Impactful scientists have higher tendency to involve collaborators in new topics.

In scientific research, collaboration is one of the most effective ways to take advantage of new ideas, skills, and resources and for performing interdisciplinary research. Although collaboration networks have been intensively studied, the question of how individual scientists choose collaborators to study a new research topic remains almost unexplored. Here, we investigate the statistics and mechanisms of collaborations of individual scientists along their careers, revealing that, in general, collaborators are involved in significantly fewer topics than expected from a controlled surrogate. In particular, we find that highly productive scientists tend to have a higher fraction of single-topic collaborators, while highly cited-i.e., impactful-scientists have a higher fraction of multitopic collaborators. We also suggest a plausible mechanism for this distinction. Moreover, we investigate the cases where scientists involve existing collaborators in a new topic. We find that, compared to productive scientists, impactful scientists show strong preference of collaboration with high-impact scientists on a new topic. Finally, we validate our findings by investigating active scientists in different years and across different disciplines.

Global research landscape on the contribution of de novo mutations to human genetic diseases over the past 20 years: bibliometric analysis.

As the contribution of de novo mutations (DNMs) to human genetic diseases has been gradually uncovered, analyzing the global research landscape over the past 20 years is essential. Because of the large and rapidly increasing number of publications in this field, understanding the current landscape of the contribution of DNMs in the human genome to genetic diseases remains a challenge. Bibliometric analysis provides an approach for visualizing these studies using information in published records in a specific field. This study aimed to illustrate the current global research status and explore trends in the field of DNMs underlying genetic diseases. Bibliometric analyses were performed using the Bibliometrix Package based on the R language version 4.1.3 and CiteSpace version 6.1.R2 software for publications from 2000 to 2021 indexed under the Web of Science Core Collection (WoSCC) about DNMs underlying genetic diseases on 17 September 2022. We identified 3435 records, which were published in 731 journals by 26,538 authors from 6052 institutes in 66 countries. There was an upward trend in the number of publications since 2013. The USA, China, and Germany contributed the majority of the records included. The University of Washington, Columbia University, and Baylor College of Medicine were the top-producing institutions. Evan E Eichler of the University of Washington, Stephan J Sanders of the Yale University School of Medicine, and Ingrid E Scheffer of the University of Melbourne were the most high-ranked authors. Keyword co-occurrence analysis suggested that DNMs in neurodevelopmental disorders and intellectual disabilities were research hotspots and trends. In conclusion, our data show that DNMs have a significant effect on human genetic diseases, with a noticeable increase in annual publications over the last 5 years. Furthermore, potential hotspots are shifting toward understanding the causative role and clinical interpretation of newly identified or low-frequency DNMs observed in patients.

Tracking Openness and Topic Evolution of COVID-19 Publications January 2020-March 2021: Comprehensive Bibliometric and Topic Modeling Analysis.

BACKGROUND: The COVID-19 outbreak highlighted the importance of rapid access to research. OBJECTIVE: The aim of this study was to investigate research communication related to COVID-19, the level of openness of papers, and the main topics of research into this disease. METHODS: Open access (OA) uptake (typologies, license use) and the topic evolution of publications were analyzed from the start of the pandemic (January 1, 2020) until the end of a year of widespread lockdown (March 1, 2021). RESULTS: The sample included 95,605 publications; 94.1% were published in an OA form, 44% of which were published as Bronze OA. Among these OA publications, 42% do not have a license, which can limit the number of citations and thus the impact. Using a topic modeling approach, we found that articles in Hybrid and Green OA publications are more focused on patients and their effects, whereas the strategy to combat the pandemic adopted by different countries was the main topic of articles selecting publication via the Gold OA route. CONCLUSIONS: Although OA scientific production has increased, some weaknesses in OA practice, such as lack of licensing or under-researched topics, still hold back its effective use for further research.

Ethical and Methodological Considerations of Twitter Data for Public Health Research: Systematic Review.

