Author Profile Research Articles

Digital academic platforms: Case studies of CEE (Hungary and Slovakia)

Present study evaluates the prevalence and academic usage of digital academic platforms exemplified by private universities across Hungary and Slovakia, assessing how these platforms are integrated into academic practices and highlighting certain variations in technological adoption within the CEE region. After the setting up of the relevant typology and by utilizing a dual-methodological approach, the regional e-visibility of the leadership of certain faculties is examined, while specific user motivations are also explored in a narrower sense through domestic questionnaire-based studies within Hungary. Firstly, by analyzing the use of certain i) academic social netowrking sites (hereinafter ASNSs); ii) author profiles linked to publishers; and databases without social functions at private universities and juxtaposing these with the experiences of Slovak institutions, the research highlights how these platforms are utilized to enhance research visibility and academic networking. Secondly, findings are meant to indicate that while DAPs are widely valued for facilitating scholarly communication in Hungary, localized academic cultures and individual motivations significantly shape their application.

Author Profile: Paul Griffin.

Author Profile: Paul Griffin.

Author profile: Dr. Samriddha Ray.

Author profile: Dr. Samriddha Ray.

Author profile: Hongsheng Xue.

Author profile: Hongsheng Xue.

Author profile: Amrita Kumari.

Author profile: Amrita Kumari.

Author profile: Albin E. Berg.

Author profile: Albin E. Berg.

Author profile: Mohona Gupta.

Author profile: Mohona Gupta.

Gender Differences in Citation Rate: An Analysis of Randomized Controlled Trials in Nephrology High-Impact Journals Over Two Decades.

Historically, women's scientific contributions have been under-recognized. We investigated whether the number of citations, a key metric used for academic promotions and scientific productivity, differs in nephrology high impact publications based on author's gender. We identified randomized clinical trials (RCTs) from 2000-2021 in ten high impact journals. We assessed author gender, citations, h-index, m-index, years active publishing, education, and grant funding. The main predictor of interest was the gender of the first author. The main outcome was the standardized citation count for analysis of the selected publications. Additionally, we evaluated standardized author citation counts using the author, rather than paper. Among the selected publications, women were first authors of 65 (17.1%) and men of 315 manuscripts (82.9%). In crude analyses, publications with male first authors had a significantly higher median number of standardized citations (14 vs. 10, P=0.01). Adjusted analyses revealed, the m-index (β=29.48, P≤0.01) and journal impact factor (β=0.78, P<0.001) were significantly associated with the standardized citation index. In contrast, neither the gender of the first author (βmale gender=1.42, P=0.71) nor of the last author (βmale gender=8.89, P=0.38) were significantly associated with the standardized citations. Similarly, in adjusted analyses based on author profiles, male authorship was not significantly associated with the standardized author citation number (βmale gender=-7.79, P=0.08). Our study highlights marked disparities in the overall number of women publishing high impact nephrology trials and the number of papers with women scientists as first authors of high impact trials in the nephrology literature. Although crude citation rates were lower in papers with female first authors, the gender of the first author was not independently associated with citation metrics. Addressing gender disparities in academic recognition requires nuanced approaches extending beyond authorship and a broader focus on complex factors that influence academic recognition and scientific contributions.

Researcher profile system adoption and use across discipline and rank: A case study at the University of Manitoba

Abstract This study analyzes the adoption and use of researcher profile systems (ORCID, Scopus Author Profiles, Web of Science Researcher Profiles (formerly Publons), Google Scholar Profiles, and ResearchGate) across discipline and rank at the University of Manitoba (Winnipeg, Canada). The purpose of the study is to assess how many faculty members have registered for and use researcher profiles and whether there are any differences in use along discipline or academic rank. The adoption rate in the current study is compared with other international studies. At the University of Manitoba, there is variance in adoption between disciplines and ranks. When comparing profile systems by discipline, Google Scholar is the primary profile system for sciences and ORCID, Publons, and ResearchGate the primary profile systems for health sciences. There is variance of publication count between disciplines. Unsurprisingly, the number of publications increases as faculty are promoted. Among the studied profile systems, ORCID is not working as efficiently as it could be. Several recommendations to increase ORCID adoption are made, including mandatory public fields and suggestions for third-party integration. As part of increasing usage of profile systems, we see academic librarians as a key component of instruction and advocacy for graduate students and faculty.

