基于P-W型网络引文引证可追溯性研究——以情报学期刊为例

网络引文引证的可追溯性研究一直是学术界普遍关注的热点.通过获取CSSCI 2010年收录的情报学期刊论文被科研人员引证的网络引文作为数据样本,实证分析情报学科研人员引证网络引文的总体可追溯情况,不同域名、网页类型网络引文与可追溯性以及URLs深度与可追溯性之间的关系,并最终针对可追溯性问题提出相应的策略来提高科研人员学术交流的效果.     

An ego-centric citation analysis of the works of Michael O. Rabin based on multiple citation indexes

The primary goal of this study was to carry out an ego-centric citation and reference analysis of the works of the mathematician and computer scientist, Michael O. Rabin. Until recently only a single citation database was available for such research – the ISI Citation Indexes. In this study we utilized and compared three major sources that provide citation data: the Web of Science, Google Scholar and Citeseer. Most cited works, citation identity, citation image makers and coauthors were identified. The citation image makers acquired through these sources differ considerably. Advantages and shortcomings of each of the tools are discussed in the context of computer science. A major issue in computer science is multiple manifestations of a work, i.e., its publication in several venues (technical reports, proceedings, journals, collections). The implications of multiple manifestations for citation analysis are discussed.     

Prediction of drive-by download attacks on Twitter

The popularity of Twitter for information discovery, coupled with the automatic shortening of URLs to save space, given the 140 character limit, provides cybercriminals with an opportunity to obfuscate the URL of a malicious Web page within a tweet. Once the URL is obfuscated, the cybercriminal can lure a user to click on it with enticing text and images before carrying out a cyber attack using a malicious Web server. This is known as a drive-by download. In a drive-by download a user's computer system is infected while interacting with the malicious endpoint, often without them being made aware the attack has taken place. An attacker can gain control of the system by exploiting unpatched system vulnerabilities and this form of attack currently represents one of the most common methods employed. In this paper we build a machine learning model using machine activity data and tweet metadata to move beyond post-execution classification of such URLs as malicious, to predict a URL will be malicious with 0.99 F-measure (using 10-fold cross-validation) and 0.833 (using an unseen test set) at 1 s into the interaction with the URL. Thus, providing a basis from which to kill the connection to the server before an attack has completed and proactively blocking and preventing an attack, rather than reacting and repairing at a later date.     

Researchers’ perceptions of citations

This paper looks at how citations are perceived among scientists. Based on a questionnaire survey it traces the repertoire of views and experiences about citations that could be found among Norwegian scientists that had published highly cited papers. Their views circle around three issues: the relation between the quality (or importance or significance) of a paper and its citation history; the importance of visibility and how different sorts of factors play a role in determining citation in general and high citation in particular; and the fairness (or lack of fairness) of the system. Taken together, the respondents’ answers and comments offer an informal (and fragmented) sociology of citations and their role in the world of science. In the final section we discuss the relevance of our findings in respect to the increasing use of citation indicators in science policy and research evaluations.     

Merging the citations received by arXiv-deposited e-prints and their corresponding published journal articles: Problems and perspectives

Merging the citation counts of arXiv-deposited e-prints (arXiv version) with those of their corresponding published journal articles (publisher version) is an important issue in citation analysis. Using examples of arXiv-deposited e-prints, this article adopts a manual approach to investigate the processing methods used by bibliographic repositories such as Google Scholar, Web of Science, Scopus, Astrophysics Data System (ADS), and INSPIRE for the citation merging. Both Google Scholar and ADS consolidate all citations from the two versions into the publisher one, whereas the consolidated citations are accumulated into the arXiv version in the INSPIRE repository. All these methods ignore the categories of the arXiv-deposited versions and the corresponding availability dates. As for Web of Science and Scopus, they count the citations of the two versions separately, which is likely regarding them as two independent articles. Focusing on journal articles that also appeared as arXiv e-prints, we classify them into two categories and identify two public availability dates of articles as the starting point of citation statistics. We present four feasible schemes to consolidate citation counts for the articles with both versions and also propose a universal scheme based on the research output. Furthermore, we investigated 2,662 e-prints in the “Computer Science - Digital Libraries” subject (cs.DL) from 1998 to 2018 in arXiv.org and manually calculated the consolidated citation counts of arXiv-deposited articles with the corresponding citation merging schemes. Furthermore, these citation consolidation methods are applied to the evaluation of articles, authors, and journals. Such empirical testing proves the feasibility of the schemes proposed in this article.     

