Introducing the ‘alt-index’ for measuring the social visibility of scientific research

Hassan, Saeed‐Ul; Iqbal, Sehrish; Aljohani, Naif Radi; Alelyani, Salem; Zuccala, Alesia A.

doi:10.1007/s11192-020-03447-z

Cited by 10 publications

(3 citation statements)

References 37 publications

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“…Note that there are challenges using altmetrics, related to its terminology, data sources and coverage, gamification and cross-discipline comparisons (57,89,105). Nevertheless, an alt-index has recently been introduced that seems promising for the assessment of a researcher's scholarly impact, as it is highly correlated to the traditional h-index in many scientific disciplines, but provides feedback more quickly (47). In line with this finding, our study reinforces the value of attention scores and adds to the understanding of what articles reach a high attention.…”

Section: Important Article Characteristicsmentioning

confidence: 99%

Articles with impact: insights into 10 years of research with machine learning

Zwaard

Leeuw

Meerhoff

et al. 2020

Journal of Applied Physiology

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Worldwide scientific output is growing faster and faster. Academics should not only publish much and fast, but also publish research with impact. The aim of this study is to use machine learning to investigate characteristics of articles that were published in the Journal of Applied Physiology between 2009 and 2018, and characterize high-impact articles. Article impact was assessed for 4,531 publications by three common impact metrics: the Altmetric Attention Scores, downloads and citations. Additionally, a broad collection of (more than 200) characteristics was collected from the article's title, abstract, authors, keywords, publication, and article engagement. We constructed random forest (RF) regression models to predict article impact and articles with the highest impact (top-25% and top-10% for each impact metric), which were compared to a naïve baseline method. RF models outperformed the baseline models when predicting the impact of unseen articles (p<0.001 for each impact metric). Also, RF models predicted top-25% and top-10% high-impact articles with a high accuracy. Moreover, RF models revealed important article characteristics. Higher impact was observed for articles about exercise, training, performance and V̇O2max, reviews, human studies, articles from large collaborations, longer articles with many references and high engagement by scientists, practitioners and public or via news outlets and videos. Lower impact was shown for articles about respiratory physiology or sleep apnea, editorials, animal studies, and titles with a question mark or a reference to places or individuals. In summary, research impact can be predicted and better understood using a combination of article characteristics and machine learning.

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Section: Important Article Characteristicsmentioning

confidence: 99%

Articles with impact: insights into 10 years of research with machine learning

Zwaard

Leeuw

Meerhoff

et al. 2020

Journal of Applied Physiology

View full text Add to dashboard Cite

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“…The increased use of social media has also significantly affected how research is spread. Circulated articles through social media are more visible than not circulated articles [ 6 ]. Various scientific studies have examined this relationship, and most have found a positive correlation between article citations and Twitter exposure [ 7 – 9 ].…”

Section: Introductionmentioning

confidence: 99%

“…These account holders may be social workers, companies, or politicians, and would have more followers than others [ 45 , 46 ]. Thus, some researchers suggest the use of “alt‑index” to measure the social visibility of scientific research [ 6 ]. Similarly, Haustein et al argued that social media metrics could not actually be regarded as alternatives to traditional citations; hence, they proposed these metrics as promoters of traditional citations [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Social media usage to share information in communication journals: An analysis of social media activity and article citations

Özkent

2022

PLoS ONE

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Social media has surrounded every area of life, and social media platforms have become indispensable for today’s communication. Many journals use social media actively to promote and disseminate new articles. Its use to share the articles contributes many benefits, such as reaching more people and spreading information faster. However, there is no consensus in the studies that to evaluate between tweeted and non-tweeted papers regarding their citation numbers. Therefore, it was aimed to show the effect of social media on the citations of articles in the top ten communication-based journals. For this purpose, this work evaluated original articles published in the top 10 communication journals in 2018. The top 10 communication-based journals were chosen based on SCImago Journal & Country Rank (cited in 2019). Afterward, it was recorded the traditional citation numbers (Google Scholar and Thompson-Reuters Web of Science) and social media exposure of the articles in January 2021 (nearly three years after the articles’ publication date). It was assumed that this period would allow the impact of the published articles (the citations and Twitter mentions) to be fully observed. Based on this assessment, a positive correlation between exposure to social media and article citations was observed in this study.

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