Search (46 results, page 1 of 3)

  • × year_i:[2020 TO 2030}
  • × theme_ss:"Informetrie"
  1. Wang, S.; Ma, Y.; Mao, J.; Bai, Y.; Liang, Z.; Li, G.: Quantifying scientific breakthroughs by a novel disruption indicator based on knowledge entities : On the rise of scrape-and-report scholarship in online reviews research (2023) 0.03 
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    Abstract
    Compared to previous studies that generally detect scientific breakthroughs based on citation patterns, this article proposes a knowledge entity-based disruption indicator by quantifying the change of knowledge directly created and inspired by scientific breakthroughs to their evolutionary trajectories. Two groups of analytic units, including MeSH terms and their co-occurrences, are employed independently by the indicator to measure the change of knowledge. The effectiveness of the proposed indicators was evaluated against the four datasets of scientific breakthroughs derived from four recognition trials. In terms of identifying scientific breakthroughs, the proposed disruption indicator based on MeSH co-occurrences outperforms that based on MeSH terms and three earlier citation-based disruption indicators. It is also shown that in our indicator, measuring the change of knowledge inspired by the focal paper in its evolutionary trajectory is a larger contributor than measuring the change created by the focal paper. Our study not only offers empirical insights into conceptual understanding of scientific breakthroughs but also provides practical disruption indicator for scientists and science management agencies searching for valuable research.
    Date
    22. 1.2023 18:37:33
  2. Lorentzen, D.G.: Bridging polarised Twitter discussions : the interactions of the users in the middle (2021) 0.03 
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    Abstract
    Purpose The purpose of the paper is to analyse the interactions of bridging users in Twitter discussions about vaccination. Design/methodology/approach Conversational threads were collected through filtering the Twitter stream using keywords and the most active participants in the conversations. Following data collection and anonymisation of tweets and user profiles, a retweet network was created to find users bridging the main clusters. Four conversations were selected, ranging from 456 to 1,983 tweets long, and then analysed through content analysis. Findings Although different opinions met in the discussions, a consensus was rarely built. Many sub-threads involved insults and criticism, and participants seemed not interested in shifting their positions. However, examples of reasoned discussions were also found. Originality/value The study analyses conversations on Twitter, which is rarely studied. The focus on the interactions of bridging users adds to the uniqueness of the paper.
    Date
    20. 1.2015 18:30:22
  3. Cerda-Cosme, R.; Méndez, E.: Analysis of shared research data in Spanish scientific papers about COVID-19 : a first approach (2023) 0.03 
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    Abstract
    During the coronavirus pandemic, changes in the way science is done and shared occurred, which motivates meta-research to help understand science communication in crises and improve its effectiveness. The objective is to study how many Spanish scientific papers on COVID-19 published during 2020 share their research data. Qualitative and descriptive study applying nine attributes: (a) availability, (b) accessibility, (c) format, (d) licensing, (e) linkage, (f) funding, (g) editorial policy, (h) content, and (i) statistics. We analyzed 1,340 papers, 1,173 (87.5%) did not have research data. A total of 12.5% share their research data of which 2.1% share their data in repositories, 5% share their data through a simple request, 0.2% do not have permission to share their data, and 5.2% share their data as supplementary material. There is a small percentage that shares their research data; however, it demonstrates the researchers' poor knowledge on how to properly share their research data and their lack of knowledge on what is research data.
    Date
    21. 3.2023 19:22:02
  4. Vakkari, P.; Järvelin, K.; Chang, Y.-W.: ¬The association of disciplinary background with the evolution of topics and methods in Library and Information Science research 1995-2015 (2023) 0.03 
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    Abstract
    The paper reports a longitudinal analysis of the topical and methodological development of Library and Information Science (LIS). Its focus is on the effects of researchers' disciplines on these developments. The study extends an earlier cross-sectional study (Vakkari et al., Journal of the Association for Information Science and Technology, 2022a, 73, 1706-1722) by a coordinated dataset representing a content analysis of articles published in 31 scholarly LIS journals in 1995, 2005, and 2015. It is novel in its coverage of authors' disciplines, topical and methodological aspects in a coordinated dataset spanning two decades thus allowing trend analysis. The findings include a shrinking trend in the share of LIS from 67 to 36% while Computer Science, and Business and Economics increase their share from 9 and 6% to 21 and 16%, respectively. The earlier cross-sectional study (Vakkari et al., Journal of the Association for Information Science and Technology, 2022a, 73, 1706-1722) for the year 2015 identified three topical clusters of LIS research, focusing on topical subfields, methodologies, and contributing disciplines. Correspondence analysis confirms their existence already in 1995 and traces their development through the decades. The contributing disciplines infuse their concepts, research questions, and approaches to LIS and may also subsume vital parts of LIS in their own structures of knowledge production.