基于Web的ISI三大引文索引数据库引文检索方法

本文在简述引文索引及其功能的基础上 ,介绍了基于 Web的 ISI三大引文索引数据库 SCI- E、SSCI及 A& HCI引文检索方法 ,并举例说明了此检索方法使用中的若干技巧 ,对专业检索人员和科研人员检索科技文献具有指导意义。     

期刊论文被引量与下载量关系研究

[目的/意义]探讨论文被引量与下载量之间的关系,对论文影响力评价有重要的意义。[方法/过程]通过CNKI数据库,以《中文核心期刊要目总览》中理工农医类及经济、历史、法律、哲学类期刊2006年刊载的55 000多篇论文为基础,分析不同类论文的被引量分布特征,比较同被引量论文的下载量以及相近下载量论文的平均被引量,采用Spearman相关性方法计算不同类论文下载量、被引量的等级相关系数。[结果/结论]不同类论文的被引量分布具有一致趋势:随被引量的增多论文数比例较快地减小直至为0,相应的被引量分布可以由指数衰减函数近似函数表征。在绝对数量上,论文的下载量、被引量存在较大差别,二者之间的相关性不明显,与下载、被引的自身特征以及各类论文的文献使用特征有关。Spearman等级相关分析表明,论文层级上,下载量、被引量的等级相关性较强,某一篇论文在一定时期内的下载量可以依靠其排序位数预测其统计意义上的被引排序数,可以为论文评价提供参考。     

Bibliometric analysis of CiteSeer data for countries

This article describes the results of our analysis of the data from the CiteSeer digital library. First, we examined the data from the point of view of source top-level Internet domains from which the data were collected. Second, we measured country shares in publications indexed by CiteSeer and compared them to those based on mainstream bibliographic data from the Web of Science and Scopus. And third, we concentrated on analyzing publications and their citations aggregated by countries. This way, we generated rankings of the most influential countries in computer science using several non-recursive as well as recursive methods such as citation counts or PageRank. We conclude that even if East Asian countries are underrepresented in CiteSeer, its data may well be used along with other conventional bibliographic databases for comparing the computer science research productivity and performance of countries.     

标准引用与论文引用的比较研究

标准和论文是科技成果的重要文献载体,具有重要的情报价值。本文通过文献调研法,从引用范围、引用目的、引用动机、关联性等维度比较了标准引用与论文引用的异同点。结论是标准引用与论文引用在表现出相似的知识关联关系和关联性,两者在引用条件、引用范围、引用目的、出版物性质、引用主体、引用内容选择等方面存在差异。     

期刊论文网络下载规律及与引文指标相关性研究

对期刊论文网络下载研究作了简要回顾,从期刊论文被引频次分布规律研究得到启示,以《中国学术期刊网络出版总库》为统计源,通过大样本抽样统计分析,结果表明:期刊论文网络下载频次分布均遵循负对数函数衰减,衰减速率由最高下载频次、篇均下载频次与载文量三个因素决定,并拟合出经验模型;期刊论文的网络下载频次与被引频次之间的线性相关性不稳定,不同期刊的差异较大,但总体上表现为显著的(中等)线性相关关系.从期刊层次考察,不同期刊的网络总下载量与总被引量为高度线性相关,网络篇均下载量与篇均被引量的线性相关性更强.     

