What&;#39;s in a title? Numbers of words and the presence of colons

Summary Much has been written about titles in scientific journal articles but little research has been carried out. We aimed to assess in two studies how factors like the length of a title and its structure might vary in different scientific fields, and whether or not these features have changed over time. Statistical analyses were made of 216,500 UK papers from science journals, and of 133,200 international oncology papers. Factors examined included title length, the use of colons in the titles, and the number of authors. All of these factors increased over time for both sets of papers, although there were some disciplinary differences in the findings. In both studies, titles with colons occurred more frequently with single than with multiple authors except when the numbers of co-authors were large. Certain features of titles can be related to different disciplines, different journals, the numbers of authors and their nationalities.     

A diachronic comparative study of research article titles in linguistics and literature journals

By investigating a total number of 1364 article titles extracted from seven SSCI-indexed linguistics journals and seven A&HCI-indexed literature journals between 1980 and 2018, this study attempts to present the prominent form features and content attributes of the titles and their diachronic variations, as well as the similarities or differences between the two disciplines regarding title features. Results show that article titles in both subject domains demonstrate consistency in the long-range trend of title length and informativity, enjoying a steady and linear growth in length and an increase with fluctuations in informativity. Nominal and compound structures are both frequently adopted in linguistics and literature journals, but the proportion of compound constructions has always been higher than that of nominal ones in literature journals over the past 39 years. Titles containing topical information enjoy the highest percentage from the 1980s to the 2010s in both linguistics and literature journals. However, in the recent decade, content information provided by linguistic article titles tends to be more diversified, presenting not only research topics, but also results, dataset and methods of the study. No significant variations between the two disciplines have been found in lexical diversity, both of which experienced an overall downward trend.

     

A lexical and syntactic study of research article titles in Library Science and Scientometrics

Title of a research article is an abstract of the abstract. Titles play a decisive role in convincing readers at first sight whether articles are worth reading or not. Not only do research article titles show how carefully words are chosen by authors, but also reflect disciplinary differences in terms of title words and structure between hard sciences and soft sciences. This study examined the lexical density and syntactic structure of 690 research article titles chosen from five Library Science and Scientometrics journals, aiming to reveal disciplinary differences. The result suggested both Library Science and Scientometrics have almost the same title length and the prevalent usage of Nominal Phrase (NP) to govern the title structure. The result also stated some disciplinary differences: Library Science demonstrates more punctuation complexity, particularly a greater frequency in using colons; but Scientometrics shows more involvement of words related to research methods, which is an indicator to papers’ scientific value, and more usage of declarative Full Sentence (FS) structure, which were mostly discovered in the research articles in hard sciences.

     

Scholarly communication in transition: The use of question marks in the titles of scientific articles in medicine, life sciences and physics 1966–2005

The titles of scientific articles have a special significance. We examined nearly 20 million scientific articles and recorded the development of articles with a question mark at the end of their titles over the last 40 years. Our study was confined to the disciplines of physics, life sciences and medicine, where we found a significant increase from 50% to more than 200% in the number of articles with question-mark titles. We looked at the principle functions and structure of the titles of scientific papers, and we assume that marketing aspects are one of the decisive factors behind the growing usage of question-mark titles in scientific articles.     

A review of operations management literature: a data-driven approach

Production and operations management has been a significant field of research for many years. However, other than an educated guess by researchers in the field or a perusal of textbook chapter titles, the major topics and their trends over time are not well established. This study provides a comprehensive review of production and operations management literature using a data-driven approach. We use Latent Semantic Analysis on 21,053 abstracts representing all publications in six leading operations management journals since their inception. 18 unique topic clusters were identified algorithmically. Just being aware of the history of research topics should be of great interest to all academics in the field, but to help future researchers we conducted three post hoc analyses: 1) analysis of methods used in all these studies, 2) citation rates by topic area over time, and 3) the growing prevalence of research covering multiple topics.     

