A Proposed Method for Residual Citation Allocation Based on Citation Contexts’ Similarity

This article proposes an approach for allocating residual citations to scientific publications and demonstrating this proposed approach with a sample of biomedical publications. Residue citations (i.e., citations that are lost due to citation practices termed “Obliteration by Incorporation” and the “Palimpsestic Syndrome”) in consequent citations in the second, third or nth generations are then reconstituted. The proposed approach takes into account citation contexts (i.e., the contribution of a cited publication) for allocating residual citation. The proposed method for allocating residual citation is based on the similarity between the citation contexts of a publication and those of its nth generation citations in their n+1th generation citations. The proposed method was demonstrated using a sample with ten base articles and their five generations of citations, from which 5,272 citation context pairs were obtained. The proposed indirect citation weighting was compared with the existing cascading citation weighting method using one T-test. Statistical tests were also performed to understand the differences in the residual citations from one generation to the other. Like the cascading citation system, residual citations from articles to their generations of citations decreased as the number of generations increased. However, residue citations accrued to publications at all the generations were statistically different between the proposed residual citation and the cascading citation system. This study proposes a method for assessing scientific communication based on the contribution of scientific publications beyond the conventional direct citation.

Diversity in citations to a single study: A citation context network analysis of how evidence from a prospective cohort study was cited

Between its origin in the 1950s and its endorsement by a consensus conference in 1984, the diet–heart hypothesis was the subject of intense controversy. Paul et al. (1963) is a highly cited prospective cohort study that reported findings inconvenient for this hypothesis, reporting no association between diet and heart disease; however, many other findings were also reported. By citation context and network analysis of 343 citing papers, I show how Paul et al. was cited in the 20 years after its publication. Generally, different findings were cited by different communities focusing on different risk factors; these communities were established by either research foci title terms or via cluster membership as established via modularity maximization. The most frequently cited findings were the significant associations between heart disease and serum cholesterol (n = 85), blood pressure (n = 57), and coffee consumption (n = 54). The lack of association between diet and heart disease was cited in just 41 papers. Yet, no single empirical finding was referred to in more than 25% of the citing papers. This raises questions about the value of inferring impact from citation counts alone and raises problems for studies using such counts to measure citation bias.

Predictions of Citations of a Scholarly Paper

Bibliometrics is a statistical analysis of written publications such as books or articles. A bibliographic citationis a reference to a book, article, web page, or other published item. Thus citations are useful for identifying the progress ofthe particular work and measuring the quality of the research article. The cited papers are downloaded using the crawler. Fromthe downloaded article, identify article relation by analyzing the citation context of the article. So first extract the citation context from the article. Citation context are classifies based on cue phrases of Simon tufel. Next, identify the relation of unlabeled article by word embedding. After labeling all articles identifythe perspective behind the citation of the article. In this project, citation relation is identified based on cue phrases of Simon tufel finally article impact is quantified based on the citation network formed from citation analysis. Index Terms: bibliometrics, citation, word embedding, article

Leveraging full-text article exploration for citation analysis

Scientific articles often include in-text citations quoting from external sources. When the cited source is an article, the citation context can be analyzed by exploring the article full-text. To quickly access the key information, researchers are often interested in identifying the sections of the cited article that are most pertinent to the text surrounding the citation in the citing article. This paper first performs a data-driven analysis of the correlation between the textual content of the sections of the cited article and the text snippet where the citation is placed. The results of the correlation analysis show that the title and abstract of the cited article are likely to include content highly similar to the citing snippet. However, the subsequent sections of the paper often include cited text snippets as well. Hence, there is a need to understand the extent to which an exploration of the full-text of the cited article would be beneficial to gain insights into the citing snippet, considering also the fact that the full-text access could be restricted. To this end, we then propose a classification approach to automatically predicting whether the cited snippets in the full-text of the paper contain a significant amount of new content beyond abstract and title. The proposed approach could support researchers in leveraging full-text article exploration for citation analysis. The experiments conducted on real scientific articles show promising results: the classifier has a 90% chance to correctly distinguish between the full-text exploration and only title and abstract cases.

