Altmetrics and citation counts

Abstract

Background. Researchers, funding agencies, and institutions involve bibliographic data to assess the impact or reputation of papers, publication venues, researchers, and institutions. Particularly citation counts, and metrics that build on these (e.g., impact factor, h-index), are widely used, despite extensive and rightful criticism regarding, for instance, their meaning, value, and comparability. Moreover, such metrics require time to accumulate and do not represent the scientific impact outside of academia, for instance, on industry. To overcome such limitations, researchers investigate and propose altmetrics to complement or provide a more meaningful alternative to traditional metrics. Altmetrics are based on user interactions in the internet and especially social-media platforms, promising a faster accumulation and to represent scientific impact on other parts of society. Aim. In this paper, we complement current research by studying the altmetrics of 18,360 papers published at 16 publication venues of the computer science domain. Method. Namely, we conducted an empirical study to understand whether altmetrics correlate with citation counts and how they have evolved over time. Results. Our results help understand how altmetrics can complement citation counts, and which represent proxy metrics that indicate the immediate impact of a paper as well as future citations. We discuss our results extensively to reflect on the limitations and criticism on such metrics. Conclusion. Our findings suggest that altmetrics can be helpful to complement citation metrics, potentially providing a better picture of overall scientific impact and reducing potential biases of focusing solely on citations.

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No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 16 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.