 수학교육의 정의적 영역에 관한 연구 동향 분석

This study analyzes trends in affective domain research through dynamic topic modeling, examining 380 papers published in seven major Korean mathematics education journals. The number of topics was set at nine based on coherence and perplexity measures, and topics were identified by considering the frequency and relevance of keywords. Topic 1 was disposition, attitude, interest, achievement, and pedagogy, Topic 2 was anxiety, interest, belief, factors, and levels, Topic 3 was attitude, problem solving, and programs, Topic 4 was attitude, belief, abilities, and learning activities, Topic 5 was belief, motivation, anxiety, programs, and activities, Topic 6 was affect(meta-affect), attitude, levels, and achievement, Topic 7 was self-efficacy, attitude, mathematical models, and practices, Topic 8 was value, motivation, instruction, and achievement, and Topic 9 was anxiety, motivation, textbooks, and storytelling. The topics covered various themes such as attitudes, anxiety, beliefs, motivation, and achievement, reflecting a strong connection to cognitive aspects like achievement, ability, and problem-solving. While overall patterns were similar, topics related to anxiety and beliefs (Topics 2 and 5) showed a distinct upward trend. The emergence of new terms such as ‘meta-affect’ and ‘mindset’ indicates the expanding scope of affective domain research. These findings suggest the need for continued exploration of new areas within the affective domain, alongside its links to cognitive factors, to support students' affective achievements.

DTM-Based Analysis of Hot Topics and Evolution of China’s Energy Policy

Quantitative research on the evolution and transformation of topics in China’s energy policy can enhance the theoretical and methodological framework of policy document analysis. Utilizing dynamic topic modeling (DTM) and social network analysis, this study examined 1872 energy policy documents issued in China between 1980 and 2023, focusing on detecting hot topics and analyzing trend evolution. DTM identified five core topics: State Grid and new energy, comprehensive energy conservation and emission reduction, intelligent building energy management, promotion of energy-saving products and new energy vehicles, and standardization of energy industry management. Temporal analysis of these core topics reveals a shift in policy focus over time, moving from infrastructure development and standardization management to new energy development and modernization of the energy system. The co-occurrence network of thematic terms transitions from an “independent and loose” structure to a “concentrated and balanced” one, with increasing network scale and frequency. The conclusions of this study offer valuable insights for establishing a dynamic monitoring and real-time updating mechanism for energy policies, enhancing the integration and coordination of energy policy topics, and effectively supporting national energy strategies in response to global energy market challenges.

Ozempic (Glucagon-like peptide 1 receptor agonist) in social media posts: Unveiling user perspectives through Reddit topic modeling

Semaglutide, a Glucagon-like peptide 1 (GLP-1) receptor agonist marketed under the brand name Ozempic, is originally prescribed for diabetes treatment and obesity management. However, healthy individuals without a medical cause use Ozempic without medical supervision to improve their physical appearance - a trend that has proliferated through social media, news coverage, and relevant celebrity endorsements. Thus, exploring social media posts can provide insight into understanding individuals’ experiences, beliefs, motivation, as well as misconceptions about Ozempic. To do so, this study utilizes BERTopic, a natural language processing approach for topic modeling, to analyze 46,491 Reddit posts from three subreddits (r/ozempic, r/ozempicforweightloss, r/semaglutide) dated between April 2019 and December 2023. The analysis revealed various discussion topics, including using Ozempic for weight loss, dosaging, insurance denial due to lack of a diabetes diagnosis, weight loss tracking, and side effect management. Overall, the overarching theme centered on the off-label use of Ozempic and its GLP-1 agonist counterparts for weight loss purposes. Moreover, awareness on the health hazards associated with the off-label and unsupervised use of Ozempic as an image enhancer do not frequently appear in the social media discussions. These findings, supported by a dynamic topic modeling analysis, offer ecological insights into the experiences and opinions of community members in Ozempic-related subreddits, reinforcing the growing evidence of the drug's increasing popularity for weight management as well as the role played by social media. The study also shows how information campaigns about the health risks associated with the off-label use of Ozempic by healthy individuals without a medical cause may help counterbalance the lack of risk awareness detected in social media discussions.