    Date
    22. 6.2023 18:15:06
  5. Milard, B.; Pitarch, Y.: Egocentric cocitation networks and scientific papers destinies (2023) 0.02 
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    Abstract
    To what extent is the destiny of a scientific paper shaped by the cocitation network in which it is involved? What are the social contexts that can explain these structuring? Using bibliometric data, interviews with researchers, and social network analysis, this article proposes a typology based on egocentric cocitation networks that displays a quadruple structuring (before and after publication): polarization, clusterization, atomization, and attrition. It shows that the academic capital of the authors and the intellectual resources of their research are key factors of these destinies, as are the social relations between the authors concerned. The circumstances of the publishing are also correlated with the structuring of the egocentric cocitation networks, showing how socially embedded they are. Finally, the article discusses the contribution of these original networks to the analyze of scientific production and its dynamics.
    Date
    21. 3.2023 19:22:14
  6. Thelwall, M.; Thelwall, S.: ¬A thematic analysis of highly retweeted early COVID-19 tweets : consensus, information, dissent and lockdown life (2020) 0.02 
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    Abstract
    Purpose Public attitudes towards COVID-19 and social distancing are critical in reducing its spread. It is therefore important to understand public reactions and information dissemination in all major forms, including on social media. This article investigates important issues reflected on Twitter in the early stages of the public reaction to COVID-19. Design/methodology/approach A thematic analysis of the most retweeted English-language tweets mentioning COVID-19 during March 10-29, 2020. Findings The main themes identified for the 87 qualifying tweets accounting for 14 million retweets were: lockdown life; attitude towards social restrictions; politics; safety messages; people with COVID-19; support for key workers; work; and COVID-19 facts/news. Research limitations/implications Twitter played many positive roles, mainly through unofficial tweets. Users shared social distancing information, helped build support for social distancing, criticised government responses, expressed support for key workers and helped each other cope with social isolation. A few popular tweets not supporting social distancing show that government messages sometimes failed. Practical implications Public health campaigns in future may consider encouraging grass roots social web activity to support campaign goals. At a methodological level, analysing retweet counts emphasised politics and ignored practical implementation issues. Originality/value This is the first qualitative analysis of general COVID-19-related retweeting.
    Date
    20. 1.2015 18:30:22
  7. Tay, W.; Zhang, X.; Karimi , S.: Beyond mean rating : probabilistic aggregation of star ratings based on helpfulness (2020) 0.01 
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    Abstract
    The star-rating mechanism of customer reviews is used universally by the online population to compare and select merchants, movies, products, and services. The consensus opinion from aggregation of star ratings is used as a proxy for item quality. Online reviews are noisy and effective aggregation of star ratings to accurately reflect the "true quality" of products and services is challenging. The mean-rating aggregation model is widely used and other aggregation models are also proposed. These existing aggregation models rely on a large number of reviews to tolerate noise. However, many products rarely have reviews. We propose probabilistic aggregation models for review ratings based on the Dirichlet distribution to combat data sparsity in reviews. We further propose to exploit the "helpfulness" social information and time to filter noisy reviews and effectively aggregate ratings to compute the consensus opinion. Our experiments on an Amazon data set show that our probabilistic aggregation models based on "helpfulness" achieve better performance than the statistical and heuristic baseline approaches.