Citation context-based topic models: discovering cited and citing topics from full text

PurposeFull text of a document is a rich source of information that can be used to provide meaningful topics. The purpose of this paper is to demonstrate how to use citation context (CC) in the full text to identify the cited topics and citing topics efficiently and effectively by employing automatic text analysis algorithms.Design/methodology/approachThe authors present two novel topic models, Citation-Context-LDA (CC-LDA) and Citation-Context-Reference-LDA (CCRef-LDA). CC is leveraged to extract the citing text from the full text, which makes it possible to discover topics with accuracy. CC-LDA incorporates CC, citing text, and their latent relationship, while CCRef-LDA incorporates CC, citing text, their latent relationship and reference information in CC. Collapsed Gibbs sampling is used to achieve an approximate estimation. The capacity of CC-LDA to simultaneously learn cited topics and citing topics together with their links is investigated. Moreover, a topic influence measure method based on CC-LDA is proposed and applied to create links between the two-level topics. In addition, the capacity of CCRef-LDA to discover topic influential references is also investigated.FindingsThe results indicate CC-LDA and CCRef-LDA achieve improved or comparable performance in terms of both perplexity and symmetric Kullback–Leibler (sKL) divergence. Moreover, CC-LDA is effective in discovering the cited topics and citing topics with topic influence, and CCRef-LDA is able to find the cited topic influential references.Originality/valueThe automatic method provides novel knowledge for cited topics and citing topics discovery. Topic influence learnt by our model can link two-level topics and create a semantic topic network. The method can also use topic specificity as a feature to rank references.

A New Citation Recommendation Strategy Based on Term Functions in Related Studies Section

Purpose Researchers frequently encounter the following problems when writing scientific articles: (1) Selecting appropriate citations to support the research idea is challenging. (2) The literature review is not conducted extensively, which leads to working on a research problem that others have well addressed. This study focuses on citation recommendation in the related studies section by applying the term function of a citation context, potentially improving the efficiency of writing a literature review. Design/methodology/approach We present nine term functions with three newly created and six identified from existing literature. Using these term functions as labels, we annotate 531 research papers in three topics to evaluate our proposed recommendation strategy. BM25 and Word2vec with VSM are implemented as the baseline models for the recommendation. Then the term function information is applied to enhance the performance. Findings The experiments show that the term function-based methods outperform the baseline methods regarding the recall, precision, and F1-score measurement, demonstrating that term functions are useful in identifying valuable citations. Research limitations The dataset is insufficient due to the complexity of annotating citation functions for paragraphs in the related studies section. More recent deep learning models should be performed to future validate the proposed approach. Practical implications The citation recommendation strategy can be helpful for valuable citation discovery, semantic scientific retrieval, and automatic literature review generation. Originality/value The proposed citation function-based citation recommendation can generate intuitive explanations of the results for users, improving the transparency, persuasiveness, and effectiveness of recommender systems.

A novel focal-loss and class-weight-aware convolutional neural network for the classification of in-text citations

We argue that citations, as they have different reasons and functions, should not all be treated in the same way. Using the large, annotated dataset of about 10K citation contexts annotated by human experts, extracted from the Association for Computational Linguistics repository, we present a deep learning–based citation context classification architecture. Unlike all existing state-of-the-art feature-based citation classification models, our proposed convolutional neural network (CNN) with fastText-based pre-trained embedding vectors uses only the citation context as its input to outperform them in both binary- (important and non-important) and multi-class (Use, Extends, CompareOrContrast, Motivation, Background, Other) citation classification tasks. Furthermore, we propose using focal-loss and class-weight functions in the CNN model to overcome the inherited class imbalance issues in citation classification datasets. We show that using the focal-loss function with CNN adds a factor of [Formula: see text] to the cross-entropy function. Our model improves on the baseline results by achieving an encouraging 90.6 F1 score with 90.7% accuracy and a 72.3 F1 score with a 72.1% accuracy score, respectively, for binary- and multi-class citation classification tasks.