Topic Distribution Research Articles

A popular topic detection method based on microblog images and short text information

Popular topic detection is a topic identification by the information of documents posted by users in social networking platforms. In a large body of research literature, most popular topic detection methods identify the distribution of unknown topics by integrating information from documents based on social networking platforms. However, among these popular topic detection methods, most of them have a low accuracy in topic detection due to the short text content and the abundance of useless punctuation marks and emoticons. Image information in short texts has also been overlooked, while this information may contain the real topic matter of the user's posted content. In order to solve the above problems and improve the quality of topic detection, this paper proposes a popular topic detection method based on microblog images and short text information. The method uses an image description model to obtain more information about short texts, identifies hot words by a new word discovery algorithm in the preprocessing stage, and uses a PTM model to improve the quality and effectiveness of topic detection during topic detection and aggregation. The experimental results show that the topic detection method in this paper improves the values of evaluation indicators compared with the other three topic detection methods. In conclusion, the popular topic detection method proposed in this paper can improve the performance of topic detection by integrating microblog images and short text information, and outperforms other topic detection methods selected in this paper.

Incidence, Diversity, and Quality of Publications in Orthopaedic Trauma Literature

Introduction: Modern medicine increasingly relies on evidence-based care, using academic journals as vehicles for information dissemination. Increasing rates of publication have led to an overwhelming volume of literature. This study evaluates the proportion of dedicated orthopaedic trauma literature, the frequency of different topic domains, and the quality of currently published information. Methods: Top orthopaedic journals were selected by impact factor and scientific articles published between 2016 –2019 were evaluated. All articles were categorized by subspecialty and pre-defined topic domains and level of evidence was recorded. Results: 3,697 articles were published in Journal of Orthopaedic Trauma (JOT), Clinical Orthopaedics and Related Research (CORR), the Journal of Bone and Joint Surgery (JBJS), and Bone and Joint Journal (BJJ). 32% of articles were dedicated to orthopaedic trauma topics. Joint Arthroplasty was the most published subspecialty in general journals (35.37%, P<0.002). Hip fractures were the most prevalent trauma topic domain in all four journals (15.5%, P<0.002). Pelvic ring injuries were the second most prevalent topic in orthopaedic trauma journals, versus distal radius fractures in non-trauma journals (P<0.05). 48.4% of published literature was level III evidence, with no differences between trauma and non-trauma publications (P>0.05). Conclusion: Orthopaedic trauma is well represented within the orthopaedic literature with a diverse distribution of topics over the four-year period, second only to Joint Arthroplasty which was by far the most common topic in general orthopaedic journals. Level of Evidence in trauma literature parallel other orthopaedic subspecialties, but there is an overall low proportion of level I and II studies in the orthopaedic literature. Level of Evidence: Level II

Do thematic funding instruments lead researchers in new directions? Strategic funding priorities and topic switching among British grant recipients

Abstract Research agencies increasingly use thematic funding schemes to steer scientists toward issues of political interest. Funders set strategic priorities and expect scientists to follow the money. Using statistical-matching methods, we paired 10,475 UK-based recipients of thematic grants (with aims proposed by the funding agency) and responsive-mode grants (with aims proposed by researchers) in Physics, Engineering, and Bioscience to investigate the relation between funding-type and shifts in scientists’ research interests. We applied citation-based community detection to create individual time-series of topic distributions based on the grantees’ publications, and used these to estimate funding-related shifts in research focus. Our analysis, which estimates the similarity of the grantees’ research focus before, during and after a grant, suggests that scientists acquiring thematic funding alter their research interests more than comparable scientists funded through responsive-mode schemes. However, recipients of both types of grants tend to revert toward their original research interests when the funding expires. We find no evidence that thematic funding increases the level of diversity in a recipient’s research portfolio. Our study thus highlights an ambiguous link between thematic funding mechanisms and researchers’ orientations.

