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In this paper we analyze topic evolution over time within bioinformatics to uncover the underlying dynamics of that field, focusing on the recent developments in the 2000s. We select 33 bioinformatics related conferences indexed in DBLP from 2000 to 2011. The major reason for choosing DBLP as the data source instead of PubMed is that DBLP retains most bioinformatics related conferences, and to study dynamics of the field, conference papers are more suitable than journal papers. We divide a period of a dozen years into four periods: period 1 (2000–2002), period 2 (2003–2005), period 3 (2006–2008) and period 4 (2009–2011). To conduct topic evolution analysis, we employ three major procedures, and for each procedure, we develop the following novel technique: the Markov Random Field-based topic clustering, automatic cluster labeling, and topic similarity based on Within-Period Cluster Similarity and Between-Period Cluster Similarity. The experimental results show that there are distinct topic transition patterns between different time periods. From period 1 to period 3, new topics seem to have emerged and expanded, whereas from period 3 to period 4, topics are merged and display more rigorous interaction with each other. This trend is confirmed by the collaboration pattern over time.

Topic modeling approaches, such as latent Dirichlet allocation (LDA) and its successor, the dynamic topic model (DTM), are widely used to identify specific topics by extracting words with similar frequencies from documents. However, these topics often require manual interpretation, which poses challenges in constructing semantics topic evolution, mainly when topics contain negations, synonyms, or rare terms. Neural network-based word embeddings, such as Word2vec and FastText, have advanced semantic understanding but have their limitations. Word2Vec struggles with out-of-vocabulary (OOV) words, and FastText generates suboptimal embeddings for infrequent terms. This study introduces Fast2Vec, a novel model that integrates the semantic capabilities of Word2Vec with the subword analysis strength of FastText to enhance semantic analysis in topic modeling. The model was evaluated using research abstracts from the Science and Technology Index (SINTA) journal database and validated using twelve public word similarity benchmarks, covering diverse semantic and syntactic dimensions. Evaluation metrics include Spearman and Pearson correlation coefficients to assess the alignment with human judgments. Experimental findings demonstrated that Fast2Vec outperforms or closely matches Word2Vec and FastText across most benchmark datasets, particularly in task requiring fine-grained semantic similarity. In OOV scenarios, Fast2Vec improved semantic similarity by 39.64% compared to Word2Vec, and 6.18% compared to FastText. Even in scenarios without OOV terms, Fast2Vec achieved a 7.82% improvement over FastText and a marginal 0.087% improvement over Word2Vec. Additionally, the model effectively categorized topics into four distinct evolution patterns (diffusion, shifting, moderate fluctuations, and stability), enabling a deeper understanding of evolution topic interests and their dynamic characteristics. Fast2Vec presents a robust and generalizable word embedding framework for semantic-based topic modeling. By combining the contextual sensitivity of Word2Vec with the subword flexibility of FastText, Fast2Vec effectively addresses prior limitations in handling OOV terms and semantic variation and demonstrates strong potential for boarder applications in natural language processing tasks.

Interdisciplinary topic reflects the knowledge exchange and integration between different disciplines. Analyzing its evolutionary path is beneficial for interdisciplinary research in identifying potential cooperative research direction and promoting the cross-integration of different disciplines. However, current studies on the evolution of interdisciplinary topics mainly focus on identifying interdisciplinary topics at the macro level. More analysis of the evolution process of interdisciplinary topics at the micro level is still needed. This paper proposes a framework for interdisciplinary topic identification and evolutionary analysis based on BERTopic to bridge the gap. The framework consists of four steps: (1) Extract the topics from the dataset using the BERTopic model. (2) Filter out the invalid global topics and stage topics based on lexical distribution and further filter out the invalid stage topics based on topic correlation. (3) Identify interdisciplinary topics based on disciplinary diversity and disciplinary cohesion. (4) Analyze the interdisciplinary topic evolution by inspecting the intensity and content in the evolution, and visualize the evolution using Sankey diagrams. Finally, We conduct an empirical study on a dataset collected from the Web of Science (WoS) in Library & Information Science (LIS) to evaluate the validity of the framework. From the dataset, we have identified two distinct types of interdisciplinary topics in LIS. Our findings suggest that the growth points of LIS mainly exist in the interdisciplinary research topics. Additionally, our analysis reveals that more and more interdisciplinary knowledge needs to be integrated to solve more complex problems. Mature interdisciplinary topics mainly formed from the internal core knowledge in LIS stimulated by external disciplinary knowledge, while promising interdisciplinary topics are still at the stage of internalizing and absorbing the knowledge of other disciplines. The dataset, the code for implementing the algorithms, and the complete experiment results will be released on GitHub at:  https://github.com/haihua0913/IITE-BERT .

