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Added by
Waleed Ammar
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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We explore the degree to which papers prepublished on arXiv garner more citations, in an attempt to paint a sharper picture of fairness issues related to prepublishing. A papers citation count is estimated using a negative-binomial generalized linear model (GLM) while observing a binary variable which indicates whether the paper has been prepublished. We control for author influence (via the authors h-index at the time of paper writing), publication venue, and overall time that paper has been available on arXiv. Our analysis only includes papers that were eventually accepted for publication at top-tier CS conferences, and were posted on arXiv either before or after the acceptance notification. We observe that papers submitted to arXiv before acceptance have, on average, 65% more citations in the following year compared to papers submitted after. We note that this finding is not causal, and discuss possible next steps.
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Previous work for text summarization in scientific domain mainly focused on the content of the input document, but seldom considering its citation network. However, scientific papers are full of uncommon domain-specific terms, making it almost impossible for the model to understand its true meaning without the help of the relevant research community. In this paper, we redefine the task of scientific papers summarization by utilizing their citation graph and propose a citation graph-based summarization model CGSum which can incorporate the information of both the source paper and its references. In addition, we construct a novel scientific papers summarization dataset Semantic Scholar Network (SSN) which contains 141K research papers in different domains and 661K citation relationships. The entire dataset constitutes a large connected citation graph. Extensive experiments show that our model can achieve competitive performance when compared with the pretrained models even with a simple architecture. The results also indicates the citation graph is crucial to better understand the content of papers and generate high-quality summaries.
In over five years, Bornmann, Stefaner, de Moya Anegon, and Mutz (2014) and Bornmann, Stefaner, de Moya Anegon, and Mutz (2014, 2015) have published several releases of the www.excellencemapping.net tool revealing (clusters of) excellent institutions worldwide based on citation data. With the new release, a completely revised tool has been published. It is not only based on citation data (bibliometrics), but also Mendeley data (altmetrics). Thus, the institutional impact measurement of the tool has been expanded by focusing on additional status groups besides researchers such as students and librarians. Furthermore, the visualization of the data has been completely updated by improving the operability for the user and including new features such as institutional profile pages. In this paper, we describe the datasets for the current excellencemapping.net tool and the indicators applied. Furthermore, the underlying statistics for the tool and the use of the web application are explained.
Accessibility research sits at the junction of several disciplines, drawing influence from HCI, disability studies, psychology, education, and more. To characterize the influences and extensions of accessibility research, we undertake a study of citation trends for accessibility and related HCI communities. We assess the diversity of venues and fields of study represented among the referenced and citing papers of 836 accessibility research papers from ASSETS and CHI, finding that though publications in computer science dominate these citation relationships, the relative proportion of citations from papers on psychology and medicine has grown over time. Though ASSETS is a more niche venue than CHI in terms of citational diversity, both conferences display standard levels of diversity among their incoming and outgoing citations when analyzed in the context of 53K papers from 13 accessibility and HCI conference venues.
Multidisciplinary cooperation is now common in research since social issues inevitably involve multiple disciplines. In research articles, reference information, especially citation content, is an important representation of communication among different disciplines. Analyzing the distribution characteristics of references from different disciplines in research articles is basic to detecting the sources of referred information and identifying contributions of different disciplines. This work takes articles in PLoS as the data and characterizes the references from different disciplines based on Citation Content Analysis (CCA). First, we download 210,334 full-text articles from PLoS and collect the information of the in-text citations. Then, we identify the discipline of each reference in these academic articles. To characterize the distribution of these references, we analyze three characteristics, namely, the number of citations, the average cited intensity and the average citation length. Finally, we conclude that the distributions of references from different disciplines are significantly different. Although most references come from Natural Science, Humanities and Social Sciences play important roles in the Introduction and Background sections of the articles. Basic disciplines, such as Mathematics, mainly provide research methods in the articles in PLoS. Citations mentioned in the Results and Discussion sections of articles are mainly in-discipline citations, such as citations from Nursing and Medicine in PLoS.
Many altmetric studies analyze which papers were mentioned how often in specific altmetrics sources. In order to study the potential policy relevance of tweets from another perspective, we investigate which tweets were cited in papers. If many tweets were cited in publications, this might demonstrate that tweets have substantial and useful content. Overall, a rather low number of tweets (n=5506) were cited by less than 3000 papers. Most tweets do not seem to be cited because of any cognitive influence they might have had on studies; they rather were study objects. Most of the papers citing tweets are from the subject areas Social Sciences, Arts and Humanities, and Computer Sciences. Most of the papers cited only one tweet. Up to 55 tweets cited in a single paper were found. This research-in-progress does not support a high policy-relevance of tweets. However, a content analysis of the tweets and/or papers might lead to a more detailed conclusion.
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