Systematic review on fingerprinting development to determine adulteration of Chinese herbal medicines

BackgroundIt has been a current research hospots using fingerprinting technology for quality control of Chinese herbal medicines (CHMs), which provides a scientific basis for establishment of overall quality control in accordance with the characteristics of CHMs. The fingerprinting technology for CHMs is diverse, and the research field covers many disciplines, such as analytical chemistry, pharmacology, pharmaceutics, biochemistry, and molecular biology. PurposeTo effectively understand the key areas and future directions of research regarding the fingerprint and adulteration of CHMs. Methods/resultsthis paper analyzed 879 articles in this field in the Web of Science Core Collection from 2000 to 2023 with CiteSpace and VOSviewer, and systematically assessed the research process, hotspots, topic distribution among disciplines, etc. The most prominent contributors of fingerprint and adulteration of CHMs research are mainly from China, India, the United States, England, and Brazil. The knowledge domains of fingerprint and adulteration of CHMs research focus mainly on the topics of molecular authentication, DNA barcoding, HPLC, near-infrared spectroscopy, manage data, chemometrics, and electrochemical fingerprinting. Most countries have recognized the pharmaceutical potential of natural products, and have paid more attention to the fingerprint and adulteration of CHMs in the past decade. Future the research tends to focus more on molecular identification and authentication, and electrochemical and chromatographic fingerprinting in controlling the adulteration of CHMs. ConclusionThis research provides a valuable reference for scholars in related fields to analyze existing research results, understand the development trend, and explore new research directions.

Quality assessment of paediatric education and research in southwest China: A cross-sectional study.

Currently, there is a limited analysis of medical education research both domestically and internationally. To enhance, improve the quality of medical education, this study conducted a quantitative analysis of teaching project data from an affiliated hospital during the period 2016-2022. The results indicated that a total of 133 teaching projects were initiated during this period, with an average age of project leaders being 42.73±6.45 years. Regarding professional ranks and titles, municipal-level project leaders had a high concentration of seniors (48.15%), while at the university-level, most project leaders held the title of deputy seniors (58.82%). At the university-level, project leaders were mainly distributed between deputy senior titles (37.08%) and intermediate titles (38.20%). In terms of research content, nearly half of the studies (46.62%) focused on teaching methods and models. Further regression analysis revealed that professional ranks and titles were an independent factor influencing the project level (P<0. 05). These findings suggest the need for improvement in medical education research, including addressing the uneven distribution of research topics, enhancing the research capacity of junior and mid-career medical education teachers, and improving the dissemination of research results.

Topic-Aware Information Coverage Maximization in Social Networks

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Influence maximization</i> (IM) aims to identify a set of nodes <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S$</tex-math> </inline-formula> to maximize the expected number of nodes influenced during the information propagation starting from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S$</tex-math> </inline-formula> . Some works had extended this problem to be <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">topic-aware</i> , where each node is associated with a topic distribution and tends to be activated with different probabilities by different topics. However, whether it is topic-aware or not, IM problem only focuses on the active nodes and overlooks all the inactive ones. Actually, an inactive node may receive the information from their active in-neighbors and become informed. Therefore, this type of nodes should also be considered when measuring the coverage of information propagation. Inspired by this, we formulate a new problem called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">topic-aware information coverage maximization</i> (TAICM), which aims to maximize the sum of the expected number of both active and informed nodes in topic-aware social networks. Then we devise a heuristic method to solve it. Experiments on three real-world datasets demonstrate that our method can achieve similar or higher information coverage in much less or at least acceptable time than some commonly used IM algorithms.

Evolution of renewable energy laws and policies in China

This study employs Latent Dirichlet Allocation (LDA) topic modelling methodology to analyze documents related to renewable energy laws and policies at the central level in China. The objective is to investigate the development and evolution of renewable energy policies in China and to gain insights into the national-level attitudes towards renewable energy development. The study consists of two phases: initially, renewable energy policy documents undergo keyword analysis using word clouds and keyword co-occurrence network analysis to elucidate the focal areas and their interconnections within the legal and policy texts. Subsequently, after determining the optimal number of topics for modelling based on topic perplexity and consistency results, the text undergoes data cleaning to isolate words with practical significance. These words are then incorporated into the LDA topic model to analyze the distribution and content of potential topics within the policies. Lastly, by linearly segmenting the time frame, changes in topic intensity over time are visually examined using heat maps. The findings indicate that energy policies have consistently prioritized “development” and emphasized the significance of “new energy” in renewable energy policies. Moreover, as renewable energy has progressed, governments and policymakers have come to acknowledge the importance of comprehensive energy planning, transitioning to clean energy sources, and regulating the electricity market. This growing awareness has led to efforts to strengthen policy and regulatory measures to foster renewable energy's sustainable development and utilization. In summary, this study highlights the effectiveness of the LDA topic model in analyzing renewable energy policies, advancing its adoption and furthering research in the field.