BACKGROUND: Much research is being carried out using publicly available Twitter data in the field of public health, but the types of research questions that these data are being used to answer and the extent to which these projects require ethical oversight are not clear. OBJECTIVE: This review describes the current state of public health research using Twitter data in terms of methods and research questions, geographic focus, and ethical considerations including obtaining informed consent from Twitter handlers. METHODS: We implemented a systematic review, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, of articles published between January 2006 and October 31, 2019, using Twitter data in secondary analyses for public health research, which were found using standardized search criteria on SocINDEX, PsycINFO, and PubMed. Studies were excluded when using Twitter for primary data collection, such as for study recruitment or as part of a dissemination intervention. RESULTS: We identified 367 articles that met eligibility criteria. Infectious disease (n=80, 22%) and substance use (n=66, 18%) were the most common topics for these studies, and sentiment mining (n=227, 62%), surveillance (n=224, 61%), and thematic exploration (n=217, 59%) were the most common methodologies employed. Approximately one-third of articles had a global or worldwide geographic focus; another one-third focused on the United States. The majority (n=222, 60%) of articles used a native Twitter application programming interface, and a significant amount of the remainder (n=102, 28%) used a third-party application programming interface. Only one-third (n=119, 32%) of studies sought ethical approval from an institutional review board, while 17% of them (n=62) included identifying information on Twitter users or tweets and 36% of them (n=131) attempted to anonymize identifiers. Most studies (n=272, 79%) included a discussion on the validity of the measures and reliability of coding (70% for interreliability of human coding and 70% for computer algorithm checks), but less attention was paid to the sampling frame, and what underlying population the sample represented. CONCLUSIONS: Twitter data may be useful in public health research, given its access to publicly available information. However, studies should exercise greater caution in considering the data sources, accession method, and external validity of the sampling frame. Further, an ethical framework is necessary to help guide future research in this area, especially when individual, identifiable Twitter users and tweets are shared and discussed. TRIAL REGISTRATION: PROSPERO CRD42020148170; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=148170.

The landscape of eating disorders research: A 40-year bibliometric analysis.

OBJECTIVE: Employing bibliometric methods, the present study aimed to map out the general landscape of existing research on eating disorders (EDs) over the past decades. METHOD: Using the Web of Science database, we retrieved 41,917 research articles related to EDs published from 1981 to 2020. After removing those without an abstract, a total of 37,446 articles were retained. The study outlined the distribution of scholarship by time, languages, regions, and countries, and identified major research lines by applying latent topic modelling. RESULTS: Results revealed a general increasing trend in the number of publications on EDs research, and researchers from Western countries dominated the production of related scholarship. The distribution of published scholarship varied significantly by languages, regions, and countries. Seven main research topics emerged from past research (i.e., animal studies of food intake, risk factors and at-risk groups for eating disorders, body image in eating disorders, studies of cognition and brain in eating disorders, symptomatology and comorbidity of eating disorders, body weight and nutrition status in eating disorders, and treatment of eating disorders), with different topics showing unique research trends across the years. CONCLUSIONS: This bibliometric analysis presents the most complete up-to-date overview on published research on EDs. While there is an increasing trend for EDs research, the available research evidence is generally from Western countries; thus, it is suggested that cooperation on EDs research should be strengthened between Western countries and other countries in the future.

Research topics in intellectual disabilities in North Korea: A scoping review.

BACKGROUND: Research outcomes on intellectual development and related disabilities in North Korea are not widely known. Therefore, the current scoping review aimed to provide preliminary insight on research topics concerning intellectual disabilities in North Korea. METHOD: A six-stage framework for scoping review was adopted to examine research trends. Articles were categorised based on the era of supreme leader and research topic. RESULTS: There is a greater amount of research regarding intellectual disabilities in the recent Kim Jong-un era compared to the period of the previous leader where research outcomes on general intelligence were the focus. Significant qualitative progress was similarly found. CONCLUSIONS: The current analysis on research outcomes provides meaningful insights to aid in understanding the atmosphere in North Korea surrounding intellectual disabilities. Follow-up studies and open discussions are necessary for further progress.

