Assessing books’ academic impacts via integrated computation of multi-level citation information

Abstract

Citations have been used as a common basis to measure academic accomplishments of scientific publications, such as articles, journals and books. However, traditional citation analysis ignored content mining and without consideration of citation equivalence, which may lead to the decline of evaluation reliability. Hence, it is urgent to integrate multi-level citation information to conduct multi-dimensional analysis, so as to assessing the academic impacts of the scientific publications more comprehensively and reliably. As an important type of scientific publications, books are an important carrier of cultural exchange and inheritance, especially in the field of humanities and social sciences. However, few of existing research focus on books’ citation analysis. Hence, in this paper, we measured books’ academic impacts by integrating multi-level citation resources, including books’ citation frequencies and citation related contents. Specifically, we firstly counted books’ citation frequencies as the frequency-level metric. Secondly, content-level metrics were detected from multi-dimensional citation contents based on finer-grained mining, including topic extraction on the metadata and citation classification on the citation contexts. Finally, differential metric weighting methods were compared to integrate the multi-level metrics and computing books’ academic impacts. The experimental results indicate that the integration of multiple citation resources is necessary, as it can significantly improve the comprehensiveness of the evaluation results. Meanwhile, compared with the type differences of books, disciplinary differences need more attention when evaluating the academic impacts of books.

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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 — 11 citations exist upstream.

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