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"Computer networks Research Periodicals"
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Social media usage to share information in communication journals: An analysis of social media activity and article citations
Social media has surrounded every area of life, and social media platforms have become indispensable for today’s communication. Many journals use social media actively to promote and disseminate new articles. Its use to share the articles contributes many benefits, such as reaching more people and spreading information faster. However, there is no consensus in the studies that to evaluate between tweeted and non-tweeted papers regarding their citation numbers. Therefore, it was aimed to show the effect of social media on the citations of articles in the top ten communication-based journals. For this purpose, this work evaluated original articles published in the top 10 communication journals in 2018. The top 10 communication-based journals were chosen based on SCImago Journal & Country Rank (cited in 2019). Afterward, it was recorded the traditional citation numbers (Google Scholar and Thompson-Reuters Web of Science) and social media exposure of the articles in January 2021 (nearly three years after the articles’ publication date). It was assumed that this period would allow the impact of the published articles (the citations and Twitter mentions) to be fully observed. Based on this assessment, a positive correlation between exposure to social media and article citations was observed in this study.
Journal Article
Fake news on the internet: a literature review, synthesis and directions for future research
PurposeThe extensive distribution of fake news on the internet (FNI) has significantly affected many lives. Although numerous studies have recently been conducted on this topic, few have helped us to systematically understand the antecedents and consequences of FNI. This study contributes to the understanding of FNI and guides future research.Design/methodology/approachDrawing on the input–process–output framework, this study reviews 202 relevant articles to examine the extent to which the antecedents and consequences of FNI have been investigated. It proposes a conceptual framework and poses future research questions.FindingsFirst, it examines the “what”, “why”, “who”, “when”, “where” and “how” of creating FNI. Second, it analyses the spread features of FNI and the factors that affect the spread of FNI. Third, it investigates the consequences of FNI in the political, social, scientific, health, business, media and journalism fields.Originality/valueThe extant reviews on FNI mainly focus on the interventions or detection of FNI, and a few analyse the antecedents and consequences of FNI in specific fields. This study helps readers to synthetically understand the antecedents and consequences of FNI in all fields. This study is among the first to summarise the conceptual framework for FNI research, including the basic relevant theoretical foundations, research methodologies and public datasets.
Journal Article
A scientometric overview of CORD-19
As the COVID-19 pandemic unfolds, researchers from all disciplines are coming together and contributing their expertise. CORD-19, a dataset of COVID-19 and coronavirus publications, has been made available alongside calls to help mine the information it contains and to create tools to search it more effectively. We analyse the delineation of the publications included in CORD-19 from a scientometric perspective. Based on a comparison to the Web of Science database, we find that CORD-19 provides an almost complete coverage of research on COVID-19 and coronaviruses. CORD-19 contains not only research that deals directly with COVID-19 and coronaviruses, but also research on viruses in general. Publications from CORD-19 focus mostly on a few well-defined research areas, in particular: coronaviruses (primarily SARS-CoV, MERS-CoV and SARS-CoV-2); public health and viral epidemics; molecular biology of viruses; influenza and other families of viruses; immunology and antivirals; clinical medicine. CORD-19 publications that appeared in 2020, especially editorials and letters, are disproportionately popular on social media. While we fully endorse the CORD-19 initiative, it is important to be aware that CORD-19 extends beyond research on COVID-19 and coronaviruses.
Journal Article
Twenty-five years of education and information technologies: Insights from a topic modeling based bibliometric analysis
Education and Information Technologies (EAIT) has been a leading journal in education & educational research since 1996. To celebrate its 25th anniversary and provide a comprehensive overview of the field, a topic modeling-based bibliometric analysis was conducted on the articles published in this journal. The study is constructed upon two methods, bibliometric analysis, and topic modeling. The study aims to find out the trends in publications and citations, prominent countries, affiliations and the status of authors, the prominent topics, and the thematic characteristics of these topics, as well as research interests and trends. The results show that the articles are grouped under the 21 topics. The top five most studied of them have been determined as \"Technology acceptance\", \"Social networkbased learning\", \"Teacher education\", \"Satisfaction of e-learning\" and \"E-learning\". Finally, the acceleration results of each topic within itself and compared to other topics show that the most accelerated topic is \"Gamification\", while the most accelerated topic compared to other topics has been determined as \"Technology acceptance\". The general results of the study shed light on future studies in terms of determining the research interests and trends of publications in the field of educational technologies, EAIT.
