Future Citations Research Articles

Highly visible sepsis publications from 2012 to 2017: Analysis and comparison of altmetrics and bibliometrics

Purpose: We sought to delineate highly visible publications related to sepsis. Within these subsets, elements of altmetrics performance, including mentions on Twitter, and the correlation between altmetrics and conventional citation counts were ascertained.Materials and Methods: Three subsets of sepsis publications from 2012 to 2017 were synthesized by the overall Altmetric.com attention score, number of mentions by unique Twitter users, and conventional citation counts. For these subsets, geolocated Twitter activity was plotted on a choropleth, the lag between publication date and altmetrics mentions was characterized, and correlations were examined between altmetrics performance and normalized conventional citation counts.Results: Of 57,152 PubMed query results, Altmetric.com data was available for 28,344 (49.6%). The top 50 publications by Altmetric.com attention score and Twitter attention represented a mix of original research and other types of work, garnering attention from Twitter users in 143 countries that was highly contemporaneous with publication. Altmetrics performance and conventional citation counts were poorly correlated.Conclusions: While unreliable to gauge impact or future citation potential, altmetrics may be valuable for parties who wish to detect and drive public awareness of research findings and may enable researchers to dynamically explore the reach of their work in novel dimensions.

Firms’ innovation strategy under the shadow of analyst coverage

We study the effect of analyst coverage on firms’ innovation strategy and outcome. Using data of US firms from 1990 to 2012, we find evidence that an increase in financial analysts leads firms to cut research and development expenses, acquire more innovative firms, and invest in corporate venture capital. We attribute the first result to the effect of analyst pressure and the others to the informational role of analysts. We also find that financial analysts encourage firms to make more efficient investments related to innovation, which increases their future patents and citations and influences the novelty of their innovations.

Detecting trends in academic research from a citation network using network representation learning.

Several network features and information retrieval methods have been proposed to elucidate the structure of citation networks and to detect important nodes. However, it is difficult to retrieve information related to trends in an academic field and to detect cutting-edge areas from the citation network. In this paper, we propose a novel framework that detects the trend as the growth direction of a citation network using network representation learning(NRL). We presume that the linear growth of citation network in latent space obtained by NRL is the result of the iterative edge additional process of a citation network. On APS datasets and papers of some domains of the Web of Science, we confirm the existence of trends by observing that an academic field grows in a specific direction linearly in latent space. Next, we calculate each node’s degree of trend-following as an indicator called the intrinsic publication year (IPY). As a result, there is a correlation between the indicator and the number of future citations. Furthermore, a word frequently used in the abstracts of cutting-edge papers (high-IPY paper) is likely to be used often in future publications. These results confirm the validity of the detected trend for predicting citation network growth.

An examination of the effect of open versus paywalled access publication on the disseminative impact and citation count of publications in intensive care medicine and anesthesia

PurposeWe aimed to assess the impact of open access (OA) versus paywalled access (PA) publication on Altmetric Attention Scores (AAS) and whether AAS correlates with future citation count access in the context of intensive care medicine (ICM) and anesthesia. Methods1854 and 2935 publications, in the year 2015, were identified in ICM and anesthesia respectively, using a Pubmed search. The mean AAS was measured for each article. ResultsMore ICM articles were OA, compared to of anesthesia articles (38.9% v 35.0% p = 0.02). The mean AAS for OA ICM publications was significantly higher than that of PA ICM publications (17.34 vs 8.45, p < 0.01), however, this was not observed when examined in a fixed follow up time frame. AAS appear to correlate with future citation counts. ConclusionsICM publications that are available as OA in the medium term result in higher AAS when compared to PA publications, this phenomenon was not observed in anesthesia. AAS correlate with future citation counts, however, a larger study is required to confirm this.

Novelty and Foreseeing Research Trends: The Case of Astrophysics and Astronomy

Abstract Metrics based on reference lists of research articles or on keywords have been used to predict citation impact. The concept behind such metrics is that original ideas stem from the reconfiguration of the structure of past knowledge, and therefore atypical combinations in the reference lists, keywords, or classification codes indicate future high-impact research. The current paper serves as an introduction to this line of research for astronomers and also addresses some of the methodological questions in this field of innovation studies. It is still not clear if the choice of particular indexes, such as references to journals, articles, or specific bibliometric classification codes affects the relationship between atypical combinations and citation impact. To understand more aspects of the innovation process, a new metric has been devised to measure to what extent researchers are able to anticipate the changing combinatorial trends of the future. Results show that the variant of the latter anticipation scores that is based on paper combinations is a good predictor of the future citation impact of scholarly works. The study also shows that the effects of tested indexes vary with the aggregation levels that were used to construct them. A detailed analysis of combinatorial novelty in the field reveals that certain sub-fields of astronomy and astrophysics have different roles in the reconfiguration of past knowledge.