Capturing new developments in an emerging technology: an updated search strategy for identifying nanotechnology research outputs

Bibliometric analysis of publication metadata is an important tool for investigating emerging fields of technology. However, the application of field definitions to define an emerging technology is complicated by ongoing and at times rapid change in the underlying technology itself. There is limited prior work on adapting the bibliometric definitions of emerging technologies as these technologies change over time. The paper addresses this gap. We draw on the example of the modular keyword nanotechnology search strategy developed at Georgia Institute of Technology in 2006. This search approach has seen extensive use in analyzing emerging trends in nanotechnology research and innovation. Yet with the growth of the nanotechnology field, novel materials, particles, technologies, and tools have appeared. We report on the process and results of reviewing and updating this nanotechnology search strategy. By employing structured text-mining software to profile keyword terms, and by soliciting input from domain experts, we identify new nanotechnology-related keywords. We retroactively apply the revised evolutionary lexical query to 20 years of publication data and analyze the results. Our findings indicate that the updated search approach offers an incremental improvement over the original strategy in terms of recall and precision. Additionally, the updated strategy reveals the importance for nanotechnology of several emerging cited-subject categories, particularly in the biomedical sciences, suggesting a further extension of the nanotechnology knowledge domain. The implications of the work for applying bibliometric definitions to emerging technologies are discussed.     

The contribution of the lexical component in hybrid clustering,the case of four decades of “Scientometrics”

The introduction of textual analysis and the use of lexical similarities already proved an important asset in science mapping. Earlier research showed the added value of hybrid document networks over link-based ones through the reduction of the extreme sparseness. However, it was only after the application of Natural Language Processing and phrase extraction that networks purely based on lexical similarities could be used as input for topic detection in quantitative science studies. This study investigates the contribution of the lexical component in hybrid cluster on a set of articles published in the journal Scientometrics since its foundation during four decades. Shifting the weight of the lexical components generates changes in the structure of the underlying hybrid network, which can be detected through clustering techniques. We show that these changes are not moving documents randomly, but in fact identify small groups of papers either at the borderline between different topics or combining those. In addition, the analysis substantiates that the lexical component adopts the structure of the network rather than amplifies hidden structures of the link-based network.     

A patent search strategy based on machine learning for the emerging field of service robotics

Emerging technologies are often not part of any official industry, patent or trademark classification systems. Thus, delineating boundaries to measure their early development stage is a nontrivial task. This paper is aimed to present a methodology to automatically classify patents concerning service robots. We introduce a synergy of a traditional technology identification process, namely keyword extraction and verification by an expert community, with a machine learning algorithm. The result is a novel possibility to allocate patents which (1) reduces expert bias regarding vested interests on lexical query methods, (2) avoids problems with citation approaches, and (3) facilitates evolutionary changes. Based upon a small core set of worldwide service robotics patent applications, we derive apt n-gram frequency vectors and train a support vector machine, relying only on titles, abstracts, and IPC categorization of each document. Altering the utilized Kernel functions and respective parameters, we reach a recall level of 83% and precision level of 85%.     

A structural topic model approach to scientific reorientation of economics and chemistry after German reunification

The detection of differences or similarities in large numbers of scientific publications is an open problem in scientometric research. In this paper we therefore develop and apply a machine learning approach based on structural topic modelling in combination with cosine similarity and a linear regression framework in order to identify differences in dissertation titles written at East and West German universities before and after German reunification. German reunification and its surrounding time period is used because it provides a structure with both minor and major differences in research topics that could be detected by our approach. Our dataset is based on dissertation titles in economics and business administration and chemistry from 1980 to 2010. We use university affiliation and year of the dissertation to train a structural topic model and then test the model on a set of unseen dissertation titles. Subsequently, we compare the resulting topic distribution of each title to every other title with cosine similarity. The cosine similarities and the regional and temporal origin of the dissertation titles they come from are then used in a linear regression approach. Our results on research topics in economics and business administration suggest substantial differences between East and West Germany before the reunification and a rapid conformation thereafter. In chemistry we observe minor differences between East and West before the reunification and a slightly increased similarity thereafter.