The combination of the topic model and the semantic method can help to discover the semantic distributions of topics and the changing characteristics of the semantic distributions, further providing a new perspective for the research of topic evolution. This study proposes a solution for quantifying the semantic distributions and the changing characteristics based on words in topic evolution through the Dynamic topic model (DTM) and the word2vec model. A dataset in the field of Library and information science (LIS) is utilized in the empirical study, and the topic-semantic probability distribution is derived. The evolving dynamics of the topics are constructed. The characteristics of evolving dynamics are used to explain the semantic distributions of topics in topic evolution. Then, the regularities of evolving dynamics are summarized to explain the changing characteristics of semantic distributions in topic evolution. Results show that no topic is distributed in a single semantic concept, and most topics correspond to various semantic concepts in LIS. The three kinds of topics in LIS are the convergent, diffusive, and stable topics. The discovery of different modes of topic evolution can further prove the development of the field. In addition, findings indicate that the popularity of topics and the characteristics of evolving dynamics of topics are irrelevant.

Comprehensive, in-depth and accurate analyses of patent technology topic evolutions become increasingly significant since the analytical results can offer related personnel the scientific support to explore or trace back to the origin and the development of the technology. However, existing methods of topic evolutions do not facilitate better understanding of how a technology topic has evolved. This paper introduces an integrated method with the LDA topic identification analysis, the improved topic life cycle analysis, and the improved technology entropy analysis for identifying, measuring and interpreting topics evolutions from patent literatures. Multiple indicators we proposed and improved have been used to measure the degree of topic development and identify the topic types of different states. And, the concept of technology entropy has been redefined and improved to measure the changes of evolution intensity and evolution direction among topics, mainly used the topic word and its probability. The results from different methods are mutually connected and complemented. The process and characteristics of topic evolution are further overviewed. Graphene is selected for the case study. The mechanism of evolution and the effect of improved methods are focused on. The research has clearly shown that more accurate and comprehensive results can be achieved for topic evolution by employing this integrated method. Furthermore, the above integration of methods has potential contributions to hot spot detection and potential technology discovery.

Innovative information and communication technologies have reformed higher education from the traditional way to smart learning. Smart learning applies technological and social developments and facilitates effective personalized learning with innovative technologies, especially smart devices and online technologies. Smart learning has attracted increasing research interest from the academia. This study aims to comprehensively review the research field of smart learning by conducting a topic modeling analysis of 555 smart learning publications collected from the Scopus database. In particular, it seeks answers to (1) what the major research topics concerning smart learning were, and (2) how these topics evolved. Results demonstrate several major research issues, for example, Interactive and multimedia learning, STEM (science, technology, engineering, and mathematics) education, Attendance and attention recognition, Blended learning for smart learning, and Affective and biometric computing. Furthermore, several emerging topics were identified, for example, Smart learning analytics, Software engineering for e-learning systems, IoT (Internet of things) and cloud computing, and STEM education. Additionally, potential inter-topic directions were highlighted, for instance, Attendance and attention recognition and IoT and cloud computing, Semantics and ontology and Mobile learning, Feedback and assessment and MOOCs (massive open online courses) and course content management, as well as Blended learning for smart learning and Ecosystem and ambient intelligence.