On the differences between citations and altmetrics: An investigation of factors driving altmetrics versus citations for finnish articles

This study examines a range of factors associated with future citation and altmetric counts to a paper. The factors include journal impact factor, individual collaboration, international collaboration, institution prestige, country prestige, research funding, abstract readability, abstract length, title length, number of cited references, field size, and field type and will be modeled in association with citation counts, Mendeley readers, Twitter posts, Facebook posts, blog posts, and news posts. The results demonstrate that eight factors are important for increased citation counts, seven different factors are important for increased Mendeley readers, eight factors are important for increased Twitter posts, three factors are important for increased Facebook posts, six factors are important for increased blog posts, and five factors are important for increased news posts. Journal impact factor and international collaboration are the two factors that significantly associate with increased citation counts and with all altmetric scores. Moreover, it seems that the factors driving Mendeley readership are similar to those driving citation counts. However, the altmetric events differ from each other in terms of a small number of factors; for instance, institution prestige and country prestige associate with increased Mendeley readers and blog and news posts, but it is an insignificant factor for Twitter and Facebook posts. The findings contribute to the continued development of theoretical models and methodological developments associated with capturing, interpreting, and understanding altmetric events.

Bending the law: geometric tools for quantifying influence in the multinetwork of legal opinions

Legal reasoning requires identification through search of authoritative legal texts (such as statutes, constitutions, or prior judicial opinions) that apply to a given legal question. In this paper, using a network representation of US Supreme Court opinions that integrates citation connectivity and topical similarity, we model the activity of law search as an organizing principle in the evolution of the corpus of legal texts. The network model and (parametrized) probabilistic search behavior generates a Pagerank-style ranking of the texts that in turn gives rise to a natural geometry of the opinion corpus. This enables us to then measure the ways in which new judicial opinions affect the topography of the network and its future evolution. While we deploy it here on the US Supreme Court opinion corpus, there are obvious extensions to large evolving bodies of legal text (or text corpora in general). The model is a proxy for the way in which new opinions influence the search behavior of litigants and judges and thus affect the law. This type of “legal search effect” is a new legal consequence of research practice that has not been previously identified in jurisprudential thought and has never before been subject to empirical analysis. We quantitatively estimate the extent of this effect and find significant relationships between search-related network structures and propensity of future citation. This finding indicates that “search influence” is a pathway through which judicial opinions can affect future legal development.

Could scientists use Altmetric.com scores to predict longer term citation counts?

Altmetrics from Altmetric.com are widely used by publishers and researchers to give earlier evidence of attention than citation counts. This article assesses whether Altmetric.com scores are reliable early indicators of likely future impact and whether they may also reflect non-scholarly impacts. A preliminary factor analysis suggests that the main altmetric indicator of scholarly impact is Mendeley reader counts, with weaker news, informational and social network discussion/promotion dimensions in some fields. Based on a regression analysis of Altmetric.com data from November 2015 and Scopus citation counts from October 2017 for articles in 30 narrow fields, only Mendeley reader counts are consistent predictors of future citation impact. Most other Altmetric.com scores can help predict future impact in some fields. Overall, the results confirm that early Altmetric.com scores can predict later citation counts, although less well than journal impact factors, and the optimal strategy is to consider both Altmetric.com scores and journal impact factors. Altmetric.com scores can also reflect dimensions of non-scholarly impact in some fields.

Tweeting birds: online mentions predict future citations in ornithology.

The rapid growth of online tools to communicate scientific research raises the important question of whether online attention is associated with citations in the scholarly literature. The Altmetric Attention Score (AAS) quantifies the attention received by a scientific publication on various online platforms including news, blogs and social media. It has been advanced as a rapid way of gauging the impact of a piece of research, both in terms of potential future scholarly citations and wider online engagement. Here, we explore variation in the AAS of 2677 research articles published in 10 ornithological journals between 2012 and 2016. On average, AAS increased sevenfold in just five years, primarily due to increased activity on Twitter which contributed 75% of the total score. For a subset of 878 articles published in 2014, including an additional 323 ornithology articles from non-specialist journals, an increase in AAS from 1 to 20 resulted in a predicted 112% increase in citation count from 2.6 to 5.5 citations per article. This effect interacted with journal impact factor, with weaker effects of AAS in higher impact factor journals. Our results suggest that altmetrics (or the online activity they measure), as well as complementing traditional measures of scholarly impact in ornithology such as citations, may also anticipate or even drive them.