  8. Zhao, D.; Strotmann, A.: Mapping knowledge domains on Wikipedia : an author bibliographic coupling analysis of traditional Chinese medicine (2022) 0.01 
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    Abstract
    Purpose Wikipedia has the lofty goal of compiling all human knowledge. The purpose of the present study is to map the structure of the Traditional Chinese Medicine (TCM) knowledge domain on Wikipedia, to identify patterns of knowledge representation on Wikipedia and to test the applicability of author bibliographic coupling analysis, an effective method for mapping knowledge domains represented in published scholarly documents, for Wikipedia data. Design/methodology/approach We adapted and followed the well-established procedures and techniques for author bibliographic coupling analysis (ABCA). Instead of bibliographic data from a citation database, we used all articles on TCM downloaded from the English version of Wikipedia as our dataset. An author bibliographic coupling network was calculated and then factor analyzed using SPSS. Factor analysis results were visualized. Factors were labeled upon manual examination of articles that authors who load primarily in each factor have significantly contributed references to. Clear factors were interpreted as topics. Findings Seven TCM topic areas are represented on Wikipedia, among which Acupuncture-related practices, Falun Gong and Herbal Medicine attracted the most significant contributors to TCM. Acupuncture and Qi Gong have the most connections to the TCM knowledge domain and also serve as bridges for other topics to connect to the domain. Herbal medicine is weakly linked to and non-herbal medicine is isolated from the rest of the TCM knowledge domain. It appears that specific topics are represented well on Wikipedia but their conceptual connections are not. ABCA is effective for mapping knowledge domains on Wikipedia but document-based bibliographic coupling analysis is not. Originality/value Given the prominent position of Wikipedia for both information users and for researchers on knowledge organization and information retrieval, it is important to study how well knowledge is represented and structured on Wikipedia. Such studies appear largely missing although studies from different perspectives both about Wikipedia and using Wikipedia as data are abundant. Author bibliographic coupling analysis is effective for mapping knowledge domains represented in published scholarly documents but has never been applied to mapping knowledge domains represented on Wikipedia.
  9. Jiang, X.; Zhu, X.; Chen, J.: Main path analysis on cyclic citation networks (2020) 0.01 
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    Abstract
    Main path analysis is a famous network-based method for understanding the evolution of a scientific domain. Most existing methods have two steps, weighting citation arcs based on search path counting and exploring main paths in a greedy fashion, with the assumption that citation networks are acyclic. The only available proposal that avoids manual cycle removal is to preprint transform a cyclic network to an acyclic counterpart. Through a detailed discussion about the issues concerning this approach, especially deriving the "de-preprinted" main paths for the original network, this article proposes an alternative solution with two-fold contributions. Based on the argument that a publication cannot influence itself through a citation cycle, the SimSPC algorithm is proposed to weight citation arcs by counting simple search paths. A set of algorithms are further proposed for main path exploration and extraction directly from cyclic networks based on a novel data structure main path tree. The experiments on two cyclic citation networks demonstrate the usefulness of the alternative solution. In the meanwhile, experiments show that publications in strongly connected components may sit on the turning points of main path networks, which signifies the necessity of a systematic way of dealing with citation cycles.
  10. Roszkowski, M.: ¬The sociological and ontological dimensions of the knowledge organization domain on Google Scholar citations (2020) 0.01 
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    Abstract
    This study aims to identify the profiles of researchers in the knowledge organization domain on Google Scholar Citations (GSC) and investigate its sociological and ontological dimensions. The sociological dimension is related to GSC users who declared research interests that fall within the scope of the knowledge organization domain. The ontological dimension is based on the study of these concepts. Domain analysis was used as a methodological framework for this study. A search was conducted on GSC using keywords in order to create a list of scholars who declared the knowledge organization domain as one of their research interests in their Google Scholar Profiles (GSPs). Next, the search for GSPs of authors who had published their papers in the Knowledge Organization journal from 2000 to 2019 was conducted. The results showed that there were 379 publicly available GSPs. Analysis of the affiliated institutions showed that the majority of them were based respectively in the USA, Brazil, and then in India. The ontological dimension of the knowledge organization domain on GSC was examined by studying keywords attached to GSPs. The most frequently used keywords were identified and using network analysis five clusters that represented the main areas of interest were extracted.
  11. Ikae, C.; Savoy, J.: Gender identification on Twitter (2022) 0.01 
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    Abstract
    To determine the author of a text's gender, various feature types have been suggested (e.g., function words, n-gram of letters, etc.) leading to a huge number of stylistic markers. To determine the target category, different machine learning models have been suggested (e.g., logistic regression, decision tree, k nearest-neighbors, support vector machine, naïve Bayes, neural networks, and random forest). In this study, our first objective is to know whether or not the same model always proposes the best effectiveness when considering similar corpora under the same conditions. Thus, based on 7 CLEF-PAN collections, this study analyzes the effectiveness of 10 different classifiers. Our second aim is to propose a 2-stage feature selection to reduce the feature size to a few hundred terms without any significant change in the performance level compared to approaches using all the attributes (increase of around 5% after applying the proposed feature selection). Based on our experiments, neural network or random forest tend, on average, to produce the highest effectiveness. Moreover, empirical evidence indicates that reducing the feature set size to around 300 without penalizing the effectiveness is possible. Finally, based on such reduced feature sizes, an analysis reveals some of the specific terms that clearly discriminate between the 2 genders.
