Search results

The purpose of this paper is to investigate the usage, captures, mentions, social media and citations of highly cited papers of Library and information science (LIS).

This study is quantitative research that was conducted using scientometrics and altmetrics indicators. The research sample consists of LIS classic papers. The papers contain highly cited papers of LIS that are introduced by Google Scholar. The research data have been gathered from Google Scholar, Scopus and Plum Analytics Categories. The data analysis has been done by Excel and SPSS applications.

The data indicate that among the highly cited articles of LIS, the highest score regarding the usage, captures, mentions and social media and the most abundance of citations belong to “Citation advantage of open access articles” and “Usage patterns of collaborative tagging systems.” Based on the results of Spearman statistical tests, there is a positive significant correlation between Google Scholar Citations and all studied indicators. However, only the correlation between Google Scholar Citations with capture metrics (p-value = 0.047) and citation metrics (p-value = 0.0001) was statistically significant.

Altmetrics indicators can be used as complement traditional indicators of Scientometrics to study the impact of papers. Therefore, the Altmetrics knowledge of LIS researchers and experts and practicing new studies in this field will be very important.

The purpose of this paper is to analyze the relationship between dissemination of research papers on Twitter and its influence on research impact.

Four types of journal Twitter accounts (journal, owner, publisher and no Twitter account) were defined to observe differences in the number of tweets and citations. In total, 4,176 articles from 350 journals were extracted from Plum Analytics. This altmetric provider tracks the number of tweets and citations for each paper. Student’s t-test for two-paired samples was used to detect significant differences between each group of journals. Regression analysis was performed to detect which variables may influence the getting of tweets and citations.

The results show that journals with their own Twitter account obtain more tweets (46 percent) and citations (34 percent) than journals without a Twitter account. Followers is the variable that attracts more tweets (ß=0.47) and citations (ß=0.28) but the effect is small and the fit is not good for tweets (R²=0.46) and insignificant for citations (R²=0.18).

This is the first study that tests the performance of research journals on Twitter according to their handles, observing how the dissemination of content in this microblogging network influences the citation of their papers.

The study aimed to measure scientific and social impacts of Iranian researchers' “Top Papers” in clinical medicine using citation and altmetric indicators.

In this applied descriptive-analytical study, it used scientometric analysis. A total of 166 “Top Papers” of Iranian researchers in clinical medicine category of Web of Science (WoS) database including “Highly Cited Papers” and “Hot Papers” published between 2009 and 2019 were used. Overall, 29 indicators and their data were extracted from WoS, Scopus, ResearchGate (RG) and PlumX in March 2020.

The results showed that there exists a positive correlation between the number of citations in WoS, Scopus, RG, PubMed and Crossref. In addition, it was found that there existed a positive correlation between the received citations by articles and altmetric indicators. According to the results, there is a strong correlation between RG Research Interest and citation impact. The correlation analysis on the Plum Analytics categories including “Usage”, “Capture”, “Mention”, “Social Media” and “Citation” showed the correlations between five dimensions of impact were positive and significant. The results have led the authors to think more about new metrics that can response to new developments in the new information areas.

There are limitations to access altmetric.com in Iran and cannot be used easily. On the other hand, because of considering 24 indicators, authors had to investigate only a sample of 166 top papers from Iranian researchers to present the detailed results. About nature of altmetric indicators, although they reflect the nonacademic impact of articles alongside bibliographic indicators, they still cannot be a complete representative of the influence of their owners.

This study can indicate a practical application appropriate for the future study. It would be valuable to further examine how social academic platforms construct images of impact of research and how this impacts the social impact of the university as a mission. This study suggests that social media attention to academic research can be much greater than what is shown in traditional indicators such as citation.

This study examines 29 indicators from four platforms including RG, WoS, Scopus and PlumX, simultaneously and measures the relationship among them.

The Saint Mary’s College of California (SMC) library plays an integral role in supporting one of the goals in the College’s Strategic Plan: “Raise the Academic Profile and Distinction”. This case study aims to assess the effectiveness of PlumX as a tool to showcase the academic profile and distinction of SMC. The library recognizes the importance of capturing impact of non-traditional creativity and engagement in addition to just traditional impact metrics of research.