     

Avoiding obscure topics and generalising findings produces higher impact research

Much academic research is never cited and may be rarely read, indicating wasted effort from the authors, referees and publishers. One reason that an article could be ignored is that its topic is, or appears to be, too obscure to be of wide interest, even if excellent scholarship produced it. This paper reports a word frequency analysis of 874,411 English article titles from 18 different Scopus natural, formal, life and health sciences categories 2009–2015 to assess the likelihood that research on obscure (rarely researched) topics is less cited. In all categories examined, unusual words in article titles associate with below average citation impact research. Thus, researchers considering obscure topics may wish to reconsider, generalise their study, or to choose a title that reflects the wider lessons that can be drawn. Authors should also consider including multiple concepts and purposes within their titles in order to attract a wider audience.     

Relation of the title length of a journal article to the length of the article

The great importance of titles being highly informative is almost unanimously accepted in literature, assuming that the more informative titles are, the more effectively they serve their functions. The most common measure of title “informativeness” has been the number of “significant” (i.e., non-trivial) words included in it, and one of the factors which might be associated with it is the length of the paper, measured by its number of pages. The present study attempted to test, in a large group of journals from different areas and over six decades, the hypothesis that a paper with more pages will have more “significant” words in its title. Large samples of original research papers were drawn from each decade year of twenty-four leading journals selected from the sciences, the social sciences and the humanities. For each paper, the number of “significant” words in the title was correlated with the number of pages. Findings indicate a difference between the scientific journals on the one hand, and the social sciences and humanities journals on the other. A moderate positive correlation was found in most scientific journals for many periods. In the social sciences journals, and to a greater extent, in the humanities journals, a significant positive correlation was limited to only a few periods, while the rest showed a very low correlation, or even a negative one. The different findings for the sciences are perhaps attributable to their unique inherent features. This revised version was published online in August 2006 with corrections to the Cover Date.     

Delineation of the genomics field by hybrid citation-lexical methods: interaction with experts and validation process

In advanced methods of delineation and mapping of scientific fields, hybrid methods open a promising path to the capitalisation of advantages of approaches based on words and citations. One way to validate the hybrid approaches is to work in cooperation with experts of the fields under scrutiny. We report here an experiment in the field of genomics, where a corpus of documents has been built by a hybrid citation-lexical method, and then clustered into research themes. Experts of the field were associated in the various stages of the process: lexical queries for building the initial set of documents, the seed; citation-based extension aiming at reducing silence; final clustering to identify noise and allow discussion on border areas. The analysis of experts’ advices show a high level of validation of the process, which combines a high-precision and low-recall seed, obtained by journal and lexical queries, and a citation-based extension enhancing the recall. This findings on the genomics field suggest that hybrid methods can efficiently retrieve a corpus of relevant literature, even in complex and emerging fields.     

Succinct effect or informative effect: the relationship between title length and the number of citations

Previous studies produced mixed results with respect to the correlation between title length and number of citations. This research aims to provide a new explanation for this controversy by examining one of the largest pools of papers to date, containing over 300,000 economics papers spanning a much longer time period (1956–2012). The results show that correlation between title length and the number of citations is negative between 1956 and 2000, but becomes positive after 2000, when online searches became the predominant method for literature retrieval. Moreover, heterogeneity analyses show that longer titles are especially crucial for papers with relatively lower influence, which researchers can typically only access using online searches.     

Collaboration network patterns and research performance: the case of Korean public research institutions

This study examines the impact of collaborating patterns on the R&;D performance of public research institutions (PRIs) in Korea’s science and engineering fields. For the construction of R&;D collaborating networks based on the co-authorship data of 127 institutions in Scopus, this paper proposes four types of collaborations by categorizing network analyses into two dimensions: structural positions (density, efficiency, and betweeness centrality) and the relational characteristics of individual nodes (eigenvector and closeness centralities). To explore the research performance by collaboration type, we employ a data envelopment analysis window analysis of a panel of 23 PRIs over a 10-year period. Comparing the R&;D productivities of each group, we find that the PRIs of higher productivity adhere to a cohesive networking strategy, retaining intensive relations with their existing partners. The empirical results suggest that excessively cohesive alliances might end up in ‘lock-in’ relations, hindering the exploitation of new opportunities for innovation. These findings are implicit in relation to the Korean Government’s R&;D policies on collaborating strategies to produce sustained research results with the advent of the convergence research era.     