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.

Bibliometric and visualized analysis of DME from 2012 to 2022.

Diabetic macular edema (DME) is the main cause of irreversible vision loss in patients with diabetes mellitus (DM), resulting in a certain burden to patients and society. With the increasing incidence of DME, more and more researchers are focusing on it. The papers related to DME between 2012 and 2022 from the Web of Science core Collection were searched in this study. Based on CiteSpace and VOS viewer, these publications were analyzed in terms of spatiotemporal distribution, author distribution, subject classification, topic distribution, and citations. A total of 5165 publications on DME were included. The results showed that the research on DME is on a steady growth trend. The country with the highest number of published documents was the US. Wong Tien Yin from Tsinghua University was the author with the most published articles. The journal of Retina, the Journal of Retinal and Vitreous Diseases had a large number of publications. The article "Mechanisms of macular edema: Beyond the surface" was the highly cited literature and "Aflibercept, bevacizumab, or ranibizumab for diabetic macular edema" had the highest co-citation frequency. The treatment, diagnosis, pathogenesis, as well as etiology and epidemiological investigation of DME, have been the current research direction. Deep learning has been widely used in the medical field for its strong feature representation ability. The study revealed the important authoritative literature, journals, institutions, scholars, countries, research hotspots, and development trends in in the field of DME. This indicates that communication and cooperation between disciplines, universities, and countries are crucial. It can advance research in DME and even ophthalmology.

Chinese is a discourse-configurational language: Miyagawa’s typology revisited

Assuming the Strong Uniformity Principle, Miyagawa recently proposed a typology of languages based on distinct patterns of Feature Inheritance at Phase C. In this typology, Chinese is identified as a Category II language (with the δ-feature on C and the φ-feature on T). That is, Chinese is regarded as a non-discourse-configurational language similar to English. This paper aims to review Miyagawa’s discussion of Chinese and argue for its reclassification as a Category III language, that is, a discourse-configurational language like Spanish. I contend that Miyagawa’s discussion of Chinese only pertains to Aboutness-shift topic, a type of topic base-generated in the CP domain across languages; as a result, his conclusions are unsustainable. On the other hand, I show that phenomena like Object Preposing, forced A-focalization, and the distribution of Familiar and Contrastive topics in non-assertive embedded clauses provide compelling evidence that the δ-feature has lowered to T in Chinese. Finally, I address a potential counterargument presented by the Chinese wh-adverbial zenme ‘how come’. I argue that zenme is not base-generated in the CP periphery, but rather inserted in the TP layer.

Machine Learning Applied to Water Distribution Networks Issues: A Bibliometric Review

INTRODUCTION: Water Distribution Networks are critical infrastructures that have garnered increasing interest from researchers.&#x0D; OBJECTIVES: This article conducts a bibliometric analysis to examine trends, the geographical distribution of researchers, hot topics, and international cooperation in using Machine Learning for Water Distribution Networks over the past decade.&#x0D; METHODS: Using “water distribution” AND (prediction OR “Machine learning” OR “ML” OR detection OR simulation), as search string, 4859 relevant publications have been retrieved from WoS database. After applying the PRISMA method, we retained 2427 documents for analysis with a Bibliometric library programmed in R.&#x0D; RESULTS: China and the USA are the most productive on the ground, and only one African country appears in this ranking in 14th place. We also identified two ways for future research works, which are: the assessment of water quality and the design of optimisation models.&#x0D; CONCLUSION: The application of this research in African countries would be fascinating for a better quality of service and efficient management of this resource, which is inaccessible to many African countries.

Multilingual topic evolution and comparative analysis for electronic commerce research: A combination of BERTopic and SBERT

Electronic commerce (EC) has become the most critical business activity in the world. China has become the world’s largest market for EC. Over the past three decades, numerous researches have examined the current status of the development of monolingual EC research in specific scenarios. However, the paradigm shift in EC development through the analysis of the dynamic evolution of semantic information has not yet been examined, and the distinctions and connections between multilingual EC studies have not yet been established. This study analyzed 16,207 English and 17,850 Chinese EC-related articles from the Web of Science database and CNKI by combining the BERTopic topic model and SBERT sentence embedding-based similarity computations. The results reveal the distributions of global and local topics in the English and Chinese EC literature, analyze the semantic intricacies of topic convergence and evolution across continuous time, as well as the distinctions and connections between English and Chinese topics. Finally, the evolutionary patterns and life cycle of three crucial English and Chinese topics are explored respectively, including their emergence, development, maturity, and decline. Overall, this study provides a comprehensive overview of EC studies from a topic perspective.