Reviewer interest in a manuscript may predict its future citation potential

Previous studies have investigated the relationship between editorial delay and eventual citations, and generally have found that longer editorial delays are related to fewer future citations. There are many reasons that editorial delay can occur, one such reason is the inability to find willing and able peer reviewers. While a previous study found that the most common reason for reviewers to decline is due to time constraints, it is unclear how reviewer interest and the potential to be cited are related. Here, we examined 4 years (2010–2013) of manuscripts submitted to the Bulletin of Marine Science and paired accepted articles with subsequent citation data. Our aim was to investigate: (1) the relationship between number of reviewer invitations and probability of acceptance, and (2) the limitation that number of reviewer invitations may have on citation rates (citations per year). Our findings suggest that the number of reviewer invitations sent is not related to the probability of manuscript acceptance. However, we found that the number of reviewer invitations sent may signal a limit on the number of citations per year. After seven reviewer invitations, there was a substantial drop in the potential of higher citation rates. We hypothesize that reviewer interest in a manuscript may serve as a predictor of the potential citations an article will receive, and could be one of many mechanisms behind the editorial delay–eventual citation relationship. Additional research is needed to see if these patterns hold true over longer time periods and for other journal titles and fields.

Online distribution channel increases article usage on Mendeley: a randomized controlled trial

Prior research shows that article reader counts (i.e. saves) on the online reference manager, Mendeley, correlate to future citations. There are currently no evidenced-based distribution strategies that have been shown to increase article saves on Mendeley. We conducted a 4-week randomized controlled trial to examine how promotion of article links in a novel online cross-publisher distribution channel (TrendMD) affect article saves on Mendeley. Four hundred articles published in the Journal of Medical Internet Research were randomized to either the TrendMD arm (n = 200) or the control arm (n = 200) of the study. Our primary outcome compares the 4-week mean Mendeley saves of articles randomized to TrendMD versus control. Articles randomized to TrendMD showed a 77% increase in article saves on Mendeley relative to control. The difference in mean Mendeley saves for TrendMD articles versus control was 2.7, 95% CI (2.63, 2.77), and statistically significant (p < 0.01). There was a positive correlation between pageviews driven by TrendMD and article saves on Mendeley (Spearman’s rho r = 0.60). This is the first randomized controlled trial to show how an online cross-publisher distribution channel (TrendMD) enhances article saves on Mendeley. While replication and further study are needed, these data suggest that cross-publisher article recommendations via TrendMD may enhance citations of scholarly articles.

Detecting latent referential articles based on their vitality performance in the latest 2 years

In this paper, we propose a methodology to detect latent referential articles through a universal, citation-based investigation. We discuss articles’ dynamic vitality performance, concealed in their citation distributions, in order to understand the mechanisms that govern which articles are likely to be referenced in the future. Articles have diverse vitality performances expressed in the amount of citations obtained in different time periods. Through an examination of the correlation between articles’ future citation count and their past citations, we establish the optimal time period during which it is best to forecast articles’ future referential possibilities. The results show that the latest 2 years is the optimal time period. In other words, the correlation between the articles’ future citation count and their past citation count reaches a maximum value in the most recent 2-year period. The articles with a higher vitality performance in the most recent 2 years have a higher ratio of being cited as references in the future. These results help, not only, in understanding mechanisms of generating references, but also provide an additional indicator for decision makers to evaluate the academic performance of individuals according to their citation performance in the latest 2 years.

Section-wise indexing and retrieval of research articles

Relevant information extraction is a dire need of the scholarly community. There are a number of systems available to find relevant information from scientific literature such as search engines, citation indexes, digital libraries etc. For a search query, a long list of irrelevant documents is presented to the users mainly due to the huge number of availability of the full-text document, and furthermore due to the unstructured nature of indexed scientific resources. The contemporary systems have formally defined the structure of scientific documents. However, populating the already available enriched scientific structure from unstructured/semi-structured scientific documents has not been addressed previously. In this research paper, we have designed, implemented, and evaluated an automated technique that is able to tag each paper’s content with logical sections appearing in the scientific document. The proposed system has been evaluated against the benchmark, subsequently, the proposed system have been also compared with machine learning techniques that may be used for the same task. It has been empirically shown that the overall correctness and completeness of our proposed technique is 0.78 and 0.79 respectively and thus the overall accuracy of about 0.78 was achieved. The achieved results are good as compared to machine learning based classification. The developed system may help future information retrieval systems, digital libraries, and citation indexes to index, retrieve, rank and visualize most relevant scientific documents for the scientific community.