Global strategies for disruptive technology protection and regulation: evidence from policy textual analysis

ABSTRACT Disruptive technology (DT) has the power to reshape markets and societal structures, necessitating both protection and regulation. This paper aims to examine DT protection and regulation strategies at both micro and macro levels through policy texts. We construct an analysis framework integrating Responsible Research and Innovation (RRI) principles, innovation theory, and DT growth process. Key measures are extracted by content analysis, and dynamic topic model is used to discover evolving policy focuses. The framework is applied to analyze DT policy files of major powers. The results indicate that supply-side policy tools are employed to promote sustainable development in DT innovation, while environmental tools are utilised for early governance to preempt potential risks and innovation damage. The focuses of DT policies exhibit three evolution trends: attenuation, reinforcement, and fluctuation. Protective policies show concentration and coherence, while regulatory policies demonstrate integration and correlation. This study broadens existing perspectives and analytical frameworks, providing valuable insights for DT development and governance.

Study on the Theme Evolution and Synergy Assessment of China’s New Energy Vehicle Policy Texts

Drawing on data from 133 Chinese New Energy Vehicle (NEV) policy documents from 2007 to 2023, this study utilizes Dynamic Topic Modelling (DTM), social network analysis and a quantitative model to investigate the evolutionary path of policy themes and the coordination effects. The following results were obtained. (1) A thematic cross-sectional analysis identified six core policy themes, namely, coordinated promotion of technology and finance, industry development and safety standardisation, market service and technical support systems, promotion strategy and urban cluster development, industrial capital and safety supervision mechanisms, and policy support and market expansion. The analysis also mapped the distribution of hot spots within these themes. (2) The keyword co-occurrence network of the NEV policy indicated that the network structure evolved from an initial ‘overall dispersion–theme concentration’, comprising 16 policy themes, to an ‘overall stability–theme coordination’, consisting of 14 policy themes. (3) The coordination degrees across the three types of policies exhibited a consistent upward spiral, with the comprehensive coordination index surging from 30 in 2007 to 951 in 2023, underscoring the complementary effects among policy instruments. These conclusions offer valuable insights for government departments to understand NEV development trends and dynamically adjust policy themes accordingly.

Dynamic elicitation and forecasting innovation requirement of smart product-service system via user-manufacturer value co-creation perspective using multi-source data

Achieving greater efficiency and lower resource consumption is a constant pursuit for successfully developing smart product-service system (PSS). Conducting dynamic requirements analysis is increasingly important in the process while the value co-creation of smart PSS stakeholders has not been sufficiently considered. This study adopts a user-manufacturer value co-creation perspective to explore smart PSS dynamic requirement elicitation and forecasting. Using multi-source data of user online reviews, historical descriptive texts and patent abstracts, this study dynamically elicits the smart PSS innovation requirements using dynamic topic model (DTM) and bidirectional encoder representation from transformers (BERT). Furthermore, the key innovation requirements of smart PSS are identified by integrating their importance and satisfaction values into a dynamic importance-performance analysis (DIPA). Then, the trend of smart PSS key innovation requirements is predicted by the grey forecasting model to determine the smart PSS improvement direction. An empirical study of smartwatch service system is conducted to validate the proposed approach. By blending both user and manufacturer requirements, this study provides a novel framework for dynamic requirement elicitation and forecasting of smart PSS based on multi-source data. It contributes to facilitating the value co-creation during smart PSS development to optimize manufacturer’s R&D resource allocation and enhance user experience.

Topic modeling of financial accounting research over 70 years

AbstractI utilize latent Dirichlet allocation and dynamic topic model that are machine learning algorithms across a data set encompassing 25,990 financial accounting articles issued from 1956 to 2023 in 16 accounting journals, and impartially ascertain 20 research topics. The topics of mergers and acquisitions, disclosure and internal control, and political connection exhibited the most rapid expansion, whereas management control systems, earnings management, and valuation experienced the greatest contraction from 2014 to 2023. I also catalog the most referenced papers for each topic and highlight the most swiftly expanding and contracting topics within the realm of 21,620 SSRN working papers. Additionally, my analysis reveals a declining trend in the concentration of research interests within published articles over the preceding seven decades. This research on topic classification itself will aid accounting investigators in bypassing superfluous efforts and fostering increased interdisciplinary research.

