Analyzing the generalizability of the network-based topic emergence identification method

Topic evolution helps the understanding of current research topics and their histories by automatically modeling and detecting the set of shared research fields in academic publications as topics. This paper provides a generalized analysis of the topic evolution method for predicting the emergence of new topics, which can operate on any dataset where the topics are defined as the relationships of their neighborhoods in the past by extrapolating to the future topics. Twenty sample topic networks were built with various fields-of-study keywords as seeds, covering domains such as business, materials, diseases, and computer science from the Microsoft Academic Graph dataset. The binary classifier was trained for each topic network using 15 structural features of emerging and existing topics and consistently resulted in accuracy and F1 over 0.91 for all twenty datasets over the periods of 2000 to 2019. Feature selection showed that the models retained most of the performance with only one-third of the tested features. Incremental learning was tested within the same topic over time and between different topics, which resulted in slight performance improvements in both cases. This indicates there is an underlying pattern to the neighbors of new topics common to research domains, likely beyond the sample topics used in the experiment. The result showed that network-based new topic prediction can be applied to various research domains with different research patterns.

Online Latent Dirichlet Allocation Model Based on Sentiment Polarity Time Series

The Product Sensitive Online Dirichlet Allocation model (PSOLDA) proposed in this paper mainly uses the sentiment polarity of topic words in the review text to improve the accuracy of topic evolution. First, we use Latent Dirichlet Allocation (LDA) to obtain the distribution of topic words in the current time window. Second, the word2vec word vector is used as auxiliary information to determine the sentiment polarity and obtain the sentiment polarity distribution of the current topic. Finally, the sentiment polarity changes of the topics in the previous and next time window are mapped to the sentiment factors, and the distribution of topic words in the next time window is controlled through them. The experimental results show that the PSOLDA model decreases the probability distribution by 0.160 1, while Online Twitter LDA only increases by 0.069 9. The topic evolution method that integrates the sentimental information of topic words proposed in this paper is better than the traditional model.

Evaluating the cohesion of municipalities’ discourse during the COVID-19 pandemic

Social media has been used as a method to alert and raise awareness among the population to help fight the COVID-19 pandemic. We argue that the discourse of municipalities and their respective mayors may have an influence on the behavior of the population and thus directly impact COVID-19 outcomes. This paper analyzes the diversity and cohesion of these discourses through posts published on Facebook, evaluating (i) diversity of topics discussed, (ii) topic evolution, and (iii) deviation from a central discourse. We also combine this information with epidemiological data to assess impact in the outcomes. In particular, we present two different Latent Dirichlet allocation (LDA) models to analyze how topics are being discussed by municipalities/mayors and compare how cohesion is related to the evolution of the pandemic. Our initial analysis suggests that municipalities tend to employ a unified discourse as a response to the worsening of epidemic outcomes. The results of our study could help to inform governments of better communication strategies in this and future health crisis.

Exploring the Development History and Research Tendency of Medical Informatics: Based on Topic Evolution Analysis (Preprint)

BACKGROUND Medical informatics has become a discipline that attracted researchers worldwide. It’s necessary to understand the development of its research hotspots and the future research trend. OBJECTIVE This research aimed to explore the evolution of research topics in medical informatics by analyzing relevant research articles published from 1964 to 2020. METHODS We collected research articles from 27 representative medical informatics journals indexed by the Web of Science Core Collection. The research topics of medical informatics were extracted based on LDA model and the topic evolution patterns were analyzed based on similarities between research topics. RESULTS A total of 56466 publications were identified. We found that medical informatics was in a period of rapid development. Health data analysis and health behavior intervention were the research hotspots all the time. While in recent years, the application of emerging computer technologies and mobile health tools attracted more research interests. CONCLUSIONS Our study provided a comprehensive understanding of the research hotspots and the evolution pattern among them in medical informatics, which was helpful for researchers to grasp research trends and design their studies.