Topic models indicate textual aboutness and pragmatics: Valuation practices in Islamophobic discourse

This study investigates the analysis of evaluative linguistic patterns within the context of the German-language Islamophobic online blog PI-News. The corpus consists of texts that refer to the Quran. The innovation of this research lies in two primary dimensions: Firstly, it adopts John Dewey’s pragmatism to establish an explicit valuation theory. Secondly, it extends the boundaries of topic modeling in the digital humanities by integrating van Dijk’s differentiation between aboutness and pragmatic macrostructures of texts. Notably, within these pragmatic macrostructures, the study highlights the practice of quoting Quran suras as a significant linguistic pattern. Together with the analysis of topic distribution and topic pathways in texts, the paper illustrates how evaluative practices reinforce the ideal of an Islam-free Europe.

Unsupervised question-retrieval approach based on topic keywords filtering and multi-task learning

Currently, the majority of retrieval-based question-answering systems depend on supervised training using question pairs. However, there is still a significant need for further exploration of how to employ unsupervised methods to improve the accuracy of retrieval-based question-answering systems. From the perspective of question topic keywords, this paper presents TFCSG, an unsupervised question-retrieval approach based on topic keyword filtering and multi-task learning. Firstly, we design the topic keyword filtering algorithm, which, unlike the topic model, can sequentially filter out the keywords of the question and can provide a training corpus for subsequent unsupervised learning. Then, three tasks are designed in this paper to complete the training of the question-retrieval model. The first task is a question contrastive learning task based on topic keywords repetition strategy, the second is questions and its corresponding sequential topic keywords similarity distribution task, and the third is a sequential topic keywords generation task using questions. These three tasks are trained in parallel in order to obtain quality question representations and thus improve the accuracy of question-retrieval task. Finally, our experimental results on the four publicly available datasets demonstrate the effectiveness of the TFCSG, with an average improvement of 7.1%, 4.4%, and 3.5% in the P@1, MAP, and MRR metrics when using the BERT model compared to the baseline model. The corresponding metrics improved by 5.7%, 3.5% and 3.0% on average when using the RoBERTa model. The accuracy of unsupervised similar question-retrieval task is effectively improved. In particular, the values of P@1, P@5, and P@10 are close, the retrieved similar questions are ranked more advance.

Short text classification using semantically enriched topic model

Modelling short text is challenging due to the small number of word co-occurrence and insufficient semantic information that affects downstream Natural Language Processing (NLP) tasks, for example, text classification. Gathering information from external sources is expensive and may increase noise. For efficient short text classification without depending on external knowledge sources, we propose Expressive Short text Classification (EStC). EStC consists of a novel document context-aware semantically enriched topic model called the Short text Topic Model (StTM) that captures words, topics and documents semantics in a joint learning framework. In StTM, the probability of predicting a context word involves the topic distribution of word embeddings and the document vector as the global context, which obtains by weighted averaging of word embeddings on the fly simultaneously with the topic distribution of words without requiring an additional inference method for the document embedding. EStC represents documents in an expressive (number of topics × number of word embedding features) embedding space and uses a linear support vector machine (SVM) classifier for their classification. Experimental results demonstrate that EStC outperforms many state-of-the-art language models in short text classification using several publicly available short text data sets.

Analysis of Mixed-Format Assessments Using Measurement Models and Topic Modeling

ABSTRACT It is common to find mixed-format data results from the use of both multiple-choice (MC) and constructed-response (CR) questions on assessments. Dealing with these mixed response types involves understanding what the assessment is measuring, and the use of suitable measurement models to estimate latent abilities. Past research in educational measurement, however, has often overlooked the written responses in CR items after analyzing the response scores. This study presents a method for bridging this gap by using a topic model called latent Dirichlet allocation to uncover the structure in written answers and then use that information to augment results from traditional measurement models. In this study, a five-step framework is employed for assessing both the examinees’ latent abilities and the internal structure of the written responses obtained from mixed-format assessments. An empirical dataset obtained from Grade 8 examinees in a southeastern state on an English language arts assessment is used for illustration. Based on the dimensionality of the assessment, a unidimensional partial credit model and two multidimensional bi-factor models were fit to the data. A comparison of results from these analyses suggests that a constrained bi-factor model was most useful for detecting the mixed-format score patterns, and that a 5-topic model was determined for the textual responses. The topic distributions were found to be related to the latent abilities estimated from the constrained bi-factor model. This framework combines both the score patterns and textual responses, and highlights the utility of combining the traditional response analysis with a start-of-art language model.