基于SCI的河南农业大学科技论文统计分析

基础科学研究的发展实力和研究水平是衡量高校科技创新能力的重要指标。利用Web of Knowledge检索平台对2001年至2010年河南农业大学发表的SCI论文进行调查,从论文数量与类型、来源出版物、学科、合作机构和国家、论文作者、被引频次的角度进行统计,评价河南农业大学的科研绩效、学科发展水平和未来发展趋势,并在此基础上,创新性地提出了优化学术水平及科研绩效的措施和建议。     

Using patent citation patterns to infer innovation market competition

We propose an empirical strategy to estimate competition in innovation markets. Our method relates firms’ market return on equity to information about patent citation patterns. Two innovations are implemented in the methodology. First is the application of daily abnormal stock returns rather than annual measures of Tobin's q. Second is the creation of citation patterns related to the area of science a firm patents in as represented by the detailed patent classification system. We find that markets positively reward firms when patents are granted. We further find that firm's market value increases when its patent portfolio is cited. We find evidence of competition in innovation markets. The market reacts at the time that the citation occurs and does not anticipate future citations at the time of patenting. Holding this effect constant, we find that citations from patents in the same area of science tend to reduce market value. We interpret these findings as consistent with more citations indicating more valuable intellectual property but citations from competing technologies decreasing it.     

Bringing PageRank to the citation analysis

The paper attempts to provide an alternative method for measuring the importance of scientific papers based on the Google’s PageRank. The method is a meaningful extension of the common integer counting of citations and is then experimented for bringing PageRank to the citation analysis in a large citation network. It offers a more integrated picture of the publications’ influence in a specific field. We firstly calculate the PageRanks of scientific papers. The distributional characteristics and comparison with the traditionally used number of citations are then analyzed in detail. Furthermore, the PageRank is implemented in the evaluation of research influence for several countries in the field of Biochemistry and Molecular Biology during the time period of 2000–2005. Finally, some advantages of bringing PageRank to the citation analysis are concluded.     

Challenges of scholarly publications on the Web to the evaluation of science—A comparison of author visibility on the Web and in print journals

This article reveals different patterns of scholarly communication in the XML research field on the Web and in print journals in terms of author visibility, and challenges the common practice of exclusively using the ISI’s databases to obtain citation counts as scientific performance indicators. Results from this study demonstrate both the importance and the feasibility of the use of multiple citation data sources in citation analysis studies of scholarly communication, and provide evidence for a developing “two tier” scholarly communication system.     

Validation and interpretation of Web users’ sessions clusters

Understanding users’ navigation on the Web is important towards improving the quality of information and the speed of accessing large-scale Web data sources. Clustering of users’ navigation into sessions has been proposed in order to identify patterns and similarities which are then managed in the context of Web users oriented applications (searching, e-commerce, etc.). This paper deals with the problem of assessing the quality of user session clusters in order to make inferences regarding the users’ navigation behavior. A common model-based clustering algorithm is used to result in clusters of Web users’ sessions. These clusters are validated by using a statistical test, which measures the distances of the clusters’ distributions to infer their dissimilarity and distinguishing level. Furthermore, a visualization method is proposed in order to interpret the relation between clusters. Using real data sets, we illustrate how the proposed analysis can be applied in popular application scenarios to reveal valuable associations among Web users’ navigation sessions.     

Information management and improvement of citation indices

Bibliometrics and citation analysis have become important sets of methods for library and information science, as well as exceptional sources of information and knowledge for many other areas. Their main sources are citation indices, which are bibliographic databases like Web of Science, Scopus, Google Scholar, etc. However, bibliographical databases lack perfection and standardization. There are several software tools that perform useful information management and bibliometric analysis importing data from them. A comparison has been carried out to identify which of them perform certain pre-processing tasks. Usually, they are not strong enough to detect all the duplications, mistakes, misspellings and variant names, leaving to the user the tedious and time-consuming task of correcting the data. Furthermore, some of them do not import datasets from different citation indices, but mainly from Web of Science (WoS).A new software tool, called STICCI.eu (Software Tool for Improving and Converting Citation Indices – enhancing uniformity), which is freely available online, has been created to solve these problems. STICCI.eu is able to do conversions between bibliographical citation formats (WoS, Scopus, CSV, BibTex, RIS), correct the usual mistakes appearing in those databases, detect duplications, misspellings, etc., identify and transform the full or abbreviated titles of the journals, homogenize toponymical names of countries and relevant cities or regions and list the processed data in terms of the most cited authors, journals, references, etc.     

