arXiv subjects

Kim Holmberg

Publications and source records attributed to Kim Holmberg.

5 recordsLinked to original sources

arXiv2023

Who Are Tweeting About Academic Publications? A Systematic Review and Meta-Analysis of Altmetric Studies

Understanding who shares academic publications on Twitter is critical to interpreting altmetrics as signals of scholarly or societal impact. Prior studies have used diverse and often incompatible user classification schemes, making synthesis difficult. This study presents a systematic review and meta-analysis of 23 empirical studies (covering 79,014 Twitter users, over 20 million tweets, and more than 5 million tweeted publications) to estimate category-specific engagement across three metrics: user counts, tweets, and tweeted publications. We developed a harmonized categorization scheme encompassing 11 user types and applied both Random Effects Models (REM) and Beta-Binomial Hierarchical Models (BBHM) to estimate proportions, account for study-level variation, and model uncertainty. Across all indicators, individual users were the most active, comprising 66% of users, 55% of tweets, and 50% of tweeted publications. BBHM further enabled in-category vs. out-of-category comparisons and revealed engagement differences not detected by REM. T-tests on study-level means confirmed significant differences between academic individuals and other user types. Despite methodological heterogeneity, results consistently show that academic and non-academic individuals statistically equally dominate Twitter engagement with scholarly content. Our findings support the need for standardized user classification schemes and demonstrate the value of Bayesian modeling for synthesizing altmetric data in study variation and sparsity.

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arXiv2021

Eigenfactor

The Eigenfactor is a journal metric, which was developed by Bergstrom and his colleagues at the University of Washington. They invented the Eigenfactor as a response to the criticism against the use of simple citation counts. The Eigenfactor makes use of the network structure of citations, i.e. citations between journals, and establishes the importance, influence or impact of a journal based on its location in a network of journals. The importance is defined based on the number of citations between journals. As such, the Eigenfactor algorithm is based on Eigenvector centrality. While journal-based metrics have been criticized, the Eigenfactor has also been suggested as an alternative in the widely used San Francisco Declaration on Research Assessment (DORA).

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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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arXiv2014

Tweets as impact indicators: Examining the implications of automated bot accounts on Twitter

This brief communication presents preliminary findings on automated Twitter accounts distributing links to scientific papers deposited on the preprint repository arXiv. It discusses the implication of the presence of such bots from the perspective of social media metrics (altmetrics), where mentions of scholarly documents on Twitter have been suggested as a means of measuring impact that is both broader and timelier than citations. We present preliminary findings that automated Twitter accounts create a considerable amount of tweets to scientific papers and that they behave differently than common social bots, which has critical implications for the use of raw tweet counts in research evaluation and assessment. We discuss some definitions of Twitter cyborgs and bots in scholarly communication and propose differentiating between different levels of engagement from tweeting only bibliographic information to discussing or commenting on the content of a paper.

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arXiv2014

Astrophysicists on Twitter: An in-depth analysis of tweeting and scientific publication behavior

This paper analyzes the tweeting behavior of 37 astrophysicists on Twitter and compares their tweeting behavior with their publication behavior and citation impact to show whether they tweet research-related topics or not. Astrophysicists on Twitter are selected to compare their tweets with their publications from Web of Science. Different user groups are identified based on tweeting and publication frequency. A moderate negative correlation (p=-0.390*) is found between the number of publications and tweets per day, while retweet and citation rates do not correlate. The similarity between tweets and abstracts is very low (cos=0.081). User groups show different tweeting behavior such as retweeting and including hashtags, usernames and URLs. The study is limited in terms of the small set of astrophysicists. Results are not necessarily representative of the entire astrophysicist community on Twitter and they most certainly do not apply to scientists in general. Future research should apply the methods to a larger set of researchers and other scientific disciplines. To a certain extent, this study helps to understand how researchers use Twitter. The results hint at the fact that impact on Twitter can neither be equated with nor replace traditional research impact metrics. However, tweets and other so-called altmetrics might be able to reflect other impact of scientists such as public outreach and science communication. To the best of our knowledge, this is the first in-depth study comparing researchers' tweeting activity and behavior with scientific publication output in terms of quantity, content and impact.

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