Growth, trends and prevalent topics in geotechnical engineering (1998–2022) using topic modelling

Data gathered from 65 922 articles published in 30 geotechnical engineering journals between 1998 and 2022 were used to build a topic model and study the evolution of research output in the field. Over this period, the number of journal publications has grown exponentially with the number of articles published per year doubling every 6·3 years. The average citations per article (32·7) are skewed by highly cited articles, which make up 7·8% of the articles but account for 34·5 of the total number of citations. The articles’ country of origin has become increasingly centralised, with nearly half the articles published between 2018 and 2022 coming from China (39%) and the United States (10%). The topic distribution in the field has become highly specialised, with emerging topics being fuelled by: (a) new technologies and methods (i.e. data science and sensing techniques – the fastest-growing topics), and (b) rising applications seemingly related to mitigation (e.g. offshore and energy geotechnics) and adaptation (e.g. geohazards, mass movements) to climate change. As a consequence, traditional topics (e.g. soil and rock mechanics) have reduced their share in the field, but still remain among the most cited topics in geotechnical engineering.

Curiosity in news consumption

AbstractWe analyze how curiosity drives news consumption. We test predictions of the information‐gap theory of curiosity using over 100,000 WeChat news articles, applying NLP methods to construct measures of salience, importance, and surprisingness associated with news headlines, experimentally validating these measures, and using them to predict clicks. Our findings confirm that people tend to consume news when: the headline sparks a salient question; the content appears more important (e.g., emphasized by the headline's position on the webpage or an exclamation mark); the headline refers to more surprising topics (measured as the KL‐divergence from a baseline topic distribution); and the headline has lower valence. Information‐gap theory helps predict aggregate news consumption. Yet our data also reveal a small negative correlation between the number of clicks and the ratio of likes to clicks, suggesting that while inducing curiosity can drive short‐term news consumption, it doesn't necessarily enhance long‐term reader engagement.

Structural-topic aware deep neural networks for information cascade prediction.

It is critical to accurately predict the future popularity of information cascades for many related applications, such as online opinion warning or academic influence evaluation. Despite many efforts devoted to developing effective prediction approaches, especially the recent presence of deep learning-based model, the structural information of the cascade network is ignored. Thus, to make use of the structural information in cascade prediction task, we propose a structural-topic aware deep neural networks (STDNN), which firstly learns the structure topic distribution of each node in the cascade, feeds it to a sequential neural network, and finally predicts the future popularity of the cascades. It can inherit the high interpretability of Hawkes process and possesses the high predictive power of deep learning methods, bridging the gap between prediction and understanding of information cascades by capturing indicative graph structures. We evaluate our model through quantitative experiments, where our model exhibits promising performance, efficiency higher than the baselines.

An Aspect-Based Review Analysis Using ChatGPT for the Exploration of Hotel Service Failures

In this study, we employed ChatGPT, an advanced large language model, to analyze hotel reviews, focusing on aspect-based feedback to understand service failures in the hospitality industry. The shift from traditional feedback analysis methods to natural language processing (NLP) was initially hindered by the complexity and ambiguity of hotel review texts. However, the emergence of ChatGPT marks a significant breakthrough, offering enhanced accuracy and context-aware analysis. This study presents a novel approach to analyzing aspect-based hotel complaint reviews using ChatGPT. Employing a dataset from TripAdvisor, we methodically identified ten hotel attributes, establishing aspect–summarization pairs for each. Customized prompts facilitated ChatGPT’s efficient review summarization, emphasizing explicit keyword extraction for detailed analysis. A qualitative evaluation of ChatGPT’s outputs demonstrates its effectiveness in succinctly capturing crucial information, particularly through the explicitation of key terms relevant to each attribute. This study further delves into topic distributions across various hotel market segments (budget, midrange, and luxury), using explicit keyword analysis for the topic modeling of each hotel attribute. This comprehensive approach using ChatGPT for aspect-based summarization demonstrates a significant advancement in the way hotel reviews can be analyzed, offering deeper insights into customer experiences and perceptions.