Semiautomatic determination of citation relevancy: A preliminary report

Technology transfer, research and development and engineering projects frequently require in-depth literature reviews. These reviews are carried out using computerized, bibliographic data bases. The review and/or searching process involves keywords selected from data base thesauri. The search strategy is formulated to provide both breadth and depth of coverage and yields both relevant and nonrelevant citations. Experience indicates that about 10–20% of the citations are relevant. As a consequence, significant amounts of time are required to eliminate the nonrelevant citations. This paper describes statistically based, lexical association methods which can be employed to determine citation relevance. In particular, the searcher selects relevant terms from citation-derived indexes and this information along with lexical statistics is used to determine citation relevance. Preliminary results are encouraging with the techniques providing an effective concentration of relevant citations.     

Methods to account for citation inflation in research evaluation

Quantitative research evaluation requires measures that are transparent, relatively simple, and free of disciplinary and temporal bias. We document and provide a solution to a hitherto unaddressed temporal bias – citation inflation – which arises from the basic fact that scientific publication is steadily growing at roughly 4% per year. Moreover, because the total production of citations grows by a factor of 2 every 12 years, this means that the real value of a citation depends on when it was produced. Consequently, failing to convert nominal citation values into real citation values produces significant mis-measurement of scientific impact. To address this problem, we develop a citation deflator method, outline the steps to generalize and implement it using the Web of Science portal, and analyze a large set of researchers from biology and physics to demonstrate how two common evaluation metrics – total citations and h-index – can differ by a remarkable amount depending on whether the underlying citation counts are deflated or not. In particular, our results show that the scientific impact of prior generations is likely to be significantly underestimated when citations are not deflated, often by 100% or more of the nominal value. Thus, our study points to the need for a systemic overhaul of the counting methods used evaluating citation impact – especially in the case of researchers, journals, and institutions – which can span several decades and thus several doubling periods.     

How status of research papers affects the way they are read and cited

Although citations are widely used to measure the influence of scientific works, research shows that many citations serve rhetorical functions and reflect little-to-no influence on the citing authors. If highly cited papers disproportionately attract rhetorical citations then their citation counts may reflect rhetorical usefulness more than influence. Alternatively, researchers may perceive highly cited papers to be of higher quality and invest more effort into reading them, leading to disproportionately substantive citations. We test these arguments using data on 17,154 randomly sampled citations collected via surveys from 9,380 corresponding authors in 15 fields. We find that most citations (54%) had little-to-no influence on the citing authors. However, citations to the most highly cited papers were 2–3 times more likely to denote substantial influence. Experimental and correlational data show a key mechanism: displaying low citation counts lowers perceptions of a paper's quality, and papers with poor perceived quality are read more superficially. The results suggest that higher citation counts lead to more meaningful engagement from readers and, consequently, the most highly cited papers influence the research frontier much more than their raw citation counts imply.     

Quantifying revolutionary discoveries: Evidence from Nobel prize-winning papers

Numerous metrics have been developed to identify revolutionary science which is crucial for advancing science. However, these metrics have rarely successfully identified revolutionary discoveries. We propose a two-dimension metric to quantify revolutionary discoveries by combining the consolidation-or-destabilization (CD) index with the citation count. To verify the validity of the metric, we utilize multivariate linear regression to investigate the differences in the CD indices and citations between 164 Nobel prize-winning papers from 1976 to 2016 (i.e., revolutionary science) and 9,034 counterparts that are similar to the Nobel prize-winning papers in terms of bibliographic information. We find that our proposed metric successfully shows a significant and distinct difference between the Nobel prize-winning papers and their counterparts in that the former receive around 880 more citations and 0.07 higher CD indices than the latter. The reliability of our proposed measure is robust.