Thematic research clusters in very old populations (≥ 80 years): a bibliometric approach.

BACKGROUND: Population aging will be one of humanity's major challenges in the decades to come. In addition to focusing on the pathologies causing the greatest mortality and morbidity in this population, such as dementia, health research in elderly people must consider a myriad of other interlinked factors, such as geriatric syndromes, social aspects, and factors related to preserving quality of life and promoting healthy aging. This study aims to identify the main subject areas attracting research attention with regard to very old (≥ 80 years) populations. METHODS: Documents assigned with the medical subject heading "Aged, 80 and over" were retrieved from MEDLINE and the Web of Science. This dataset was used to determine publication output by disease, geographic region, country, and discipline. A co-word analysis was undertaken to identify thematic research clusters. RESULTS: Since the mid-2000s, there has been a boom in scientific output focusing specifically on very old populations, especially in Europe (43.7% of the documents) but also in North America (30.5%) and Asia (26%); other regions made only nominal contributions (0.5 to 4.4%). The USA produced the most research, while the most growth over the study period occurred in Japan, Spain, and China. Four broad thematic clusters were identified: a) geriatric diseases, health services for the aged, and social and psychological issues of aging; b) cardiovascular diseases; c) neoplasms, and d) bacterial infections & anti-bacterial agents. CONCLUSIONS: Scientific research in very old populations covers a wide variety of interrelated topics. In quantitative terms, the top subject areas have to do with cardiovascular and cerebrovascular diseases (including aortic valve stenosis and stroke), dementia, and neoplasms. However, other degenerative pathologies, geriatric syndromes, and different social and psychosocial aspects also attract considerable interest. It is necessary to promote more equal participation in global research on pathologies and topics related to very elderly populations, as the highest rates of population aging and the largest numbers of elderly people in the next decades will be in low- and middle-income countries.

Improved dynamics of sharing research findings in the COVID-19 epidemic compared with the SARS and Ebola epidemics.

BACKGROUND: When a new or re-emergent pathogen, such as SARS-CoV-2, causes a major outbreak, rapid access to pertinent research findings is crucial for planning strategies and decision making. We researched whether the speed of sharing research results in the COVID-19 epidemic was higher than the SARS and Ebola epidemics. We also researched whether there is any difference in the most frequent topics investigated before and after the COVID-19, SARS, and Ebola epidemics started. METHODS: We used PubMed database search tools to determine the time-period it took for the number of articles to rise after the epidemics started and the most frequent topics assigned to the articles. RESULTS: The main results were, first, the rise in the number of articles occurred 6 weeks after the COVID-19 epidemic started whereas, this rise occurred 4 months after the SARS and 7 months after the Ebola epidemics started. Second, etiology, statistics & numerical data, and epidemiology were the three most frequent topics investigated in the COVID-19 epidemic. However, etiology, microbiology, and genetics in the SARS epidemic, and statistics & numerical data, epidemiology, and prevention & control in the Ebola epidemic were more frequently studied compared with other topics. Third, some topics were studied more frequently after the epidemics started. CONCLUSIONS: The speed of sharing results in the COVID-19 epidemic was much higher than the SARS and Ebola epidemics, and that there is a difference in the most frequent articles' topics investigated in these three epidemics. Due to the value of time in controlling epidemics spread, the study highlights the necessity of defining more solutions for rapidly providing pertinent research findings in fighting against the next public health emergency.

Characterization of Global Research Trends and Prospects on Single-Cell Sequencing Technology: Bibliometric Analysis.

