Could scientists use Altmetric.com scores to predict longer term citation counts?

Thelwall, Mike; Nevill, Tamara

doi:10.1016/j.joi.2018.01.008

Cited by 104 publications

(99 citation statements)

References 43 publications

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“…Shema, Bar-Ilan, and Thelwall (2014) showed that citing in blog posts correlates with a future higher level of citations. Recently Thelwall and Nevill demonstrated that altmetric scores, particularly those including Mendeley, can predict future, long term citation counts (Thelwall & Nevill, 2018).…”

Section: Altmetricsmentioning

confidence: 99%

Retractions from altmetric and bibliometric perspectives

Shema

Hahn

Mazarakis

et al. 2019

Information - Wissenschaft &Amp; Praxis

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Information. Wissenschaft & Praxis 2019; 70(2-3): 98-110 aléatoire. Un modèle de régression binomial permet d'estimer la probabilité qu'une publication soit retirée pour cause d'inconduite ou d'erreurs. Dans l'échantillon de scores altmétriques élevés, nous avons trouvé une corrélation positive entre les « Altmetric attention Scores » et la probabilité de retrait pour cause d'inconduite ou d'erreurs.

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Section: Altmetricsmentioning

confidence: 99%

Retractions from altmetric and bibliometric perspectives

Shema

Hahn

Mazarakis

et al. 2019

Information - Wissenschaft &Amp; Praxis

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“…only some publications with high citation counts are more likely to appear on Twitter or in news articles. Thus, these contradictive statements, i.e., findings by Thelwall & Nevill (2018) and Erdt et al (2016), have to be studied in more detail. Moreover, there are only few studies (Manisha & Mahesh, 2015) that are concerned with the effect of news articles on the citation counts.…”

Section: Introductionmentioning

confidence: 98%

What increases (social) media attention: Research impact, author prominence or title attractiveness?

Zagovora¹,

Weller²,

Janosov³

et al. 2018

Preprint

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Do only major scientific breakthroughs hit the news and social media, or does a 'catchy' title help to attract public attention? How strong is the connection between the importance of a scientific paper and the (social) media attention it receives? In this study we investigate these questions by analysing the relationship between the observed attention and certain characteristics of scientific papers from two major multidisciplinary journals: Nature Communication (NC) and Proceedings of the National Academy of Sciences (PNAS). We describe papers by features based on the linguistic properties of their titles and centrality measures of their authors in their co-authorship network. We identify linguistic features and collaboration patterns that might be indicators for future attention, and are characteristic to different journals, research disciplines, and media sources.

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“…Click-based reactions offer obvious benefits to researchers in the fields of bibliometrics and alternative metrics (or altmetrics)-a growing area of interest that takes into consideration the dissemination of a research outcome via multiple social media platforms [2][3][4][5]. Previous studies have used citations as the gold standard for understanding and predicting the influence that scholarly research has on the scientific community itself [6], but evaluating the emotional impact that work may have on society has remained largely untouched.…”

Section: Introductionmentioning

confidence: 99%

Measuring the Diversity of Facebook Reactions to Research

Freeman

Alhoori

Shahzad

2020

Proc. ACM Hum.-Comput. Interact.

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Online and in the real world, communities are bonded together by emotional consensus around core issues. Emotional responses to scientific findings often play a pivotal role in these core issues. When there is too much diversity of opinion on topics of science, emotions flare up and give rise to conflict. This conflict threatens positive outcomes for research. Emotions have the power to shape how people process new information. They can color the public's understanding of science, motivate policy positions, even change lives. And yet little work has been done to evaluate the public's emotional response to science using quantitative methods. In this paper, we use a dataset of responses to scholarly articles on Facebook to analyze the dynamics of emotional valence, intensity, and diversity. We present a novel way of weighting click-based reactions that increases their comprehensibility, and use these weighted reactions to develop new metrics of aggregate emotional responses. We use our metrics along with LDA topic models and statistical testing to investigate how users' emotional responses differ from one scientific topic to another. We find that research articles related to gender, genetics, or agricultural/environmental sciences elicit significantly different emotional responses from users than other research topics. We also find that there is generally a positive response to scientific research on Facebook, and that articles generating a positive emotional response are more likely to be widely shared-a conclusion that contradicts previous studies of other social media platforms.

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Could scientists use Altmetric.com scores to predict longer term citation counts?

Cited by 104 publications

References 43 publications

Retractions from altmetric and bibliometric perspectives

Retractions from altmetric and bibliometric perspectives

What increases (social) media attention: Research impact, author prominence or title attractiveness?

Measuring the Diversity of Facebook Reactions to Research

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