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Incidental or influential? – A decade of using text-mining for citation function classification.
This work looks in depth at several studies that have attempted to automate the process of citation importance classification based on the publications’ full text. We offer a comparison of their individual similarities, strengths and weaknesses. We analyse a range of features that have been previously used in this task. Our experimental results confirm that the number of in-text references are highly predictive of influence. Contrary to the work of Valenzuela et al. (2015), we find abstract similarity one of the most predictive features. Overall, we show that many of the features previously described in literature have been either reported as not particularly predictive, cannot be reproduced based on their existing descriptions or should not be used due to their reliance on external changing evidence. Additionally, we find significant variance in the results provided by the PDF extraction tools used in the pre-processing stages of citation extraction. This has a direct and significant impact on the classification features that rely on this extraction process. Consequently, we discuss challenges and potential improvements in the classification pipeline, provide a critical review of the performance of individual features and address the importance of constructing a large-scale gold-standard reference dataset
Will This Paper Increase Your h-index? Scientific Impact Prediction
Scientific impact plays a central role in the evaluation of the output of
scholars, departments, and institutions. A widely used measure of scientific
impact is citations, with a growing body of literature focused on predicting
the number of citations obtained by any given publication. The effectiveness of
such predictions, however, is fundamentally limited by the power-law
distribution of citations, whereby publications with few citations are
extremely common and publications with many citations are relatively rare.
Given this limitation, in this work we instead address a related question asked
by many academic researchers in the course of writing a paper, namely: "Will
this paper increase my h-index?" Using a real academic dataset with over 1.7
million authors, 2 million papers, and 8 million citation relationships from
the premier online academic service ArnetMiner, we formalize a novel scientific
impact prediction problem to examine several factors that can drive a paper to
increase the primary author's h-index. We find that the researcher's authority
on the publication topic and the venue in which the paper is published are
crucial factors to the increase of the primary author's h-index, while the
topic popularity and the co-authors' h-indices are of surprisingly little
relevance. By leveraging relevant factors, we find a greater than 87.5%
potential predictability for whether a paper will contribute to an author's
h-index within five years. As a further experiment, we generate a
self-prediction for this paper, estimating that there is a 76% probability that
it will contribute to the h-index of the co-author with the highest current
h-index in five years. We conclude that our findings on the quantification of
scientific impact can help researchers to expand their influence and more
effectively leverage their position of "standing on the shoulders of giants."Comment: Proc. of the 8th ACM International Conference on Web Search and Data
Mining (WSDM'15
Intrinsically Dynamic Network Communities
Community finding algorithms for networks have recently been extended to
dynamic data. Most of these recent methods aim at exhibiting community
partitions from successive graph snapshots and thereafter connecting or
smoothing these partitions using clever time-dependent features and sampling
techniques. These approaches are nonetheless achieving longitudinal rather than
dynamic community detection. We assume that communities are fundamentally
defined by the repetition of interactions among a set of nodes over time.
According to this definition, analyzing the data by considering successive
snapshots induces a significant loss of information: we suggest that it blurs
essentially dynamic phenomena - such as communities based on repeated
inter-temporal interactions, nodes switching from a community to another across
time, or the possibility that a community survives while its members are being
integrally replaced over a longer time period. We propose a formalism which
aims at tackling this issue in the context of time-directed datasets (such as
citation networks), and present several illustrations on both empirical and
synthetic dynamic networks. We eventually introduce intrinsically dynamic
metrics to qualify temporal community structure and emphasize their possible
role as an estimator of the quality of the community detection - taking into
account the fact that various empirical contexts may call for distinct
`community' definitions and detection criteria.Comment: 27 pages, 11 figure
Modeling the clustering in citation networks
For the study of citation networks, a challenging problem is modeling the
high clustering. Existing studies indicate that the promising way to model the
high clustering is a copying strategy, i.e., a paper copies the references of
its neighbour as its own references. However, the line of models highly
underestimates the number of abundant triangles observed in real citation
networks and thus cannot well model the high clustering. In this paper, we
point out that the failure of existing models lies in that they do not capture
the connecting patterns among existing papers. By leveraging the knowledge
indicated by such connecting patterns, we further propose a new model for the
high clustering in citation networks. Experiments on two real world citation
networks, respectively from a special research area and a multidisciplinary
research area, demonstrate that our model can reproduce not only the power-law
degree distribution as traditional models but also the number of triangles, the
high clustering coefficient and the size distribution of co-citation clusters
as observed in these real networks
Chi-square-based scoring function for categorization of MEDLINE citations
Objectives: Text categorization has been used in biomedical informatics for
identifying documents containing relevant topics of interest. We developed a
simple method that uses a chi-square-based scoring function to determine the
likelihood of MEDLINE citations containing genetic relevant topic. Methods: Our
procedure requires construction of a genetic and a nongenetic domain document
corpus. We used MeSH descriptors assigned to MEDLINE citations for this
categorization task. We compared frequencies of MeSH descriptors between two
corpora applying chi-square test. A MeSH descriptor was considered to be a
positive indicator if its relative observed frequency in the genetic domain
corpus was greater than its relative observed frequency in the nongenetic
domain corpus. The output of the proposed method is a list of scores for all
the citations, with the highest score given to those citations containing MeSH
descriptors typical for the genetic domain. Results: Validation was done on a
set of 734 manually annotated MEDLINE citations. It achieved predictive
accuracy of 0.87 with 0.69 recall and 0.64 precision. We evaluated the method
by comparing it to three machine learning algorithms (support vector machines,
decision trees, na\"ive Bayes). Although the differences were not statistically
significantly different, results showed that our chi-square scoring performs as
good as compared machine learning algorithms. Conclusions: We suggest that the
chi-square scoring is an effective solution to help categorize MEDLINE
citations. The algorithm is implemented in the BITOLA literature-based
discovery support system as a preprocessor for gene symbol disambiguation
process.Comment: 34 pages, 2 figure
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