Investigation of the topic of information literacy and its changes can be informative for researchers and provide a better understanding of the corresponding domains. This study conducted a topic model dynamic analysis of the articles on information literacy studies in the Web of Science core collection database that were published from 2005 to 2019. The global topics and their popularities, topical similarities and correlations, along with the evolution of temporal local topics and the diffusion of subject local topics were analyzed and presented. Nine global topics differed in terms of their temporal and subject characteristics, and this study focused on ability, technology, field, people, place and application of information literacy. For the temporal local topics, crossing was the main evolutionary mechanism; hence, the core topic words were relatively stable, but few new research directions have been explored in recent years. For the subject local topics, absorbing with division and absorbing were the main mechanisms, which supported the diffusion progress of information literacy studies among subjects. However, it is necessary to promote the development of future research through the innovative development of multidisciplinary integration. Researchers and practitioners should focus on the impact of information technology, increase the breadth and depth of the research field, and develop innovative evaluation methods that are based on data to promote the comprehensive, sustainable and effective improvement in information literacy.

Recycling technology for lithium-ion batteries (LIBs) is vital for reducing resource wastage and environmental pollution caused by spent LIBs. Although enterprises and Research and Development (R&D) personnel have worked hard to research and develop related technologies, they have not analyzed the entire process and future development directions of LIB technologies. Therefore, this study proposes a technical topic evolution analysis framework that combines change point detection and natural language processing technology to analyze LIB recycling technology patents. First, the change point detection method is used to quantitatively divide the period of technology development, allowing for an accurate analysis of the evolution process of technology topics. Next, using Latent Dirichlet allocation (LDA), technical topics existing in each period are identified. Furthermore, the Doc2vec model is used to obtain technical topic vectors while calculating the cosine similarity between topic vectors for constructing evolution paths. Finally, a two-dimensional evaluation model is defined to identify future research and development directions of the technology. Using the constructed framework, we systematically trace the dynamic evolution process of LIB recycling technology and further highlight the future development direction of the technology. This study contributes to developing a better understanding of the highly dynamic field of LIB recycling technology, and the framework constructed here can help analyze the evolution of technology-related topics in other fields.

Microblog is a popular and open platform for discovering and sharing the latest news about social issues and daily life. The quickly-updated microblog streams make it urgent to develop an effective tool to monitor such streams. Emerging topic tracking is one of such tools to reveal what new events are attracting the most online attention at present. However, due to the fast changing, high noise and short length of the microblog feeds, two challenges should be addressed in emerging topic tracking. One is the problem of detecting emerging topics early, long before they become hot, and the other is how to effectively monitor evolving topics over time. In this study, we propose a novel emerging topics tracking method, which aligns emerging word detection from temporal perspective with coherent topic mining from spatial perspective. Specifically, we first design a metric to estimate word novelty and fading based on local weighted linear regression (LWLR), which can highlight the word novelty of expressing an emerging topic and suppress the word novelty of expressing an existing topic. We then track emerging topics by leveraging topic novelty and fading probabilities, which are learnt by designing and solving an optimization problem. We evaluate our method on a microblog stream containing over one million feeds. Experimental results show the promising performance of the proposed method in detecting emerging topic and tracking topic evolution over time on both effectiveness and efficiency.

The study of topic evolution aims to analyze the behavior of different research fields by utilizing various features such as the relationships between articles. In recent years, many published papers consider more than one field of study which has led to a significant increase in the number of inter-field and interdisciplinary articles. Therefore, we can analyze the similarity/dissimilarity and convergence/divergence of research fields based on topic analysis of the published papers. Our research intends to create a methodology for studying the evolution of the research fields. In this paper, we propose an embedding approach for modeling each research topics as a multidimensional vector. Using this model, we measure the topic’s distances over the years and investigate how topics evolve over time. The proposed similarity metric showed many advantages over other alternatives (such as Jaccard similarity) and it resulted in better stability and accuracy. As a case study, we applied the proposed method to subsets of computer science for experimental purposes, and the results were quite comprehensible and coherent.