BACKGROUND: As single-cell sequencing technology has been gradually introduced, it is essential to characterize global collaboration networks and map development trends over the past 20 years. OBJECTIVE: The aim of this paper was to illustrate collaboration in the field of single-cell sequencing methods and explore key topics and future directions. METHODS: Bibliometric analyses were conducted with CiteSpace and VOSviewer software on publications prior to November 2019 from the Web of Science Core Collection about single-cell sequencing methods. RESULTS: Ultimately, we identified 2489 records, which were published in 495 journals by 14,202 authors from 1970 institutes in 61 countries. There was a noticeable increase in publications in 2014. The United States and high-income countries in Europe contributed to most of the records included. Harvard University, Stanford University, Karolinska Institutes, Peking University, and the University of Washington were the biggest nodes in every cluster of the collaboration network, and SA Teichmann, JC Marioni, A Regev, and FC Tang were the top-producing authors. Keywords co-occurrence analysis suggested applications in immunology as a developing research trend. CONCLUSIONS: We concluded that the global collaboration network was unformed and that high-income countries contributed more to the rapidly growth of publications of single-cell sequencing technology. Furthermore, the application in immunology might be the next research hotspot and developmental direction.

A bibliometric analysis of publication of funded studies in nursing research from Web of Science, 2008-2018.

AIM: To analyse the current status and publication trends of funded studies in nursing-related research from 2008 to 2018, available in the Web of Science. DESIGN: A longitudinal bibliometric analysis of publications of funded studies in nursing-related research, obtained from the Web of Science, was conducted. METHODS: On 10 May 2019, we accessed 77,772 funded studies (2008-2018) from the Web of Science. Bibliometric methods and indicators were used to classify the publications and summarize the overall number, countries/regions, institutions, journals, and other parameters of the publications. RESULTS: The global output of nursing-related funded research publications increased significantly over time. The three leading countries with the highest number of funded publications were the United States, Australia, and the United Kingdom, with the United States accounting for 15 of the top 20 institutions associated with funded publications, which mostly included institutions of higher education. The most common disciplines of these publications were oncology, psychiatry, and paediatrics. The top three journals that published the largest number of nursing-related funded publications were the Journal of Clinical Nursing, the Journal of Advanced Nursing, and the International Journal of Nursing Studies. Keywords with the highest frequency of occurrence included "nurses," "qualitative research," "older people," "quality of life," "depression," "cancer," and "children." CONCLUSIONS: Nursing-related research has been drawing increasing attention over the years. Analysing the output of funded publications and monitoring the new dynamics of the international development of academic research in the field of nursing are crucial for determining future directions of nursing-related research development. IMPACT: The results of this study will provide a reference for scholars to evaluate the current utilization efficiency of global nursing-related research funding and demonstrate the development and trends in nursing-related research.

Twenty Key Challenges in Environmental and Resource Economics.

Economic and ecological systems are closely interlinked at a global and a regional level, offering a broad variety of important research topics in environmental and resource economics. The successful identification of key challenges for current and future research supports development of novel theories, empirical applications, and appropriate policy designs. It allows establishing a future-oriented research agenda whose ultimate goal is an efficient, equitable, and sustainable use of natural resources. Based on a normative foundation, the paper aims to identify fundamental topics, current trends, and major research gaps to motivate further development of academic work in the field.

Investigating the emerging COVID-19 research trends in the field of business and management: A bibliometric analysis approach.

The COVID-19 pandemic has been labeled as a black swan event that caused a ripple effect on every aspect of human life. Despite the short time span of the pandemic-only four and half months so far-a rather large volume of research pertaining to COVID-19 has been published (107 articles indexed in Scopus and the Web of Science). This article presents the findings of a bibliometric study of COVID-19 literature in the business and management domain to identify current areas of research and propose a way forward. The analysis of the published literature identified four main research themes and 18 sub-themes. The findings and propositions of this study suggest that COVID-19 will be the catalyst of several long- and short-term policy changes and requires the theoretical and empirical attention of researchers. The offered propositions will act as a roadmap to potential research opportunities.

