Do altmetrics work for assessing research quality?

Nuzzolese, Andrea Giovanni; Ciancarini, Paolo; Gangemi, Aldo; Peroni, Silvio; Poggi, Francesco; Presutti, Valentina

doi:10.1007/s11192-018-2988-z

Cited by 52 publications

(51 citation statements)

References 26 publications

(26 reference statements)

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“…Altmetrics are non-traditional metrics that cover not just citation counts but also downloads, social media shares, and other measures of the impact of research outputs [65]. Altmetrics measures the broader impact of research on society [5,7,8,[66][67][68][69][70], especially by using a much wider set of resources, including social media posts, press releases, news articles, and political debates stemming from academic work, as well as assesses wider non-academic impact [6]. In 2014, Bornmannanalyzed the advantages and disadvantages of measuring the impact of using altmetrics [4].…”

Section: Altmetricsmentioning

confidence: 99%

Measuring researchers’ potential scholarly impact with structural variations: Four types of researchers in information science (1979–2018)

2020

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We propose a method to measure the potential scholarly impact of researchers based on network structural variations they introduced to the underlying author co-citation network of their field. We applied the method to the information science field based on 91,978 papers published between 1979 and 2018 from the Web of Science. We divided the entire period into eight consecutive intervals and measured structural variation change rates (ΔM) of individual authors in corresponding author co-citation networks. Four types of researchers are identified in terms of temporal dynamics of their potential scholarly impact-1) Increasing, 2) Decreasing, 3) Sustained, and 4) Transient. The study contributes to the understanding of how researchers' scholarly impact might evolve in a broad context of the corresponding research community. Specifically, this study illustrated a crucial role played by structural variation metrics in measuring and explaining the potential scholarly impact of a researcher. This method based on the structural variation analysis offers a theoretical framework and a practical platform to analyze the potential scholarly impact of researchers and their specific contributions.

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Section: Altmetricsmentioning

confidence: 99%

Measuring researchers’ potential scholarly impact with structural variations: Four types of researchers in information science (1979–2018)

2020

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“…In this study we use the altmetrics provided by Plum Analytics 2 (PlumX) which is one of the leading platforms that provides altmetrics (Nuzzolese 2019). It is a provider of alternative metrics created in 2012 and covers more than 52.6M of artifacts, metrics and sources of metrics that are collected into five separate categories: (i) Citations: contain both traditional citation indexes such as Scopus, as well as citations that help indicate societal impact such as Clinical or Policy Citations.…”

Section: Scientometric Analysismentioning

confidence: 99%

The impact of early scientific literature in response to COVID-19: a scientometric perspective

Golinelli

Nuzzolese

Boetto

et al. 2020

Preprint

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Background. In the early phases of a new pandemic, identifying the most relevant evidence and quantifying which studies are shared the most can help researchers and policy makers. The aim of this study is to describe and quantify the impact of early scientific production in response to COVID-19 pandemic.Methods. The study consisted of: 1) review of the scientific literature produced in the first 30 days since the first COVID-19 paper was published; 2) analysis of papers' metrics with the construction of a "Computed-Impact-Score" (CIS) that represents a unifying score over heterogeneous bibliometric indicators. In this study we use metrics and alternative metrics collected into five separate categories. On top of those categories we compute the CIS. Highest CIS papers are further analyzed.Results. 239 papers have been included in the study. The mean of citations, mentions and social media interactions resulted in 1.63, 10 and 1250, respectively. The paper with highest CIS resulted "Clinical features of patients [...]" by Chaolin Huang et al., which rated first also in citations and mentions. This is the first paper describing patients affected by the new disease and reporting data that are clearly of great interest to both the scientific community and the general population.Conclusions. The early response of scientific literature during an epidemic does not follow a preestablished pattern. Being able to monitor how communications spread from the scientific world toward the general population using both traditional and alternative metric measures is essential, especially in the early stages of a pandemic.

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“…Mining these data may provide useful insights as an alternative to traditional citation metrics (Priem et al, 2011). Although the popularity of altmetric techniques has been increasing (Nuzzolese et al, 2019), there is a lack of information and evidence on their effectiveness, and the major outstanding challenges are to ensure the use of standards and best practice (Haustein et al, 2015;Bornmann et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Predicting literature’s early impact with sentiment analysis in Twitter

Hassan

Aljohani

Idrees

et al. 2020

Knowledge-Based Systems

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The traditional bibliometric techniques gauge the research impact through citation-based quantitative indices. However, due to citation lag time, it may take years to address the impact of an article. This paper seeks to measure an early impact of research articles using tweet sentiments associated with them. We claim that the papers cited in positive and neutral tweets have a higher impact than those not cited or cited in negative tweets. Accordingly, we use SentiStrenth, and we improve it by incorporating new opinion bearing words of scientific domain in its sentiment lexicons. Then, we classify the sentiment of 6,482,260 tweets linked to 1,083,535 publications covered by Altmetric.com. By using positive and negative tweets as an independent variable and the citation count as the dependent variable, the linear regression analysis shows a weak positive prediction of high citation counts across 16 broad disciplines in Scopus. By introducing an additional indicator, i.e. 'number of unique Twitter users,' the regression model improves the adjusted R-squared value of regression analysis in several disciplines. Overall, the encouraging positive correlation between the tweet sentiments and citations show that Twitterbased opinion may be exploited as a complementary indicator for predicting literature's early impact.

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Do altmetrics work for assessing research quality?

Cited by 52 publications

References 26 publications

Measuring researchers’ potential scholarly impact with structural variations: Four types of researchers in information science (1979–2018)

Measuring researchers’ potential scholarly impact with structural variations: Four types of researchers in information science (1979–2018)

The impact of early scientific literature in response to COVID-19: a scientometric perspective

Predicting literature’s early impact with sentiment analysis in Twitter

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