LIFE SCIENCES TEACHERS’ CONCEPTIONS ABOUT SOCIOSCIENTIFIC ISSUES IN THE TOPIC EVOLUTION

The goal of science education is emphatically positioned on promoting science literacy. The rationale is learners should not only learn about scientific knowledge and processes but also on how to apply the knowledge when making decisions about heterogenous societal and personal issues. Previous research has indicated that by addressing socioscientific issues (SSIs) when teaching controversial science topics, it provides a suitable context for developing scientific literacy in learners. Scientifically literate learners are well-informed citizens with regards to the social, ethical, economic, and political issues impacting on contemporary society. The theory of evolution is one such Life Sciences topic deeply embedded with SSIs. Teachers are conflicted when teaching this topic due to the controversy surrounding the theory as they view the teaching of evolution as a way of negating the legitimacy of their religious and cultural convictions. It is against this background that the study sought to answer the research question: How do Life Sciences teachers conceptualise socioscientific issues embedded in the topic evolution? In an explanatory mixed method approach, a questionnaire with both quantitative and qualitative questions was administered to 28 randomly selected grade 12 Life Sciences teachers. Data was analysed and descriptive statistics were obtained, and themes generated. The findings showed that all the participants were knowledgeable about the SSIs embedded in the topic evolution. In justifying their conceptions 61% of the teachers perceived SSIs as important in improving learners’ reasoning and argumentative skills; developing learners’ critical thinking skills; and in informing learners in decision making. There were however 11% of the teachers who pointed out that SSIs as too sensitive to deal with hence not suitable to teach young learners. Though the teachers were knowledgeable about the SSIs embedded in the theory of evolution, it does not mean that they could address them when teaching the various concepts of evolution. The main source of the controversy rose from the evolution of humankind versus the Christian belief in the six-day special creation. The participants (25%) indicated that evolution challenges peoples’ religious and cultural convictions, which conflicts both the teachers and learners to question or go against their religious beliefs. Several teachers pointed out that some of the concepts on evolution such as ‘living organisms share common ancestry (18%) and ‘the formation of new species from existing species’ (11%), undermine the superiority of human beings over other organisms. The findings have implications for both pre-and in-service teacher professional development.

Discovery and Topic Evolution Analysis of CRISPR/Cas9 Gene Editing Technology in Biomedical Field:Internet-based Survey (Preprint)

BACKGROUND In 2020, the discoverers of CRISPR/Cas9 technology, Jennifer A. Doudna and Emmanuelle Charpentier, were awarded the Nobel Prize in Chemistry. This technology has revolutionized the field of biomedical science since its birth and contributed to the treatment of a variety of complex diseases. Scholars have carried out many studies on the development trend of CRISPR technology, bibliometrics, visualization and other aspects. However, there are few studies on the hot spot analysis and topic changes in the biomedical field, which is not conducive to scholars' thinking. OBJECTIVE This study aimed to:(1) mining and analyzing the research hotspots of CRISPR/Cas9 system in each stage; (2) sorting out and visualizing the evolution direction of the hot research topics of CRISPR/Cas9 gene editing technology in the past decade; (3) exploring the hidden research hotspots of CRISPR/Cas9 gene editing technology, so as to provide reference for further research METHODS In this study, research papers related to CRISPR/Cas9 gene editing technology in biomedical field published on the Web of Science core datasets from 2010 to 2020 were used; literature research method, analysis method and expert consultation method and other methods were comprehensively utilized; CiteSpace software and VOS viewer software were selected as visualization tools to analyze hot topics and topic evolution in research. RESULTS The generated co-occurrence map and cluster map were analyzed by keyword analysis strategy to determine the hot topics in each development stage. The topic evolution types and evolution paths are studied based on the categories and keywords of hot topics in each stage and the characteristics of their advancement along the time axis, and the topic evolution diagram and evolution path diagram were presented respectively. CONCLUSIONS Based on the above research results, firstly, in recent years, scholars have attached great importance to the research on this technology in the field of biomedical science, and the research topics have been deepened and enriched. Secondly, the development of CRISPR/Cas9 system shows different hot research directions in different periods, which is in line with the development of The Times. Finally, the theme evolution of CRISPR/Cas9 in the field of biomedicine generally shows a trend from single to multiple, and from theory to practice. Hidden research hotspots focus on the detection and treatment of infectious diseases, aging mitigation, drug resistance and other common diseases.