233,626 research outputs found
Are Killer Bees Good for Coffee? The Contribution of a Paper\u27s Title and Other Factors to Its Future Citations
How can the title of a paper affect its subsequent number of citations? We compared the citation rate of 5941 papers published in the journal Biological Conservation from 1968 to 2012 in relation to: paper length; title length; number of authors; paper age; presence of punctuation (colons, commas or question marks); geographic and taxonomic breadth; the word ‘method’; and the type of manuscript (article, review). The total number of citations increased in more recently published papers and thus we corrected citation rate (average number of citations per year since publication) by publication age. As expected, review papers had, on average, twice the number of citations compared to other types of articles. Papers with the greatest geographic or taxonomic breadth were cited up to twice as frequently as narrowly focused papers. Titles phrased as questions, shorter titles, and papers with more authors had slightly higher numbers of citations. However, overall, we found that the included parameters explained only 12% of the variability in citation rate. This suggests that finding a good title is necessary, but that other factors are more important to construct a well-cited paper. We suggest that to become highly cited, a primary requirement is that papers need to advance the science significantly and be useful to readers
Impact of lexical and sentiment factors on the popularity of scientific papers
We investigate how textual properties of scientific papers relate to the
number of citations they receive. Our main finding is that correlations are
non-linear and affect differently most-cited and typical papers. For instance,
we find that in most journals short titles correlate positively with citations
only for the most cited papers, for typical papers the correlation is in most
cases negative. Our analysis of 6 different factors, calculated both at the
title and abstract level of 4.3 million papers in over 1500 journals, reveals
the number of authors, and the length and complexity of the abstract, as having
the strongest (positive) influence on the number of citations.Comment: 9 pages, 3 figures, 3 table
What increases (social) media attention: Research impact, author prominence or title attractiveness?
Do only major scientific breakthroughs hit the news and social media, or does
a 'catchy' title help to attract public attention? How strong is the connection
between the importance of a scientific paper and the (social) media attention
it receives? In this study we investigate these questions by analysing the
relationship between the observed attention and certain characteristics of
scientific papers from two major multidisciplinary journals: Nature
Communication (NC) and Proceedings of the National Academy of Sciences (PNAS).
We describe papers by features based on the linguistic properties of their
titles and centrality measures of their authors in their co-authorship network.
We identify linguistic features and collaboration patterns that might be
indicators for future attention, and are characteristic to different journals,
research disciplines, and media sources.Comment: Paper presented at 23rd International Conference on Science and
Technology Indicators (STI 2018) in Leiden, The Netherland
A Supervised Approach to Extractive Summarisation of Scientific Papers
Automatic summarisation is a popular approach to reduce a document to its
main arguments. Recent research in the area has focused on neural approaches to
summarisation, which can be very data-hungry. However, few large datasets exist
and none for the traditionally popular domain of scientific publications, which
opens up challenging research avenues centered on encoding large, complex
documents. In this paper, we introduce a new dataset for summarisation of
computer science publications by exploiting a large resource of author provided
summaries and show straightforward ways of extending it further. We develop
models on the dataset making use of both neural sentence encoding and
traditionally used summarisation features and show that models which encode
sentences as well as their local and global context perform best, significantly
outperforming well-established baseline methods.Comment: 11 pages, 6 figure
The NASA Astrophysics Data System: The Search Engine and its User Interface
The ADS Abstract and Article Services provide access to the astronomical
literature through the World Wide Web (WWW). The forms based user interface
provides access to sophisticated searching capabilities that allow our users to
find references in the fields of Astronomy, Physics/Geophysics, and
astronomical Instrumentation and Engineering. The returned information includes
links to other on-line information sources, creating an extensive astronomical
digital library. Other interfaces to the ADS databases provide direct access to
the ADS data to allow developers of other data systems to integrate our data
into their system.
The search engine is a custom-built software system that is specifically
tailored to search astronomical references. It includes an extensive synonym
list that contains discipline specific knowledge about search term
equivalences.
Search request logs show the usage pattern of the various search system
capabilities. Access logs show the world-wide distribution of ADS users.
The ADS can be accessed at http://adswww.harvard.eduComment: 23 pages, 18 figures, 11 table
Reasoning & Querying – State of the Art
Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF
Animating the development of Social Networks over time using a dynamic extension of multidimensional scaling
The animation of network visualizations poses technical and theoretical
challenges. Rather stable patterns are required before the mental map enables a
user to make inferences over time. In order to enhance stability, we developed
an extension of stress-minimization with developments over time. This dynamic
layouter is no longer based on linear interpolation between independent static
visualizations, but change over time is used as a parameter in the
optimization. Because of our focus on structural change versus stability the
attention is shifted from the relational graph to the latent eigenvectors of
matrices. The approach is illustrated with animations for the journal citation
environments of Social Networks, the (co-)author networks in the carrying
community of this journal, and the topical development using relations among
its title words. Our results are also compared with animations based on
PajekToSVGAnim and SoNIA
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