The Impacts of Authorship on the Future Citations of Conference Articles in `Information Science` Field

The Impacts of Authorship on the Future Citations of Conference Articles in `Information Science` Field

Hawkes process-based technology impact analysis

Abstract Patent citation analysis is considered a useful tool for technology impact analysis. However, the outcomes of previous methods do not provide a fair reflection of a technology’s future prospects since they are based on deterministic approaches, assuming that future trends will remain the same as those in the past. As a remedy, we propose a Hawkes process-based patent citation analysis method to assess the future technological impact and uncertainty of a technology in a time period of interest by employing the future citation counts of the relevant patents as a quantitative proxy. For this, we construct a citation interval matrix from the United States Patent and Trademark Office (USPTO) database, and employ a Hawkes process - a special case of path-dependent stochastic processes - as a method for patent citation forecasting. Specifically, the Hawkes process models the idiosyncratic and dynamic behaviours of a technology’s evolution and obsolescence by increasing the likelihood of another subsequent citation by oneself (i.e., self-excitation) and decaying the likelihood back towards the initial level naturally. A case study of the patents about molecular amplification diagnosis technology shows that our method outperforms previous deterministic approaches in terms of accuracy and practicality.

Firms' Innovation Strategy under the Shadow of Analyst Coverage

We study the effect of analyst coverage on firms’ innovation strategy and outcome. By considering three different channels that allow firms to innovate: internal RD however, this negative effect becomes not significant when firms’ in-house R&D spending and external innovation channels are taken into account. We find that more financial analysts encourage firms to make more efficient investments related to innovation, which increase their future patents and citations. We address endogeneity with an instrumental variables approach and a difference-in-differences strategy where exogenous variation in analyst coverage comes from brokerage house mergers.

Independent boards and innovation

Much research has suggested that independent boards of directors are more effective in reducing agency costs and improving firm governance. How they influence innovation is less clear. Relying on regulatory changes, we show that firms that transition to independent boards focus on more crowded and familiar areas of technology. They patent and claim more and receive more total future citations to their patents. However, the citation increase comes mainly from incremental patents in the middle of the citation distribution; the numbers of uncited and highly cited patents—arguably associated with riskier innovation strategies—do not change significantly.

Path analysis of the relationship between visibility and citation: the mediating roles of save, discussion, and recommendation metrics

This study aimed to assess the mediating role of save, discussion, and recommendation measures in the relationship between visibility and citation in biomedical articles in 2009---2013. Path analysis method was used to assess the causal relationships between the variables in this descriptive correlational study. Systematic and random stratified methods were employed for sampling. The sample size was determined to be 1892 articles using the Cochrane formula and data were gathered by using the PLOS altmetrics. The study's model fit indices showed that visibility influences citation both directly and indirectly through the mediating role of save. Discussion had a significant, negative role in the relationship between visibility and citation, and recommendation did not have any significant mediating role in this relationship. Among the social networks presenting altmetrics, it seems that networks such as Mendeley which provide a basis for saving scientific articles have an important and significant effect on the amount of future citations through visibility metrics. This is while social networks discussing scientific findings have a negative effect on the future citation of articles through visibility metrics. This asserts that social networks based on save have an influential role as the basis of scientific interaction.

Does high impact factor successfully predict future citations? An analysis using Peirce’s measure

Journals are routinely evaluated by journal impact factors. However, more controversially, these same impact factors are often used to evaluate authors and groups as well. A more meaningful approach will be to use actual citation rates. Since in each journal there is a very highly skewed distribution of articles according to citation rates, there is little correlation between journal impact factor and actual citation rate of articles from individual scientists or research groups. Simply stated, journal impact factor does not successfully predict high citations in future. In this paper, we propose the use of Peirce's measure of predictive success (Peirce in Science 4(93):453---454, 1884) to see if the use of journal impact factors to predict high citation rates is acceptable or not. It is seen that this measure is independent of Pearson's correlation (Seglen 1997) and gives a more quantitative refinement of the Type I and Type II classification of Smith (Financ Manag 133---149, 2004). The measures are used to examine the portfolios of some active scientists. It is clear that the journal impact factor is not effective in predicting future citations of successful authors.

Two citation-based indicators to measure latent referential value of papers

Traditional bibliometric indicators aim at helping academic administrators or research investors measure the influence of publications. These indicators focus on how to quantify and compare the scientific output of researchers. However, little attention has been paid to the aspect that bibliometric indicators can also be used to help scientists find valuable referential papers. In this paper, we propose three points to characterize valuable referential papers: first, valuable referential papers always are high-quality research; second, valuable referential papers are closely related to a considerable quantity of recent papers; third, valuable referential papers lead to hotspots which attract papers to follow successively. We extract the critical subnetwork from the original citation network which only reserves the significant nodes and edges that meet the three preceding points. Then we present two indicators on the basis of the critical subnetwork. The experimental results demonstrate that papers recommended by our indicators are relatively new and our indicators have greater Spearman's rank correlation coefficients with the future citation count compared with other bibliometric indicators like the raw citation count.