arXiv subjects

Fereshteh Didegah

Publications and source records attributed to Fereshteh Didegah.

3 recordsLinked to original sources

arXiv2022

The association between topic growth and citation impact of research publications

Citations are used for research evaluation, and it is therefore important to know which factors influence or associate with citation impact of articles. Several citation factors have been studied in the literature. In this study we propose a new factor, topic growth, that no previous study has studied empirically. The growth rate of topics may influence future citation counts because a high growth in a topic means there are more publications citing previous publications in that topic. We construct topics using community detection in a citation network and use a two-part regression model to study the association between topic growth and citation counts in eight broad disciplines. The first part of the model uses quantile regression to estimate the effect of growth ratio on citation counts for publications with more than three citations. The second part of the model uses logistic regression to model the influence of the explanatory variables on the probability of being lowly cited versus being modestly or highly cited. Both models control for three variables that may distort the association between the topic growth and citations: journal impact, number of references, and number of authors. The regression model clearly shows that publications in fast-growing topics have a citation advantage compared to publications in slow-growing or declining topics in all of the eight disciplines. Using citation indicators for research evaluation may give incentives for researchers to publish in fast-growing topics, but they may cause research to be less diversified. The results have also some implications for citation normalization.

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arXiv2017

On the differences between citations and altmetrics: An investigation of factors driving altmetrics vs. citations for Finnish articles

This study examines a range of factors associating 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 modelled 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.

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arXiv2017

Measuring Social Media Activity of Scientific Literature: An Exhaustive Comparison of Scopus and Novel Altmetrics Big Data

This paper measures social media activity of 15 broad scientific disciplines indexed in Scopus database using Altmetric.com data. First, the presence of Altmetric.com data in Scopus database is investigated, overall and across disciplines. Second, the correlation between the bibliometric and altmetric indices is examined using Spearman correlation. Third, a zero-truncated negative binomial model is used to determine the association of various factors with increasing or decreasing citations. Lastly, the effectiveness of altmetric indices to identify publications with high citation impact is comprehensively evaluated by deploying Area Under the Curve (AUC) - an application of receiver operating characteristic. Results indicate a rapid increase in the presence of Altmetric.com data in Scopus database from 10.19% in 2011 to 20.46% in 2015. A zero-truncated negative binomial model is implemented to measure the extent to which different bibliometric and altmetric factors contribute to citation counts. Blog count appears to be the most important factor increasing the number of citations by 38.6% in the field of Health Professions and Nursing, followed by Twitter count increasing the number of citations by 8% in the field of Physics and Astronomy. Interestingly, both Blog count and Twitter count always show positive increase in the number of citations across all fields. While there was a positive weak correlation between bibliometric and altmetric indices, the results show that altmetric indices can be a good indicator to discriminate highly cited publications, with an encouragingly AUC= 0.725 between highly cited publications and total altmetric count. Overall, findings suggest that altmetrics could better distinguish highly cited publications.

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