Research topics and trends in medical education by social network analysis.

BACKGROUND: As studies analyzing the networks and relational structures of research topics in academic fields emerge, studies that apply methods of network and relationship analysis, such as social network analysis (SNA), are drawing more attention. The purpose of this study is to explore the interaction of medical education subjects in the framework of complex systems theory using SNA and to analyze the trends in medical education. METHODS: The authors extracted keywords using Medical Subject Headings terms from 9,379 research articles (162,866 keywords) published in 1963-2015 in PubMed. They generated an occurrence frequency matrix, calculated relatedness using Weighted Jaccard Similarity, and analyzed and visualized the networks with Gephi software. RESULTS: Newly emerging topics by period units were identified as historical trends, and 20 global-level topic clusters were obtained through network analysis. A time-series analysis led to the definition of five historical periods: the waking phase (1963-1975), the birth phase (1976-1990), the growth phase (1991-1996), the maturity phase (1997-2005), and the expansion phase (2006-2015). CONCLUSIONS: The study analyzed the trends in medical education research using SNA and analyzed their meaning using complex systems theory. During the 53-year period studied, medical education research has been subdivided and has expanded, improved, and changed along with shifts in society's needs. By analyzing the trends in medical education using the conceptual framework of complex systems theory, the research team determined that medical education is forming a sense of the voluntary order within the field of medicine by interacting with social studies, philosophy, etc., and establishing legitimacy and originality.

Epidemiologic research topics in Germany: a keyword network analysis of 2014 DGEpi conference presentations.

Knowledge of epidemiologic research topics as well as trends is useful for scientific societies, researchers and funding agencies. In recent years researchers recognized the usefulness of keyword network analysis for visualizing and analyzing scientific research topics. Therefore, we applied keyword network analysis to present an overview of current epidemiologic research topics in Germany. Accepted submissions to the 9th annual congress of the German Society for Epidemiology (DGEpi) in 2014 were used as data source. Submitters had to choose one of 19 subject areas, and were ask to provide a title, structured abstract, names of authors along with their affiliations, and a list of freely selectable keywords. Keywords had been provided for 262 (82 %) submissions, 1030 keywords in total. Overall the most common keywords were: "migration" (18 times), "prevention" (15 times), followed by "children", "cohort study", "physical activity", and "secondary data analysis" (11 times each). Some keywords showed a certain concentration under one specific subject area, e.g. "migration" with 8 of 18 in social epidemiology or "breast cancer" with 4 of 7 in cancer epidemiology. While others like "physical activity" were equally distributed over multiple subject areas (cardiovascular & metabolic diseases, ageing, methods, paediatrics, prevention & health service research). This keyword network analysis demonstrated the high diversity of epidemiologic research topics with a large number of distinct keywords as presented at the annual conference of the DGEpi.

The joint cardiovascular research profile of the university medical centres in the Netherlands.

Biomedical scientific research in the Netherlands has a good reputation worldwide. Quantitatively, the university medical centres (UMCs) deliver about 40 % of the total number of scientific publications of this research. Analysis of the bibliometric output data of the UMCs shows that their research is highly cited. These output-based analyses also indicate the high impact of cardiovascular scientific research in these centres, illustrating the strength of this research in the Netherlands. A set of six joint national cardiovascular research topics selected by the UMCs can be recognised. At the top are heart failure, rhythm disorder research and atherosclerosis. National collaboration of top scientists in consortia in these three areas is successful in acquiring funding of large-scale programs. Our observations suggest that funding national consortia of experts focused on a few selected research topics may increase the international competitiveness of cardiovascular research in the Netherlands.

Development and emerging trends of drug resistance mutations in HIV: a bibliometric analysis based on CiteSpace.