  12. Gök, A.; Karaulova, M.: How "international" is international research collaboration? (2024) 0.01 
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    Abstract
    In the context of the increasing global connectivity in science, this article investigates the internal heterogeneity of international research collaborations (IRCs). We focus on the prevalence of shared heritage collaborations and the rise of multiple institutional affiliations as a collaboration mechanism. An analytical typology of IRCs based on the characteristics of collaborating researchers' location and heritage is developed and empirically tested on the dataset of Russia's publications in 2015. We found that shared heritage IRC and IRC via multiple affiliations are the cornerstones of internationalization. Significant structural differences are revealed between conventional IRC and these nonconventional IRCs across fields of science, locations, visibility of international partners, and the sources of funding. These results contribute towards a better understanding of IRC as a complex, heterogeneous phenomenon, which encompasses a variety of arrangements for knowledge creation across borders. A more nuanced understanding of IRC is needed for smarter university strategy, metric development, and policymaking.
  13. Chawla, D.S.: Hundreds of 'predatory' journals indexed on leading scholarly database (2021) 0.01 
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  14. Zhang, Y.; Zhang, C.: Enhancing keyphrase extraction from microblogs using human reading time (2021) 0.01 
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    Abstract
    The premise of manual keyphrase annotation is to read the corresponding content of an annotated object. Intuitively, when we read, more important words will occupy a longer reading time. Hence, by leveraging human reading time, we can find the salient words in the corresponding content. However, previous studies on keyphrase extraction ignore human reading features. In this article, we aim to leverage human reading time to extract keyphrases from microblog posts. There are two main tasks in this study. One is to determine how to measure the time spent by a human on reading a word. We use eye fixation durations (FDs) extracted from an open source eye-tracking corpus. Moreover, we propose strategies to make eye FD more effective on keyphrase extraction. The other task is to determine how to integrate human reading time into keyphrase extraction models. We propose two novel neural network models. The first is a model in which the human reading time is used as the ground truth of the attention mechanism. In the second model, we use human reading time as the external feature. Quantitative and qualitative experiments show that our proposed models yield better performance than the baseline models on two microblog datasets.
  15. Fang, Z.; Costas, R.; Tian, W.; Wang, X.; Wouters, P.: How is science clicked on Twitter? : click metrics for Bitly short links to scientific publications (2021) 0.01 
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    Abstract
    To provide some context for the potential engagement behavior of Twitter users around science, this article investigates how Bitly short links to scientific publications embedded in scholarly Twitter mentions are clicked on Twitter. Based on the click metrics of over 1.1 million Bitly short links referring to Web of Science (WoS) publications, our results show that around 49.5% of them were not clicked by Twitter users. For those Bitly short links with clicks from Twitter, the majority of their Twitter clicks accumulated within a short period of time after they were first tweeted. Bitly short links to the publications in the field of Social Sciences and Humanities tend to attract more clicks from Twitter over other subject fields. This article also assesses the extent to which Twitter clicks are correlated with some other impact indicators. Twitter clicks are weakly correlated with scholarly impact indicators (WoS citations and Mendeley readers), but moderately correlated to other Twitter engagement indicators (total retweets and total likes). In light of these results, we highlight the importance of paying more attention to the click metrics of URLs in scholarly Twitter mentions, to improve our understanding about the more effective dissemination and reception of science information on Twitter.
