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 Comparative Study of Pairwise Learning Methods based on Kernel Ridge Regression

Many machine learning problems can be formulated as predicting labels for a pair of objects. Problems of that kind are often referred to as pairwise learning, dyadic prediction or network inference problems. During the last decade kernel methods have played a dominant role in pairwise learning. They still obtain a state-of-the-art predictive performance, but a theoretical analysis of their behavior has been underexplored in the machine learning literature. In this work we review and unify existing kernel-based algorithms that are commonly used in different pairwise learning settings, ranging from matrix filtering to zero-shot learning. To this end, we focus on closed-form efficient instantiations of Kronecker kernel ridge regression. We show that independent task kernel ridge regression, two-step kernel ridge regression and a linear matrix filter arise naturally as a special case of Kronecker kernel ridge regression, implying that all these methods implicitly minimize a squared loss. In addition, we analyze universality, consistency and spectral filtering properties. Our theoretical results provide valuable insights in assessing the advantages and limitations of existing pairwise learning methods.Comment: arXiv admin note: text overlap with arXiv:1606.0427

Shorter Leukocyte Telomere Length in Relation to Presumed Nonalcoholic Fatty Liver Disease in Mexican-American Men in NHANES 1999-2002.

Leukocyte telomere length is shorter in response to chronic disease processes associated with inflammation such as diabetes mellitus and coronary artery disease. Data from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2002 was used to explore the relationship between leukocyte telomere length and presumed NAFLD, as indicated by elevated serum alanine aminotransferase (ALT) levels, obesity, or abdominal obesity. Logistic regression models were used to evaluate the relationship between telomere length and presumed markers of NAFLD adjusting for possible confounders. There was no relationship between elevated ALT levels, abdominal obesity, or obesity and telomere length in adjusted models in NHANES (OR 1.13, 95% CI 0.48-2.65; OR 1.17, 95% CI 0.52-2.62, resp.). Mexican-American men had shorter telomere length in relation to presumed NAFLD (OR 0.07, 95% CI 0.006-0.79) and using different indicators of NAFLD (OR 0.012, 95% CI 0.0006-0.24). Mexican origin with presumed NAFLD had shorter telomere length than men in other population groups. Longitudinal studies are necessary to evaluate the role of telomere length as a potential predictor to assess pathogenesis of NALFD in Mexicans