Background: Antiretroviral therapy has led to AIDS being a chronic disease. Nevertheless, the presence of constantly emerging drug resistance mutations poses a challenge to clinical treatment. A systematic analysis to summarize the advancements and uncharted territory of drug resistance mutations is urgently needed and may provide new clues for solving this problem. Methods: We gathered 3,694 publications on drug resistance mutations from the Web of Science Core Collection with CiteSpace software and performed an analysis to visualize the results and predict future new directions and emerging trends. Betweenness centrality, count, and burst value were taken as standards. Results: The number of papers on HIV medication resistance mutations during the last 10 years shows a wave-like trend. In terms of nation, organization, and author, the United States (1449), University of London (193), and Mark A. Wainberg (66) are the most significant contributors. The most frequently cited article is "Drug resistance mutations for surveillance of transmitted HIV-1 drug-resistance: 2009 update." Hot topics in this field include "next-generation sequencing," "tenofovir alafenamide," "children," "regimens," "accumulation," "dolutegravir," "rilpivirine," "sex," "pretreatment drug resistance," and "open label." Research on drug resistance in teenagers, novel mutation detection techniques, and drug development is ongoing, and numerous publications have indicated the presence of mutations related to current medications. Therefore, testing must be performed regularly for patients who have used medications for a long period. Additionally, by choosing medications with a longer half-life, patients can take fewer doses of their prescription, increasing patient compliance. Conclusion: This study involved a bibliometric visualization analysis of the literature on drug resistance mutations, providing insight into the field's evolution and emerging patterns and offering academics a resource to better understand HIV drug resistance mutations and contribute to the field's advancement.

Machine Learning-Based Approach for Identifying Research Gaps: COVID-19 as a Case Study.

BACKGROUND: Research gaps refer to unanswered questions in the existing body of knowledge, either due to a lack of studies or inconclusive results. Research gaps are essential starting points and motivation in scientific research. Traditional methods for identifying research gaps, such as literature reviews and expert opinions, can be time consuming, labor intensive, and prone to bias. They may also fall short when dealing with rapidly evolving or time-sensitive subjects. Thus, innovative scalable approaches are needed to identify research gaps, systematically assess the literature, and prioritize areas for further study in the topic of interest. OBJECTIVE: In this paper, we propose a machine learning-based approach for identifying research gaps through the analysis of scientific literature. We used the COVID-19 pandemic as a case study. METHODS: We conducted an analysis to identify research gaps in COVID-19 literature using the COVID-19 Open Research (CORD-19) data set, which comprises 1,121,433 papers related to the COVID-19 pandemic. Our approach is based on the BERTopic topic modeling technique, which leverages transformers and class-based term frequency-inverse document frequency to create dense clusters allowing for easily interpretable topics. Our BERTopic-based approach involves 3 stages: embedding documents, clustering documents (dimension reduction and clustering), and representing topics (generating candidates and maximizing candidate relevance). RESULTS: After applying the study selection criteria, we included 33,206 abstracts in the analysis of this study. The final list of research gaps identified 21 different areas, which were grouped into 6 principal topics. These topics were: "virus of COVID-19," "risk factors of COVID-19," "prevention of COVID-19," "treatment of COVID-19," "health care delivery during COVID-19," "and impact of COVID-19." The most prominent topic, observed in over half of the analyzed studies, was "the impact of COVID-19." CONCLUSIONS: The proposed machine learning-based approach has the potential to identify research gaps in scientific literature. This study is not intended to replace individual literature research within a selected topic. Instead, it can serve as a guide to formulate precise literature search queries in specific areas associated with research questions that previous publications have earmarked for future exploration. Future research should leverage an up-to-date list of studies that are retrieved from the most common databases in the target area. When feasible, full texts or, at minimum, discussion sections should be analyzed rather than limiting their analysis to abstracts. Furthermore, future studies could evaluate more efficient modeling algorithms, especially those combining topic modeling with statistical uncertainty quantification, such as conformal prediction.

Infodemic: Challenges and solutions in topic discovery and data process.