Untangling the truth: User engagement with misinformation in toothache-related Facebook posts.

Social media is a platform for sharing views on aspects of life, including oral health. This study aimed to characterize Facebook posts related to toothache information. Two independent investigators retrieved 500 English-language posts with the highest level of interaction using CrowdTangleTM and analyzed their facticity, motivation, author's profile, content, sentiment, and type of post. Data were analysed descriptively and using Pearson's Chi-square and Mann-Whitney U tests and multiple logistic regression models. Most posts were produced by regular users and were not financially motivated, although commercial posts had significantly higher total interaction among users. While link- or video-containing posts (OR = 1.66) and posts with positive sentiments (OR = 1.53) were associated with users' total interaction, older (OR = 1.81) and link- or video-containing posts (OR = 2.04) were associated with overperforming scores. Misinformation was positively associated with financial motivation (OR = 2.03) and positive sentiments (OR = 3.79). This study highlights the importance of addressing the spread of misinformation related to oral health on social media and taking steps to ensure that accurate and reliable information is readily available. Toothache-related misinformation was associated with positive sentiments and financial motivation. Links, videos, and positive sentiments awakened greater user engagements with toothache-related posts.

Uncovering a pseudoscience: an analysis of 'biological dentistry' Instagram posts.

This infodemiology study aimed to analyze characteristics of English-language Instagram posts on 'Biological Dentistry'. Using CrowdTangle, we analyzed 500 'Biological Dentistry' posts published on Instagram from May 2017 to May 2022. Two researchers assessed each post for facticity, motivation, author's profile, sentiment, and interaction metrics. Statistical analysis was employed to compare interaction metrics between dichotomized categories of posts' characteristics and determine predictors of misinformation and user engagement. Over half of the posts (58.4%) were from health-related authors, and a considerable number contained misinformation (68.2%) or were financially motivated (52%). Sentiment was mostly negative or neutral (59.8%). Misinformation was associated with financial motivation (OR = 2.12) and health-related authors (OR = 5.56), while non-health-related authors' posts associated with higher engagement (OR = 1.98). Reliable content, non-health-related authorship, and positive sentiment were associated with increased user interaction. Misinformation about 'Biological Dentistry' on Instagram is mainly spread by financially incentivized health-related authors. Yet, non-health-related authors' posts resonate more with audiences, highlighting a nuanced relationship between content facticity, authorship, and engagement.

Exploring the factors driving higher interactions in false amber necklace posts on Facebook.

The consumption and sharing of information on social media contribute to the circulation of false health content. In this sense, popular belief-driven posts recommending the use of amber necklaces to prevent symptoms of teething can be easily found on social media, even when they offer health risks for children. Accordingly, this study aimed to characterize posts in Portuguese by identifying message-based factors associated with higher user interactions of Facebook. A total of 500 Facebook posts published in Portuguese between August 2016 and August 2021 were retrieved by CrowdTangle, and analyzed quantitatively according to time of publication, total user interaction, and post overperforming score, and then qualitatively by two independent investigators, according to motivation, authors' profiles, and sentiment. The data were evaluated by descriptive analysis, Mann-Whitney U test, chi-square test, and multiple logistic regression models regarding interaction metrics. The posts were published predominantly by commercial profiles, with social, psychological, and financial motivations, and expressed positive sentiment. Furthermore, time of publication (OR=2.65), regular user profiles (OR=3.30), and neutral/negative sentiment (OR=2.76) were associated positively with total interactions, whereas only time of publication was associated with post overperforming scores (OR=1.65). In conclusion, Facebook posts promoting efficacy of amber necklaces in managing teething symptoms are primarily created by commercial profiles with vested interests in marketing the product. The key factors that contribute to the success of posts containing false information about amber necklaces on Facebook can be identified by considering the author's profile, sentiment, and time of publication.