Using natural language processing to evaluate temporal patterns in suicide risk variation among high-risk Veterans

Measuring suicide risk fluctuation remains difficult, especially for high-suicide risk patients. Our study addressed this issue by leveraging Dynamic Topic Modeling, a natural language processing method that evaluates topic changes over time, to analyze high-suicide risk Veterans Affairs patients’ unstructured electronic health records. Our sample included all high-risk patients that died (cases) or did not (controls) by suicide in 2017 and 2018. Cases and controls shared the same risk, location, and treatment intervals and received nine months of mental health care during the year before the relevant end date. Each case was matched with five controls. We analyzed case records from diagnosis until death and control records from diagnosis until matched case's death date. Our final sample included 218 cases and 943 controls. We analyzed the corpus using a Python-based Dynamic Topic Modeling algorithm. We identified five distinct topics, “Medication,” “Intervention,” “Treatment Goals,” “Suicide,” and “Treatment Focus.” We observed divergent change patterns over time, with pathology-focused care increasing for cases and supportive care increasing for controls. The case topics tended to fluctuate more than the control topics, suggesting the importance of monitoring lability. Our study provides a method for monitoring risk fluctuation and strengthens the groundwork for time-sensitive risk measurement.

Climate Communication with Chinese Youth by Chinese Nongovernmental Organizations: A Case Study of Chinese Weather Enthusiasts on Bilibili

Abstract Nongovernmental organizations (NGOs) have increasingly played pivotal roles in shaping climate agendas and mobilizing individuals to engage in environmental initiatives. However, the nature of NGOs’ online interaction with users, especially in developing countries, remains largely unexplored. This study focused on the dynamics of engagement between a Chinese NGO, Chinese Weather Enthusiasts (CWE), and Chinese youth on the social media platform of Bilibili. The research comprised two main components. First, named entity recognition was employed to analyze weather-related terms in CWE’s posts on Bilibili and dynamic topic modeling was utilized to uncover shifts in thematic focus. Subsequently, descriptive analysis and negative binomial regression were employed to investigate the correlation between weather types and user engagement metrics. The study unveiled two noteworthy findings: first, CWE posts are closely linked to short-term weather, providing timely content that may meet the public’s demand for climate information. Second, the engagement of Chinese youth users is not affected by extreme weather types. Future research should continue to elucidate strategies that NGOs can employ to enhance online engagement among youth users. Significance Statement This study seeks to contribute to the current literature of climate communication by investigating how NGOs engage with Chinese youth on social media, an area that has received scant attention thus far. Focusing on an influential Chinese climate NGO, CWE, and its interactions with Chinese youth on the social media platform of Bilibili, this research sheds lights on strategies to communicate information related to extreme weather to this demographic. Examining factors that influence online user engagement offers both theoretical insights about the mechanisms of climate communication and practical implications for NGOs and policymakers to mobilize youth for environmental initiatives. The findings also underscore the importance of tailoring climate communication to align with the daily experiences of the target audience and public-centric approaches in climate communication strategies.

Dynamic product competitive analysis based on online reviews

Competitive intelligence is vital for enterprises to survive in the market. Recently, online reviews have gained popularity among enterprises and researchers as a means to acquire timely and precise competitive insights. However, extant studies overlook the evolution of competitive information because they do not account for the variation of online reviews and products. In this research, we propose a method for dynamic competitive analysis by concentrating on the changes in products and online reviews. First, products and their related online reviews are analyzed via Dynamic Topic Model to derive product features mentioned in different slices. Second, we use sentiment analysis to estimate product performance and transfer the results into a product competitive relation network. Third, we implement competitive analysis from the perspectives of products and markets based on competitiveness propagation. By tracking the evolution of competitive relations among products, we discover competitors and glean more competitive insights. Lastly, a case study of laptops is used for validation. Experimental results indicate that our method is effective in capturing evolving and potential competitive relations among products.