  16. Manley, S.: Letters to the editor and the race for publication metrics (2022) 0.01 
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    Date
    6. 4.2022 19:22:26
  17. Lemke, S.; Mazarakis, A.; Peters, I.: Conjoint analysis of researchers' hidden preferences for bibliometrics, altmetrics, and usage metrics (2021) 0.01 
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    Abstract
    The amount of annually published scholarly articles is growing steadily, as is the number of indicators through which impact of publications is measured. Little is known about how the increasing variety of available metrics affects researchers' processes of selecting literature to read. We conducted ranking experiments embedded into an online survey with 247 participating researchers, most from social sciences. Participants completed series of tasks in which they were asked to rank fictitious publications regarding their expected relevance, based on their scores regarding six prototypical metrics. Through applying logistic regression, cluster analysis, and manual coding of survey answers, we obtained detailed data on how prominent metrics for research impact influence our participants in decisions about which scientific articles to read. Survey answers revealed a combination of qualitative and quantitative characteristics that researchers consult when selecting literature, while regression analysis showed that among quantitative metrics, citation counts tend to be of highest concern, followed by Journal Impact Factors. Our results suggest a comparatively favorable view of many researchers on bibliometrics and widespread skepticism toward altmetrics. The findings underline the importance of equipping researchers with solid knowledge about specific metrics' limitations, as they seem to play significant roles in researchers' everyday relevance assessments.
  18. Chen, L.; Ding, J.; Larivière, V.: Measuring the citation context of national self-references : how a web journal club is used (2022) 0.01 
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    Abstract
    The emphasis on research evaluation has brought scrutiny to the role of self-citations in the scholarly communication process. While author self-citations have been studied at length, little is known on national-level self-references (SRs). This paper analyses the citation context of national SRs, using the full-text of 184,859 papers published in PLOS journals. It investigates the differences between national SRs and nonself-references (NSRs) in terms of their in-text mention, presence in enumerations, and location features. For all countries, national SRs exhibit a higher level of engagement than NSRs. NSRs are more often found in enumerative citances than SRs, which suggests that researchers pay more attention to domestic than foreign studies. There are more mentions of national research in the methods section, which provides evidence that methodologies developed in a nation are more likely to be used by other researchers from the same nation. Publications from the United States are cited at a higher rate in each of the sections, indicating that the country still maintains a dominant position in science. On the whole, this paper contributes to a better understanding of the role of national SRs in the scholarly communication system, and how it varies across countries and over time.
  19. Järvelin, K.; Vakkari, P.: LIS research across 50 years: content analysis of journal articles : offering an information-centric conception of memes (2022) 0.01 
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    Abstract
    Purpose This paper analyses the research in Library and Information Science (LIS) and reports on (1) the status of LIS research in 2015 and (2) on the evolution of LIS research longitudinally from 1965 to 2015. Design/methodology/approach The study employs a quantitative intellectual content analysis of articles published in 30+ scholarly LIS journals, following the design by Tuomaala et al. (2014). In the content analysis, we classify articles along eight dimensions covering topical content and methodology. Findings The topical findings indicate that the earlier strong LIS emphasis on L&I services has declined notably, while scientific and professional communication has become the most popular topic. Information storage and retrieval has given up its earlier strong position towards the end of the years analyzed. Individuals are increasingly the units of observation. End-user's and developer's viewpoints have strengthened at the cost of intermediaries' viewpoint. LIS research is methodologically increasingly scattered since survey, scientometric methods, experiment, case studies and qualitative studies have all gained in popularity. Consequently, LIS may have become more versatile in the analysis of its research objects during the years analyzed. Originality/value Among quantitative intellectual content analyses of LIS research, the study is unique in its scope: length of analysis period (50 years), width (8 dimensions covering topical content and methodology) and depth (the annual batch of 30+ scholarly journals).
  20. Wiggers, G.; Verberne, S.; Loon, W. van; Zwenne, G.-J.: Bibliometric-enhanced legal information retrieval : combining usage and citations as flavors of impact relevance (2023) 0.01 
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    Abstract
    Bibliometric-enhanced information retrieval uses bibliometrics (e.g., citations) to improve ranking algorithms. Using a data-driven approach, this article describes the development of a bibliometric-enhanced ranking algorithm for legal information retrieval, and the evaluation thereof. We statistically analyze the correlation between usage of documents and citations over time, using data from a commercial legal search engine. We then propose a bibliometric boost function that combines usage of documents with citation counts. The core of this function is an impact variable based on usage and citations that increases in influence as citations and usage counts become more reliable over time. We evaluate our ranking function by comparing search sessions before and after the introduction of the new ranking in the search engine. Using a cost model applied to 129,571 sessions before and 143,864 sessions after the intervention, we show that our bibliometric-enhanced ranking algorithm reduces the time of a search session of legal professionals by 2 to 3% on average for use cases other than known-item retrieval or updating behavior. Given the high hourly tariff of legal professionals and the limited time they can spend on research, this is expected to lead to increased efficiency, especially for users with extremely long search sessions.

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