Meet the Author: linking and displaying author information in EconBiz Author profiles

Meet the Author: linking and displaying author information in EconBiz Author profiles

Misrepresentation of Citations: A Case Study of Google Scholar

The current study aims to identify the misrepresented and actual citations in Google Scholar and rank the professionals according to their correct accreditations. The selected Google Scholar profiles were analysed to reveal the misrepresentation of the citation counts based on the wrong indexing of author names. A total of 30 top prolific author profiles from Library and Information Science professionals were selected for this study. The publication and biographical data were gathered from Google Scholar and compared with Web of Science and Scopus to avoid ambiguity. The findings reveal that misrepresenting citations to the author profiles due to the wrong indexing of author names leads to an increased citation count and affects the resultant author ranking.

SCIENTIST PROFILE IN IDENTIFICATION SYSTEMS AS AN INDICATOR OF PUBLICATION ACTIVITY OF A SCIENTIST: THE ROLE OF THE UNIVERSITY LIBRARY

The article deals with the features of international identification systems of scientists. The author analyzes the profile of a scientist tied to a single unique number identifier as the most important criterion for identifying scientific articles of the author in databases of scientific citation. The directions of activity of the Scientific Library of the Admiral Makarov National University of Shipbuilding, which are aimed at to increase the indicators of publication activity and citations by providing information support of university scientists, popularization of the achievements of leading scientists. Conclusions. The system of accounting for scientific publications in an organization involves: identifying all author identifiers in the main scientific citation systems; combining information into a single profile with a unique identification number of each system; displaying indexed scientific publications in the ORCID system (author responsibility), and actively involving researchers in the work of reputable international professional social networks. It is proved that the formation of a package of new services provided by the Scientific Library: assistance in in the preparation of manuscripts according to the requirements of publishers; consulting on the state of the author's profile in various scientometric systems; selection of scientific journals for publication of research; maintaining retrospective and current information on the publications of higher education institutions contribute to increasing the publication activity of university researchers.

Author and organization profiles in Dimensions and Lens information systems: The study of functionality

The paper demonstrates the features of author and organizational publication profiles in Dimensions and Lens information systems. In recent years, these systems have been successfully competing with many subscription-based systems. The authors discuss the advantages of these databases in terms of coverage of research content in publication profiles as compared to that of commercial bibliographic platforms, as well as the wide range of functional capabilities. As illustrated by publication profiles of two research organizations and their authors, we describe original approaches to searching bibliographic information, as well as editing bibliographic errors in databases. These features enable (a) to monitor effectively the scholarly output of research organizations and authors, and (b) to enhance visibility of research findings in the international information space. Solving information-retrieval problems and editing publication profiles of Russian organizations and authors in Dimensions and Lens prove to be similar to that of commercial systems, i. e. Web of Science and Scopus. The vast experience of working with proprietary databases and subscription-based systems, would enable Russian academic libraries to adapt to new interfaces in a short time frame.

Unveiling deception: Characterizing false amber necklace messages on Facebook.

Messages promoting the benefits of amber necklaces for children are common on social media, despite their health risks. This study characterized Facebook posts with false content about the efficacy of amber necklaces in teething. A sample of 500 English-language Facebook posts was analyzed by two investigators to determine the motivations, author's profile, and sentiments of posts. Latent Dirichlet Allocation topic modeling was used to identify salient terms and topics. An intertopic distance map was created to calculate the topic similarity. These data were analyzed using descriptive analysis, the Mann-Whitney U test, Cramer's V test, and multiple logistic regression models, regarding the time since initial posting and interaction metrics. Most posts were made by business profiles and expressed positive sentiments, with social, psychological, and financial motivations. The posts were categorized into the topics "giveaway," "healing features," and "sales." Overperforming scores and total interaction increased with time since the initial posting. Posts with links had higher overperforming scores. The findings suggest that Facebook posts about the efficacy of amber necklaces in teething are motivated by financial interests, using psychological and social mechanisms to achieve greater interaction with their target audience.

