Context of altmetrics data matters: an investigation of count type and user category

Yu, Houqiang

doi:10.1007/s11192-017-2251-z

Cited by 36 publications

(31 citation statements)

References 13 publications

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“…The investigation of the relationship between altmetrics and assessments by peers in this study demonstrates that the relationship between altmetrics and peers' assessments (one aspect of scientific quality) is not as strong as the relationship between peers' assessments and citations. Against the backdrop of the literature investigating the user population on the underlying platforms, this result was expectable (see, e.g., Yu, 2017). The platforms are not only used by scientists, but also by people who do not have the expertise to assess the quality of research.…”

Section: Discussionmentioning

confidence: 97%

Normalization of zero-inflated data: An empirical analysis of a new indicator family and its use with altmetrics data

Bornmann

Haunschild

2018

Journal of Informetrics

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Recently, two new indicators (Equalized Mean-based Normalized Proportion Cited, EMNPC;Mean-based Normalized Proportion Cited, MNPC) were proposed which are intended for sparse scientometrics data. The indicators compare the proportion of mentioned papers (e.g.on Facebook) of a unit (e.g., a researcher or institution) with the proportion of mentioned papers in the corresponding fields and publication years (the expected values). In this study, we propose a third indicator (Mantel-Haenszel quotient, MHq) belonging to the same indicator family. The MHq is based on the MH analysisan established method in statistics for the comparison of proportions. We test (using citations and assessments by peers, i.e. F1000Prime recommendations) if the three indicators can distinguish between different quality levels as defined on the basis of the assessments by peers. Thus, we test their convergent validity. We find that the indicator MHq is able to distinguish between the quality levels in most cases while MNPC and EMNPC are not. Since the MHq is shown in this study to be a valid indicator, we apply it to six types of zero-inflated altmetrics data and test whether different altmetrics sources are related to quality. The results for the various altmetrics demonstrate that the relationship between altmetrics (Wikipedia, Facebook, blogs, and news data) and assessments by peers is not as strong as the relationship between citations and assessments by peers. Actually, the relationship between citations and peer assessments is about two to three times stronger than the association between altmetrics and assessments by peers.

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Section: Discussionmentioning

confidence: 97%

Normalization of zero-inflated data: An empirical analysis of a new indicator family and its use with altmetrics data

Bornmann

Haunschild

2018

Journal of Informetrics

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“…For instance, health care professionals tended to tweet health and medical journal articles, whereas institutional accounts were more likely to tweet social science and humanities journal articles (Vainio & Holmberg, ). Using an automatic classification provided by http://altmetric.com, Yu () found that more 85% of tweeters sharing scientific articles were members of the public. http://altmetric.com's classifications of Twitter users are unreliable, however, because they apply ‘Member of the public’ to profiles that cannot be fitted into other categories (i.e.…”

Section: Literature Reviewmentioning

confidence: 99%

Who shares health and medical scholarly articles on Facebook?

2019

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Over a million journal articles had been shared on public Facebook pages by 2017, but little is known about who is sharing (posting links to) these papers and whether mention counts could be an impact indicator. This study classified users who had posted about 749 links on Facebook before October 2017 mentioning 500 medical and health‐related research articles, obtained using http://altmetric.com data. Most accounts (68%) belonged to groups, including online communities, journals, academic organizations, and societies. Of individual profiles, academics accounted for only 4%, but the largest group were health care professionals (16%). More than half (58%) of all Facebook accounts examined were not academic. The non‐academic dominance suggests that public Facebook posts linking to health‐related articles are mostly used to facilitate scientific knowledge flow between non‐academic professionals and the public. Therefore, Facebook mention counts may be a combined academic and non‐academic attention indicator in the health and medical domains.

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“…Another study also suggests that most of the tweets are distributed by the public user (Yu, 2017). Mohammadi et al (2018) showed that about 45% of tweets were posted by people who didn't work in academia.…”

Section: Twittermentioning

confidence: 99%

Altmetrics and societal impact measurements: Match or mismatch? A literature review

Tahamtan¹,

Bornmann²

2020

EPI

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Can alternative metrics (altmetrics) data be used to measure societal impact? We wrote this literature overview of empirical studies in order to find an answer to this question. The overview includes two parts. The first part, "societal impact measurements", explains possible methods and problems in measuring the societal impact of research, case studies for societal impact measurement, societal impact considerations at funding organizations, and the societal problems that should be solved by science. The second part of the review, "altmetrics", addresses a major question in research evaluation, which is whether altmetrics are proper indicators for measuring the societal impact of research. In the second part we explain the data sources used for altmetrics studies and the importance of field-normalized indicators for impact measurements. This review indicates that it should be relevant for impact measurements to be oriented towards pressing societal problems. Case studies in which societal impact of certain pieces of research is explained seem to provide a legitimate method for measuring societal impact. In the use of altmetrics, field-specific differences should be considered by applying field normalization (in cross-field comparisons). Altmetrics data such as social media counts might mainly reflect the public interest and discussion of scholarly works rather than their societal impact. Altmetrics (Twitter data) might be especially fruitfully employed for research evaluation purposes, if they are used in the context of network approaches. Conclusions based on altmetrics data in research evaluation should be drawn with caution.

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Context of altmetrics data matters: an investigation of count type and user category

Cited by 36 publications

References 13 publications

Normalization of zero-inflated data: An empirical analysis of a new indicator family and its use with altmetrics data

Normalization of zero-inflated data: An empirical analysis of a new indicator family and its use with altmetrics data

Who shares health and medical scholarly articles on Facebook?

Altmetrics and societal impact measurements: Match or mismatch? A literature review

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