This paper describes the collaborative effort of the College and the College’s library to identify faculty scholarship, creativity and engagement and collect data demonstrating the impact of the works. Traditional metrics, like citation counts, do not do SMC faculty justice because faculty scholarship comes beyond just books and articles. To more fully document the real intellectual corpus the College, the library is working with a new system, PlumX, to collect web-based information about both traditionally and non-traditionally published work.

The collection of metrics across five categories (citations, usage, social media, mentions and captures), and the flexibility of displaying on screen or downloading for use in other analytic reports made possible through PlumX proved to be a start toward demonstrating the academic distinction of College’s unique faculty. SMC will continue to partner with PlumX to assess and improve its usability and effectiveness.

This paper outlines how altmetrics can be used to measure and share impact of faculty research at a liberal arts, teaching-focused college in ways reflective of the unique intellectual contributions.

Background modelling has played an imperative role in the moving object detection as the progress of foreground extraction during video analysis and surveillance in many real-time applications. It is usually done by background subtraction. This method is uprightly based on a mathematical model with a fixed feature as a static background, where the background image is fixed with the foreground object running over it. Usually, this image is taken as the background model and is compared against every new frame of the input video sequence. In this paper, the authors presented a renewed background modelling method for foreground segmentation. The principal objective of the work is to perform the foreground object detection only in the premeditated region of interest (ROI). The ROI is calculated using the proposed algorithm reducing and raising by half (RRH). In this algorithm, the coordinate of a circle with the frame width as the diameter is considered for traversal to find the pixel difference. The change in the pixel intensity is considered to be the foreground object and the position of it is determined based on the pixel location. Most of the techniques study their updates to the pixels of the complete frame which may result in increased false rate; The proposed system deals these flaw by controlling the ROI object (the region only where the background subtraction is performed) and thus extracts a correct foreground by exactly categorizes the pixel as the foreground and mines the precise foreground object. The broad experimental results and the evaluation parameters of the proposed approach with the state of art methods were compared against the most recent background subtraction approaches. Moreover, the efficiency of the authors’ method is analyzed in different situations to prove that this method is available for real-time videos as well as videos available in the 2014 challenge change detection data set.

In this paper, the authors presented a fresh background modelling method for foreground segmentation. The main objective of the work is to perform the foreground object detection only on the premeditated ROI. The region for foreground extraction is calculated using proposed RRH algorithm. Most of the techniques study their updates to the pixels of the complete frame which may result in increased false rate; most challenging case is that, the slow moving object is updated quickly to detect the foreground region. The anticipated system deals these flaw by controlling the ROI object (the region only where the background subtraction is performed) and thus extracts a correct foreground by exactly categorizing the pixel as the foreground and mining the precise foreground object.

Plum Analytics provide a new conduit for documenting and contextualizing the public impact and reach of research within digitally networked environments. While limitations are notable, the metrics promoted through the platform can be used to build a more comprehensive view of research impact.

The algorithm used in the work was proposed by the authors and are used for experimental evaluations.

This study aims to measure the impact of the selected papers in the field of social sciences indexed in Scopus using altmetrics tools.

The research community consists of the articles of the Iranian researchers in the field of social sciences indexed in the Scopus database in 2014–2018. Some of the most important altmetric service providers have been used to assess the presence of the research outputs in the social media and their impact assessment. Also, the relationship between variables such as scientific collaboration of researchers, open access journals and the quality of research journals with altmetric activity have been investigated through appropriate correlation tests.

The findings indicated that the most important social media publishing Iranian articles are Mendeley, Twitter and Facebook. The results of the correlation test showed a statistically significant positive and weak relationship between the scientific collaboration of researchers and their altmetric activity. Also, there is a significant and weak statistical relation between journal openness and the altmetric scores. In this study, the findings suggest that the published articles in the journals with higher quality indicators have higher altmetric scores and are more likely to be present in social media.

In this study, the social network indicators have been introduced as a solution to examine the effectiveness of research activities on social media. These indicators can be used to evaluate the impact and usefulness of the articles and other scientific outputs with the aim of completing and eliminating the shortcomings of traditional scientometrics indicators. What distinguishes altmetric criteria from other criteria related to the scientometric studies is the speed, ease and transparency of these scales. This allows the publications to be evaluated regardless of their formal form and in the shortest possible time, and in addition to the scientific impact, the social impact of the works is also measured.

The results of these studies show that using altmetric service providers not only reflects the social impact of publications on authors in different subject areas but also helps libraries, universities, research organizations and politicians in planning, budgeting and allocating resources.