Traditional citation analysis methods have been criticized because their theoretical base of statistical counts does not reflect the motive or judgment of citing authors. In particular, self-citations may give undue credits to a cited article or mislead scientific development. This research aims to answer the question of whether self-citation is biased by probing into the motives and context of citations. It takes an integrated and fine-grained view of self-citations by examining them via multiple lenses—polarity, density, and location of citations. In addition, it explores potential moderating effects of citation level and associations among location contexts of citations to the same references for the first time. We analyzed academic publications across different topics and disciplines using both qualitative and quantitative methods. The results provide evidence that self-citations are free of bias in terms of citation density and polarity uncertainty, but they can be biased with respect to positivity and negativity of citations. Furthermore, this study reveals impacts of self-citing behavior on some citation patterns involving citation density, location concentration, and associations. The examination of self-citing behavior from those new perspectives shed new lights on the nature and function of self-citing behavior.
more »
« less
This content will become publicly available on December 1, 2024
Signing data citations enables data verification and citation persistence
Abstract Commonly used data citation practices rely on unverifiable retrieval methods which are susceptible to content drift, which occurs when the data associated with an identifier have been allowed to change. Based on our earlier work on reliable dataset identifiers, we propose signed citations, i.e., customary data citations extended to also include a standards-based, verifiable, unique, and fixed-length digital content signature. We show that content signatures enable independent verification of the cited content and can improve the persistence of the citation. Because content signatures are location- and storage-medium-agnostic, cited data can be copied to new locations to ensure their persistence across current and future storage media and data networks. As a result, content signatures can be leveraged to help scalably store, locate, access, and independently verify content across new and existing data infrastructures. Content signatures can also be embedded inside content to create robust, distributed knowledge graphs that can be cited using a single signed citation. We describe applications of signed citations to solve real-world data collection, identification, and citation challenges.
more »
« less
- Award ID(s):
- 2027654
- NSF-PAR ID:
- 10431155
- Date Published:
- Journal Name:
- Scientific Data
- Volume:
- 10
- Issue:
- 1
- ISSN:
- 2052-4463
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
more » « less
Abstract Following widespread availability of computerized databases, much research has correlated bibliometric measures from papers or patents to subsequent success, typically measured as the number of publications or citations. Building on this large body of work, we ask the following questions: given available bibliometric information in one year, along with the combined theories on sources of creative breakthroughs from the literatures on creativity and innovation, how accurately can we explain the impact of authors in a given research community in the following year? In particular, who is most likely to publish, publish highly cited work, and even publish a highly cited outlier? And, how accurately can these existing theories predict breakthroughs using only contemporaneous data? After reviewing and synthesizing (often competing) theories from the literatures, we simultaneously model the collective hypotheses based on available data in the year before RNA interference was discovered. We operationalize author impact using publication count, forward citations, and the more stringent definition of being in the top decile of the citation distribution. Explanatory power of current theories altogether ranges from less than 9% for being top cited to 24% for productivity. Machine learning (ML) methods yield similar findings as the explanatory linear models, and tangible improvement only for non-linear Support Vector Machine models. We also perform predictions using only existing data until 1997, and find lower predictability than using explanatory models. We conclude with an agenda for future progress in the bibliometric study of creativity and look forward to ML research that can explain its models.
-
A standard measure of the influence of a research paper is the number of times it is cited. However, papers may be cited for many reasons, and citation count offers limited information about the extent to which a paper affected the content of subsequent publications. We therefore propose a novel method to quantify linguistic influence in timestamped document collections. There are two main steps: first, identify lexical and semantic changes using contextual embeddings and word frequencies; second, aggregate information about these changes into per-document influence scores by estimating a high-dimensional Hawkes process with a low-rank parameter matrix. We show that this measure of linguistic influence is predictive of future citations: the estimate of linguistic influence from the two years after a paper’s publication is correlated with and predictive of its citation count in the following three years. This is demonstrated using an online evaluation with incremental temporal training/test splits, in comparison with a strong baseline that includes predictors for initial citation counts, topics, and lexical features.more » « less
-
Proc. of 2023 IEEE 39th International Conference on Data Engineering (Ed.)Numerous papers get published all the time. However, some papers are born to be well-cited while others are not. In this work, we revisit the important problem of citation prediction, by focusing on the important yet realistic prediction on the average number of citations a paper will attract per year. The task is nonetheless challenging because many correlated factors underlie the potential impact of a paper, such as the prestige of its authors, the authority of its publishing venue, and the significance of the problems/techniques/applications it studies. To jointly model these factors, we propose to construct a heterogeneous publication network of nodes including papers, authors, venues, and terms. Moreover, we devise a novel heterogeneous graph neural network (HGN) to jointly embed all types of nodes and links, towards the modeling of research impact and its propagation. Beyond graph heterogeneity, we find it also important to consider the latent research domains, because the same nodes can have different impacts within different communities. Therefore, we further devise a novel cluster-aware (CA) module, which models all nodes and their interactions under the proper contexts of research domains. Finally, to exploit the information-rich texts associated with papers, we devise a novel text-enhancing (TE) module for automatic quality term mining. With the real-world publication data of DBLP, we construct three different networks and conduct comprehensive experiments to evaluate our proposed CATE-HGN framework, against various state-of-the-art models. Rich quantitative results and qualitative case studies demonstrate the superiority of CATEHGN in citation prediction on publication networks, and indicate its general advantages in various relevant downstream tasks on text-rich heterogeneous networks.more » « less
-
more » « less
Abstract Motivation Development of bioinformatics methods is a long, complex and resource-hungry process. Hundreds of these tools were released. While some methods are highly cited and used, many suffer relatively low citation rates. We empirically analyze a large collection of recently released methods in three diverse protein function and disorder prediction areas to identify key factors that contribute to increased citations.
Results We show that provision of a working web server significantly boosts citation rates. On average, methods with working web servers generate three times as many citations compared to tools that are available as only source code, have no code and no server, or are no longer available. This observation holds consistently across different research areas and publication years. We also find that differences in predictive performance are unlikely to impact citation rates. Overall, our empirical results suggest that a relatively low-cost investment into the provision and long-term support of web servers would substantially increase the impact of bioinformatics tools.