Analysis of Peatland Research Trends Based on BERTopic

Peatlands comprise approximately 3% of the land area worldwide. Peatland exists in most countries, including tropical, subtropical, and boreal regions. Accordingly, peatland has garnered increased research attention as a potential countermeasure against climate change. Therefore, it is necessary to identify and comprise the topics constituting global peatland research. In this study, we applied BERTopic—a topic modeling technique—to analyze relatedness between research topics to classify global peatland research trends, evaluate changes over time, and analyze the relationships between topics. To this end, we searched the keyword “peatland” on ScienceDirect—a global academic publication data platform—and collected the titles and abstracts from 10,158 publications from 1953 to 2022 for dynamic topic modeling and network analysis. Eighty-two peatland research topics were identified, which were combined into 15 main topics. Over time, an increasing trend was observed in topics related to production, management, and fire. In addition, upon analysis of the relationships between topics, three groups centered on fire, peatland value, and carbon were identified. We anticipate that the findings of this study can be expanded to analyze trends in research related to fires in peatlands, regional characteristics of peat soil, prediction of greenhouse gases emission and mitigation due to peatland fires, and prediction of future peatland research topics.

Topic Modeling for Evolving Textual Data Using LDA, HDP, NMF, BERTOPIC, and DTM With a Focus on Research Papers

As the volume of academic literature continues to burgeon, the necessity for advanced tools to decipher evolving research trends becomes increasingly apparent. This study delves into the utilization of topic modeling techniques—specifically Latent Dirichlet Allocation (LDA), Hierarchical Dirichlet Process (HDP), Non-negative Matrix Factorization (NMF), BERTopic, and Dynamic Topic Modeling (DTM)—applied to a dynamic corpus of research papers. Our research endeavors to confront the challenges posed by capturing temporal dynamics, evolving terminology, and interdisciplinary themes within academic literature. Through a comprehensive comparative investigation of these models, we assess their efficacy in extracting and tracking research topics over time. While DTM exhibited the highest term topic probability, its inclusion of non-meaningful words proved to be a hindrance to its suitability. Conversely, NMF, HDP, LDA, and BERTopic demonstrated comparable performance in topic extraction. Surprisingly, DTM emerged as the most effective model in our research, showcasing its prowess in navigating the intricacies of evolving research trends.

Deep factor asset pricing with policy guidance based on multi-source heterogeneous information

We propose a novel latent factor pricing model to extract latent pricing factors and corresponding factor loadings from multi-source heterogeneous information through a deep learning architecture. Notably, we pioneer the extraction of policy pricing factors from China’s national strategies (“Five-Year Plans”, “Government Work Reports”, and “Monetary Policy Reports”) using natural language processing and a dynamic topic model. The proposed mixed-frequency deep factor asset pricing (MIDAS-DF) model learns from mixed-frequency heterogeneous data and captures nonlinear joint patterns between inputs and outputs, providing more nuanced insights into asset pricing. The empirical analyses of the Chinese A-share market from January 1, 2003 to July 31, 2022 show that the MIDAS-DF model outperforms competing models in pricing individual stocks, various test portfolios, and investment portfolios. The results also demonstrate that low-frequency policy information anchors long-term pricing trends, while high-frequency market and sentiment information refine short-term pricing accuracy. They work together to enhance the pricing performance.

Changing discourse of public administration: Where PAD stands?

AbstractPublic administration in the recent years has become a very dynamic and vast domain owing to evolving challenges. Our study aims to comparatively evaluate the changing discourse in public administration research in general and ‘Public Administration and Development’ (PAD) in particular. For this purpose, we have taken research articles from nine journals of public administration domain for the period of 2010–2022. We have used the text‐mining based approach of Dynamic Topic Modelling to understand the evolution of topics in public administration research. This is followed by the Named Entity Recognition (NER) to discover the geographic importance given in public administration research vis‐à‐vis PAD. We have used the measure of ‘Jaccard similarity’ to compare the changing discourse of PAD with respect to other journals. We have found that topics on approaches to governance, organization performance and health were always on the priority in literature. We have also found that topic coverage in PAD is more diverse and niche compared to the overall trends in public administration research. NER findings suggest that public administration research is more Euro‐North American centric while PAD has pan‐continental presence with focus on developing countries.