Methods and software for significant indicators determination of the natural language texts author profile

Methods for the formation and optimization of author profiles are presented. The author profile is an image – a vector in a multidimensional space, which components are author’s texts measurements by a number of methods based on 4-grams, stemming, recurrence analysis and formal stochastic grammar. The author’s profile is a model of his language, including vocabulary, sentence syntax features. A comparative analysis of each of the methods effectiveness is carried out. By means of the genetic algorithm, a reduced profile of the author is formed. Insignificant indicators are excluded, which allows to reduce their number by 20%. The reduced author’s profile contains attributes that are significant for this author and is an effective attribution of a particular author.

A Word Embeddings based Approach for Author Profiling: Gender and Age Prediction

Author Profiling (AP) is a method of identifying the demographic profiles such as age, gender, location, native language and personality traits of an author by processing their written texts. The AP techniques are used in multiple applications such as literary research, marketing, forensics and security. The researchers identified various differences in the authors writing styles by analysing various datasets. The differences in writing styles are represented as stylistic features. The researchers extracted several style based features like structural, content, word, character, syntactic, readability and semantic features to recognize the profiles of the authors. Traditionally, the researchers extracted various feature combinations for differentiating the profiles of authors. Several existing works are used Machine Learning (ML) methods for predicting the author characteristics of a new author. The existing works achieved good accuracies for predicting the author characteristics by considering the both stylistic features and ML algorithms combination. Recently, in advent of Deep Learning (DL) techniques the researchers are proposed approaches to author profiling by using these techniques. Few researchers identified that the deep learning techniques performance is good for author profiles prediction than the results of style based features. In this work, a word embeddings based approach is proposed for gender and age prediction. In this approach, the experiment conducted with different word embedding models such as Word2Vec, GloVe, FastText and BERT for generating word vectors for words. The documents are converted as vectors by using the document representation technique which uses the word embeddings of words. The document vectors are transferred to three different ML algorithms such as Extreme Gradient Boosting (XGBoost), Random Forest (RF) and Logistic Regression (LR) for generating the trained model. This model is used for predicating the accuracy of age and gender prediction. The XGBoost classifier with word embeddings of BERT achieved good accuracies for age and gender prediction than other word embeddings and ML algorithms. The experiment implemented on PAN 2014 competition Reviews dataset for age and gender prediction. The proposed approach attained best accuracies for predicting age and gender than the performances of various existing approaches proposed for AP.

Belleten Dergisinin Görsel Haritalaması ve Bibliyometrik Analizi (2009-2021)

The Belleten Journal, which started publication in 1937, has contributed to Turkey's knowledge and dissemination of information for nearly a century. In this research, visual mapping and bibliometric analysis of 431 articles published in the journal Belleten, which were scanned in Web of Science (WoS) and Scopus databases between 2009-2021, were conducted. Data were obtained from the WoS and Scopus database in January (2022). In the study, the articles with the highest impact factor, the distribution of the articles, the author profile, the number of authors, the distribution of the authors according to the universities they belong to and the most frequently cited authors were determined. In order to determine the intellectual structure of the journal Belleten, co-citation analysis was carried out to reveal the relationship networks between bibliometric items. Research findings show that Golen, Z. with six articles and Demiryürek, N. with five articles in the journal Belleten are the most productive authors. Ankara University is the university that contributes the most to the journal with 24 articles. Hacettepe University follows Ankara University with 20 articles. The highest number of publications in the journal is in Turkey with 374 articles. Turkey is followed by Kyrgyzstan with six articles and Azerbaijan with five articles. Of the articles published in the journal, 293 are empirical, and 138 are review articles. The findings show that the Belleten Journal successfully fulfills its function as an effective platform for producing and disseminating knowledge in Turkish History, Political Science, Turkish Culture, and Anthropology.