Journal Article
Bibliometric and content analysis of the internet of things research: a social science perspective
PurposeThe aim is to reveal contemporary research trends and patterns in Internet of Things (IoTs) so that social scientists who are new to the discipline may be steered towards rightful directions when examining this phenomenon.Design/methodology/approachA total of 169 IoT articles indexed in the Web of Science database were analyzed via bibliometric analysis and content analysis. The VOSViewer software was used to identify popular keywords of the IoT topics, its publication productivity, the most relevant journals, and the most prolific authors within. Content analysis was conducted manually to determine the most popular research methods used, the most frequently studied contexts, the most popular IoT application areas, the most highly examined user perspectives, and the most often employed theories.FindingsThe synthesis of both the bibliometric and content analysis results suggest the necessity of investigating the post-adoption technology usage behavior of IoT technology in developing countries, particularly in smart home. This is especially so from new landscapes using other theories or models, apart from the overwhelmed Technology Adoption Model (TAM) and its variants.Originality/valueWith a focus on addressing the state-of-the-art of IoT in social science, and to synthesize its future research directions systematically, this study was conducted with both bibliometric and content analysis, in order to enhance the overall analysis for higher accuracy and more reliable results.
Journal Article
Twitter Predicts Citation Rates of Ecological Research
The relationship between traditional metrics of research impact (e.g., number of citations) and alternative metrics (altmetrics) such as Twitter activity are of great interest, but remain imprecisely quantified. We used generalized linear mixed modeling to estimate the relative effects of Twitter activity, journal impact factor, and time since publication on Web of Science citation rates of 1,599 primary research articles from 20 ecology journals published from 2012-2014. We found a strong positive relationship between Twitter activity (i.e., the number of unique tweets about an article) and number of citations. Twitter activity was a more important predictor of citation rates than 5-year journal impact factor. Moreover, Twitter activity was not driven by journal impact factor; the 'highest-impact' journals were not necessarily the most discussed online. The effect of Twitter activity was only about a fifth as strong as time since publication; accounting for this confounding factor was critical for estimating the true effects of Twitter use. Articles in impactful journals can become heavily cited, but articles in journals with lower impact factors can generate considerable Twitter activity and also become heavily cited. Authors may benefit from establishing a strong social media presence, but should not expect research to become highly cited solely through social media promotion. Our research demonstrates that altmetrics and traditional metrics can be closely related, but not identical. We suggest that both altmetrics and traditional citation rates can be useful metrics of research impact.
Journal Article
A decade of learning analytics: Structural topic modeling based bibliometric analysis
Learning analytics (LA) has become an increasingly active field focusing on leveraging learning process data to understand and improve teaching and learning. With the explosive growth in the number of studies concerning LA, it is significant to investigate its research status and trends, particularly the thematic structure. Based on 3900 LA articles published during the past decade, this study explores answers to questions such as “what research topics were the LA community interested in?” and “how did such research topics evolve?” by adopting structural topic modeling and bibliometrics. Major publication sources, countries/regions, institutions, and scientific collaborations were examined and visualized. Based on the analyses, we present suggestions for future LA research and discussions about important topics in the field. It is worth highlighting LA combining various innovative technologies (e.g., visual dashboards, neural networks, multimodal technologies, and open learner models) to support classroom orchestration, personalized recommendation/feedback, self-regulated learning in flipped classrooms, interaction in game-based and social learning. This work is useful in providing an overview of LA research, revealing the trends in LA practices, and suggesting future research directions.
Journal Article
Systematic review of research design and reporting of imaging studies applying convolutional neural networks for radiological cancer diagnosis
Objectives
To perform a systematic review of design and reporting of imaging studies applying convolutional neural network models for radiological cancer diagnosis.
Methods
A comprehensive search of PUBMED, EMBASE, MEDLINE and SCOPUS was performed for published studies applying convolutional neural network models to radiological cancer diagnosis from January 1, 2016, to August 1, 2020. Two independent reviewers measured compliance with the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Compliance was defined as the proportion of applicable CLAIM items satisfied.
Results
One hundred eighty-six of 655 screened studies were included. Many studies did not meet the criteria for current design and reporting guidelines. Twenty-seven percent of studies documented eligibility criteria for their data (50/186, 95% CI 21–34%), 31% reported demographics for their study population (58/186, 95% CI 25–39%) and 49% of studies assessed model performance on test data partitions (91/186, 95% CI 42–57%). Median CLAIM compliance was 0.40 (IQR 0.33–0.49). Compliance correlated positively with publication year (
ρ
= 0.15,
p
= .04) and journal H-index (
ρ
= 0.27,
p
< .001). Clinical journals demonstrated higher mean compliance than technical journals (0.44 vs. 0.37,
p
< .001).
Conclusions
Our findings highlight opportunities for improved design and reporting of convolutional neural network research for radiological cancer diagnosis.
Key Points
•
Imaging studies applying convolutional neural networks (CNNs) for cancer diagnosis frequently omit key clinical information including eligibility criteria and population demographics.
•
Fewer than half of imaging studies assessed model performance on explicitly unobserved test data partitions.
•
Design and reporting standards have improved in CNN research for radiological cancer diagnosis, though many opportunities remain for further progress.
Journal Article