BACKGROUND: The Coronavirus Disease 2019 (COVID-19) pandemic was a huge shock to society, and the ensuing information problems had a huge impact on society at the same time. The urgent need to understand the Infodemic, i.e., the importance of the spread of false information related to the epidemic, has been highlighted. However, while there is a growing interest in this phenomenon, studies on the topic discovery, data collection, and data preparation phases of the information analysis process have been lacking. OBJECTIVE: Since the epidemic is unprecedented and has not ended to this day, we aimed to examine the existing Infodemic-related literature from January 2019 to December 2022. METHODS: We have systematically searched ScienceDirect and IEEE Xplore databases with some search limitations. From the searched literature we selected titles, abstracts and keywords, and limitations sections. We conducted an extensive structured literature search and analysis by filtering the literature and sorting out the available information. RESULTS: A total of 47 papers ended up meeting the requirements of this review. Researchers in all of these literatures encountered different challenges, most of which were focused on the data collection step, with few challenges encountered in the data preparation phase and almost none in the topic discovery section. The challenges were mainly divided into the points of how to collect data quickly, how to get the required data samples, how to filter the data, what to do if the data set is too small, how to pick the right classifier and how to deal with topic drift and diversity. In addition, researchers have proposed partial solutions to the challenges, and we have also proposed possible solutions. CONCLUSIONS: This review found that Infodemic is a rapidly growing research area that attracts the interest of researchers from different disciplines. The number of studies in this field has increased significantly in recent years, with researchers from different countries, including the United States, India, and China. Infodemic topic discovery, data collection, and data preparation are not easy, and each step faces different challenges. While there is some research in this emerging field, there are still many challenges that need to be addressed. These findings highlight the need for more articles to address these issues and fill these gaps.

Content Analysis of Abstracts Published in Autism Journals in 2021: The year in Review.

PURPOSE: Ever since Leo Kanner first described autism in 1943, the research in this field has grown immensely. In 2021 alone, 5837 SCOPUS indexed documents were published with a title that contained the words: "autism", "autistic", or "ASD". The purpose of this study was to examine the most common topics of autism research in 2021 and present a geographical contribution to this research. METHODS: We performed a content analysis of 1102 abstracts from the articles published in 11 Autism journals in 2021. The following journals, indexed by the SCOPUS database, were included: Autism, Autism Research, Molecular Autism, Journal of Autism and Developmental Disorders, Research in Autism Spectrum Disorders, Focus on Autism and Other Developmental Disabilities, Education and Training in Autism and Developmental Disabilities, Review Journal of Autism and Developmental Disorders, Advances in Autism, Autism and Developmental Language Impairments, and Autism in Adulthood. RESULTS: According to the analysis, the main research topics were: mental health, social communication, social skills, quality of life, parenting stress, ADHD, Covid-19, self-efficacy, special education, and theory of mind. In relation to geographic distribution, most studies came from the USA, followed by the UK, Australia, and Canada. CONCLUSION: Research topics were aligned with the priorities set by stakeholders in autism, most notably persons with autism themselves and their family members. There is a big gap in research production between developed countries and developing countries.

Survey on sentiment analysis: evolution of research methods and topics.

Sentiment analysis, one of the research hotspots in the natural language processing field, has attracted the attention of researchers, and research papers on the field are increasingly published. Many literature reviews on sentiment analysis involving techniques, methods, and applications have been produced using different survey methodologies and tools, but there has not been a survey dedicated to the evolution of research methods and topics of sentiment analysis. There have also been few survey works leveraging keyword co-occurrence on sentiment analysis. Therefore, this study presents a survey of sentiment analysis focusing on the evolution of research methods and topics. It incorporates keyword co-occurrence analysis with a community detection algorithm. This survey not only compares and analyzes the connections between research methods and topics over the past two decades but also uncovers the hotspots and trends over time, thus providing guidance for researchers. Furthermore, this paper presents broad practical insights into the methods and topics of sentiment analysis, while also identifying technical directions, limitations, and future work.