A novel machine-learning approach to measuring scientific knowledge flows using citation context analysis

Hassan, Saeed‐Ul; Safder, Iqra; Akram, Anam; Kamiran, Faisal

doi:10.1007/s11192-018-2767-x

Cited by 53 publications

(40 citation statements)

References 42 publications

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“…For example, a classification scheme could contain "background", "methodological", "constant/comparison", "negational", and "perfunctory" citations. To assign citations to more general categories, they could be classified as "influential" (or useful) versus "non-influential" (or perfunctory), as evidenced by some previous studies (based on Hassan et al, 2018;Pride & Knoth, 2017;Valenzuela et al, 2015;Zhu et al, 2015). Citations could also be classified in terms of their relevance into "great relevance" (cited the main ideas presented in cited papers), (2) "intermediate relevance" (cited the techniques mentioned in the cited papers), and (3) "minor relevance" (not cited the main ideas or the techniques in the cited papers) (see Ramos et al, 2012).…”

Section: Summarizing the Empirical Resultsmentioning

confidence: 99%

“…The Association for Computational Linguistics Archives (ACL) Anthology (https://www.aclweb.org/anthology/) has been used by many researchers to conduct citation content/context studies (e.g. Hassan, Safder, Akram, & Kamiran, 2018;Hernández-Alvarez et al, 2017;Jha et al, 2017;Valenzuela, Ha, & Etzioni, 2015;Zhu et al, 2015). CiteSeer (http://csxstatic.ist.psu.edu/) which contains publications in computer and information sciences, is another source that can be considered for citation context studies (Doslu & Bingol, 2016).…”

Section: Technical Developments and New Sources Of Citation Studiesmentioning

confidence: 99%

“…Another set of papers have employed a similar approach to Small et al (2017) and Small (2018) in order to classify papers to two groups -influential (important) and non-influential papers (Hassan et al, 2018;Pride & Knoth, 2017;Valenzuela et al, 2015;Zhu et al, 2015). However, the main difference is that Small et al (2017) and Small (2018) have mainly focused on linguistic cues to classify papers, while the studies that are described in the following have employed a wide range of features such as "the total number of times a paper was cited" in the citing paper in order to classify studies to influential and non-influential papers.…”

Section: Influential Versus Non-influential Citationsmentioning

confidence: 99%

“…This study showed that, contrary to the study by Valenzuela et al (2015), "self-citation" and "similarity between abstracts" were other predictors of citation influence. Hassan et al (2018) also proposed model was tested through the analysis of 20527 papers from the ACL anthology.…”

Section: Influential Versus Non-influential Citationsmentioning

confidence: 99%

“…Austria was the top country with 13.71% important citations, followed by Sweden (9.77%), Brazil (9.11%), Netherlands (9.06%), and Germany (7.35%). USA (6.83%) was the seventh country and China (4.11%) was the 15th country in terms of the total number of important citations (Hassan et al, 2018).…”

Section: Influential Versus Non-influential Citationsmentioning

confidence: 99%

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What do citation counts measure? An updated review of studies on citations in scientific documents published between 2006 and 2018

2019

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The purpose of this paper is to update the review of Bornmann and Daniel (2008) presenting a narrative review of studies on citations in scientific documents. The current review covers 41 studies published between 2006 and 2018. Bornmann and Daniel (2008) focused on earlier years. The current review describes the (new) studies on citation content and context analyses as well as the studies that explore the citation motivation of scholars through surveys or interviews. One focus in this paper is on the technical developments in the last decade, such as the richer meta-data available and machine-readable formats of scientific papers. These developments have resulted in citation context analyses of large datasets in comprehensive studies (which was not possible previously). Many studies in recent years have used computational and machine learning techniques to determine citation functions and polarities, some of which have attempted to overcome the methodological weaknesses of previous studies. The automated recognition of citation functions seems to have the potential to greatly enhance citation indices and information retrieval capabilities. Our review of the empirical studies demonstrates that a paper may be cited for very different scientific and non-scientific reasons. This result accords with the finding by Bornmann and Daniel (2008). The current review also shows that to better understand the relationship between citing and cited documents, a variety of features should be analyzed, primarily the citation context, the semantics and linguistic patterns in citations, citation locations within the citing document, and citation polarity (negative, neutral, positive).