Detecting bursty terms in computer science research

Research topics rise and fall in popularity over time, some more swiftly than others. The fastest rising topics are typically called bursts; for example “deep learning”, “internet of things” and “big data”. Being able to automatically detect and track bursty terms in the literature could give insight into how scientific thought evolves over time. In this paper, we take a trend detection algorithm from stock market analysis and apply it to over 30 years of computer science research abstracts, treating the prevalence of each term in the dataset like the price of a stock. Unlike previous work in this domain, we use the free text of abstracts and titles, resulting in a finer-grained analysis. We report a list of bursty terms, and then use historical data to build a classifier to predict whether they will rise or fall in popularity in the future, obtaining accuracy in the region of 80%. The proposed methodology can be applied to any time-ordered collection of text to yield past and present bursty terms and predict their probable fate.

     

Global knowledge management research: A bibliometric analysis

The present study characterizes the dynamic publication activity of global knowledge management (KM) by data collected through a search restricted to articles in ISI Web of Science.A total of 2727 unique authors had contributed 1407 publications since 1975. The overwhelming majority (2349 or 86%) of them wrote one publication. The productive authors, their contribution and authorship position are listed to indicate their productivity and degree of involvement in their research publications. The sum of research output of the first or responsible authors from USA, UK and Germany reaches 57% of the total productivity. The distribution of articles is rather widespread - they published in 462 titles of serials, spanning 110 Journal Citation Reports subject categories. The higher quality journals make publication of findings more visible. A Pearson's correlation coefficient is statistically found to be significant between citation frequency of article and impact factor of journal, instead of authorship pattern. The results also indicate that R&D expenditures were actually not proportional to research productivity or citation counts. As the subject highly interacts with other disciplines, the field of KM has not yet developed its own body of literature. KM might have been evolving an interdisciplinary theory that is developing at the boundaries of scientific disciplines. This revised version was published online in June 2006 with corrections to the Cover Date.     

The evolution of research article titles: the case of Journal of Pragmatics 1978–2018

Scientometrics - Previous studies seldom investigated the evolution of research article titles in pragmatics. Based on qualitative and quantitative analysis of 650 research article titles, this...     

On the impact of Gold Open Access journals

Gold Open Access (=Open Access publishing) is for many the preferred route to achieve unrestricted and immediate access to research output. However, true Gold Open Access journals are still outnumbered by traditional journals. Moreover availability of Gold OA journals differs from discipline to discipline and often leaves scientists concerned about the impact of these existent titles. This study identified the current set of Gold Open Access journals featuring a Journal Impact Factor (JIF) by means of Ulrichsweb, Directory of Open Access Journals and Journal Citation Reports (JCR). The results were analyzed regarding disciplines, countries, quartiles of the JIF distribution in JCR and publishers. Furthermore the temporal impact evolution was studied for a Top 50 titles list (according to JIF) by means of Journal Impact Factor, SJR and SNIP in the time interval 2000–2010. The identified top Gold Open Access journals proved to be well-established and their impact is generally increasing for all the analyzed indicators. The majority of JCR-indexed OA journals can be assigned to Life Sciences and Medicine. The success-rate for JCR inclusion differs from country to country and is often inversely proportional to the number of national OA journal titles. Compiling a list of JCR-indexed OA journals is a cumbersome task that can only be achieved with non-Thomson Reuters data sources. A corresponding automated feature to produce current lists “on the fly” would be desirable in JCR in order to conveniently track the impact evolution of Gold OA journals.     

Claim-based patent indicators: A novel approach to analyze patent content and monitor technological advances

This paper proposes a new method for developing patent indicators by text mining patent claims according to their drafting structure. We apply the method on nanocellulose as a case of study, although any subject could be the target of investigation. The results show that patent claims are a more reliable source of key terms to develop technical indicators than, for example, patent titles and abstracts. Indicators from patent claims in combination with other traditional indicators developed from bibliographic patent data may contribute significantly to the analytical process of technological forecasting, monitoring and competitive intelligence studies.