Research Trends in Taekwondo Demonstrations from 2004 to 2023 Utilizing Topic Modeling

PURPOSE By analyzing trends in Taekwondo demonstrations, specifically in breaking and performances, to date, this study aims to offer timely insights and set the groundwork for future research.METHODS We used Korean abstracts from a total of 425 papers containing the keyword “Taekwondo demonstrations” spanning 20 years from April 2004 to April 2023. We employed Python 3.5.2 to conduct dynamic topic modeling (Latent Dirichlet Analysis, LDA) and to examine the correlation between the topic distribution by section and the publication year.RESULTS The main findings from the LDA are as follows. Topic 1 (10%): “The development of demonstrations: performance in culture and art, ” Topic 2 (11%): “The development of formalized rules and judgments in a demonstration event,” Topic 3 (08%): “A study on the educational courses and professionalism of Taekwondo coaches,” Topic 4 (11%): “Technical movements and kinematic characteristics,” Topic 5 (09%): “A study on marketing perspectives of demonstration performances,” and Topic 7 (33%): “Global exchange: the development and rise of internationalization.” In the correlation analysis between the topic share by section and the publication year, Topics 1 to 5 exhibited no statistically significant correlation. However, Topic 6, “A study on the attainment of events, training, and the psychological factors influencing athletes” and Topic 7, “Global exchange: the development and rise of internationalization,” also displayed a very statistically significant but negative correlation.CONCLUSIONS Future research should focus on studies on the psychological management of athletes during the performance of specific techniques and training methods. Further research considering the global characteristics of Taekwondo may be required.

The Integration of Complex Systems Science and Community-Based Research: A Scoping Review

Complex systems science (CSS) and community-based research (CBR) have emerged over the past 50 years as complementary disciplines. However, there is a gap in understanding what has driven the recent proliferation of integrating these two disciplines to study complex and relevant issues. In this review, we report on the results of a scoping review of articles that utilized both disciplines. After two levels of reviewing articles using DistillerSR, a web-based platform designed to streamline and facilitate the process of conducting systematic reviews, we used two forms of natural language processing to extract data. We developed a novel named entity recognition model to extract descriptive information from the corpus of articles. We also conducted dynamic topic modeling to deductively examine in tandem the development of CSS and CBR and to inductively discover the specific topics that may be driving their use in research and practice. We find that among the CSS and CBR papers, CBR topic frequency has grown at a faster pace than CSS, with CBR using CSS concepts and techniques more often. Four topics that may be driving this trend are collaboration within social systems, business management, food and land use and knowledge, and water shed management. We conclude by discussing the implications of this work for researchers and practitioners who are interested in studying and solving complex social, economic, and health-related issues.

Designing an information system for integrated topic analysis of social media big data

Open communities of users in social media are a source of data that quickly presents the thematic agenda of issues relevant to the population. The indicators of user activity are views, likes, comments and reposts, and they are of a dynamic nature. The article presents a new vision at the topic modeling problems, the results of which are examined for dynamic properties. These data are relevant to solve problems of information support for regional and municipal development. The authors reveal their experience in designing an information system for integrated topic analysis of large open social media data. The system is based on three technologies: building dynamic topic models for monitoring social media, intelligent analysis of topic modeling results; and cognitive visualization of dynamic topic modeling results. To take into account design uncertainty, object modeling tools, system design and a modular approach were used.

신문기사 빅데이터를 활용한 친환경 섬유의 추이에 관한 연구

The development of environmentally friendly products and services has become a trend, and the development and utilization of eco-friendly textiles with economic value is gaining attention as a new business model. Analyzing and identifying trends and developments in eco-friendly textiles can provide important information and insights for various stakeholders such as companies, governments, and consumers to help them achieve sustainable growth. For this study, we collected and analyzed data from newspaper articles mainly covering the textile and fashion sector from 2000 to June 2023. A total of 12,331 articles containing the keyword 'eco-friendly textiles' were collected, and after performing morphological analysis on the extracted data, Latent Dirichlet Allocation and Dynamic Topic Modeling analysis were performed to identify topics by year. The results of the study are expected to provide strategic guidance and insights for the sustainable development of the textile industry, thereby helping to promote the research, development, and commercialization of eco-friendly textiles.