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Section: Summarizing the Empirical Resultsmentioning

confidence: 99%

Section: Technical Developments and New Sources Of Citation Studiesmentioning

confidence: 99%

Section: Influential Versus Non-influential Citationsmentioning

confidence: 99%

Section: Influential Versus Non-influential Citationsmentioning

confidence: 99%

Section: Influential Versus Non-influential Citationsmentioning

confidence: 99%

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What do citation counts measure? An updated review of studies on citations in scientific documents published between 2006 and 2018

2019

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Extracting the evolutionary backbone of scientific domains: The semantic main path network analysis approach based on citation context analysis

Jiang

Liu

2023

Asso for Info Science & Tech

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Main path analysis is a popular method for extracting the scientific backbone from the citation network of a research domain. Existing approaches ignored the semantic relationships between the citing and cited publications, resulting in several adverse issues, in terms of coherence of main paths and coverage of significant studies. This paper advocated the semantic main path network analysis approach to alleviate these issues based on citation function analysis.A wide variety of SciBERT-based deep learning models were designed for identifying citation functions. Semantic citation networks were built by either including important citations, for example, extension, motivation, usage and similarity, or excluding incidental citations like background and future work.Semantic main path network was built by merging the top-K main paths extracted from various time slices of semantic citation network. In addition, a three-way framework was proposed for the quantitative evaluation of main path analysis results. Both qualitative and quantitative analysis on three research areas of computational linguistics demonstrated that, compared to semantics-agnostic counterparts, different types of semantic main path networks provide complementary views of scientific knowledge flows. Combining them together, we obtained a more precise and comprehensive picture of domain evolution and uncover more coherent development pathways between scientific ideas.

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Mining the Context of Citations in Scientific Publications

Hassan

Iqbal

Imran

et al. 2018

Lecture Notes in Computer Science

Self Cite

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Recent advancements in information retrieval systems significantly rely on the context-based features and semantic matching techniques to provide relevant information to users from ever-growing digital libraries. Scientific communities seek to understand the implications of research, its importance and its applicability for future research directions. To mine this information, absolute citations merely fail to measure the importance of scientific literature, as a citation may have a specific context in full text. Thus, a comprehensive contextual understanding of cited references is necessary. For this purpose, numerous techniques have been proposed that tap the power of artificial intelligence models to detect important or incidental (non-important) citations in full text scholarly publications. In this paper, we compare and build upon on four state-of-the-art models that detect important citations using 450 manually annotated citations by experts-randomly selected from 20,527 papers from the Association for Computational Linguistics corpus. Of the total 64 unique features proposed by the four selected state-of-the-art models, the top 29 were chosen using the Extra-Trees classifier. These were then fed it to our supervised machine learning based models: Random Forest (RF) and Support Vector Machine. The RF model outperforms existing selected systems by more than 10%, with 89% precision-recall curve. Finally, we qualitatively assessed important and non-important citations by employing and self-organizing maps. Overall, our research work supports information retrieval algorithms that detect and fetch scientific articles on the basis of both qualitative and quantitative indices in scholarly big data.

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A novel machine-learning approach to measuring scientific knowledge flows using citation context analysis

Cited by 53 publications

References 42 publications

What do citation counts measure? An updated review of studies on citations in scientific documents published between 2006 and 2018

What do citation counts measure? An updated review of studies on citations in scientific documents published between 2006 and 2018

Extracting the evolutionary backbone of scientific domains: The semantic main path network analysis approach based on citation context analysis

Mining the Context of Citations in Scientific Publications

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