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"Computer networks Periodicals"
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A Principal Component Analysis of 39 Scientific Impact Measures
The impact of scientific publications has traditionally been expressed in terms of citation counts. However, scientific activity has moved online over the past decade. To better capture scientific impact in the digital era, a variety of new impact measures has been proposed on the basis of social network analysis and usage log data. Here we investigate how these new measures relate to each other, and how accurately and completely they express scientific impact.
We performed a principal component analysis of the rankings produced by 39 existing and proposed measures of scholarly impact that were calculated on the basis of both citation and usage log data.
Our results indicate that the notion of scientific impact is a multi-dimensional construct that can not be adequately measured by any single indicator, although some measures are more suitable than others. The commonly used citation Impact Factor is not positioned at the core of this construct, but at its periphery, and should thus be used with caution.
Journal Article
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
A bibliometric analysis of publications in computer networking research
Computer networking is a major research discipline in computer science, electrical engineering, and computer engineering. The field has been actively growing, in terms of both research and development, for the past hundred years. This study uses the article content and metadata of four important computer networking periodicals—IEEE Communications Surveys and Tutorials (COMST), IEEE/ACM Transactions on Networking (TON), ACM Special Interest Group on Data Communications (SIGCOMM), and IEEE International Conference on Computer Communications (INFOCOM)—obtained using ACM, IEEE Xplore, Scopus and CrossRef, for an 18-year period (2000–2017) to address important bibliometrics questions. All of the venues are prestigious, yet they publish quite different research. The first two of these periodicals (COMST and TON) are highly reputed journals of the fields while SIGCOMM and INFOCOM are considered top conferences of the field. SIGCOMM and INFOCOM publish new original research. TON has a similar genre and publishes new original research as well as the extended versions of different research published in the conferences such as SIGCOMM and INFOCOM, while COMST publishes surveys and reviews (which not only summarize previous works but highlight future research opportunities). In this study, we aim to track the co-evolution of trends in the COMST and TON journals and compare them to the publication trends in INFOCOM and SIGCOMM. Our analyses of the computer networking literature include: (a) metadata analysis; (b) content-based analysis; and (c) citation analysis. In addition, we identify the significant trends and the most influential authors, institutes and countries, based on the publication count as well as article citations. Through this study, we are proposing a methodology and framework for performing a comprehensive bibliometric analysis on computer networking research. To the best of our knowledge, no such study has been undertaken in computer networking until now.
Journal Article
Users Polarization on Facebook and Youtube
Users online tend to select information that support and adhere their beliefs, and to form polarized groups sharing the same view-e.g. echo chambers. Algorithms for content promotion may favour this phenomenon, by accounting for users preferences and thus limiting the exposure to unsolicited contents. To shade light on this question, we perform a comparative study on how same contents (videos) are consumed on different online social media-i.e. Facebook and YouTube-over a sample of 12M of users. Our findings show that content drives the emergence of echo chambers on both platforms. Moreover, we show that the users' commenting patterns are accurate predictors for the formation of echo-chambers.
Journal Article
Neural network world :international journal on neural and mass-parallel computing and information systems
Mezinárodní časopis o problematice neuronových a paralelních výpočetních a informačních systémů
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