Konferenzbeitrag

Beyond Citations: Corpus-based Methods for Detecting the Impact of Research Outcomes on Society

This paper proposes, implements and evaluates a novel, corpus-based approach for identifying categories indicative of the impact of research via a deductive (top-down, from theory to data) and an inductive (bottom-up, from data to theory) approach. The resulting categorization schemes differ in substance. Research outcomes are typically assessed by using bibliometric methods, such as citation counts and patterns, or alternative metrics, such as references to research in the media. Shortcomings with these methods are their inability to identify impact of research beyond academia (bibliometrics) and considering text-based impact indicators beyond those that capture attention (altmetrics). We address these limitations by leveraging a mixed-methods approach for eliciting impact categories from experts, project personnel (deductive) and texts (inductive). Using these categories, we label a corpus of project reports per category schema, and apply supervised machine learning to infer these categories from project reports. The classification results show that we can predict deductively and inductively derived impact categories with 76.39% and 78.81% accuracy (F1-score), respectively. Our approach can complement solutions from bibliometrics and scientometrics for assessing the impact of research and studying the scope and types of advancements transferred from academia to society.

Beyond Citations: Corpus-based Methods for Detecting the Impact of Research Outcomes on Society

Urheber*in: Rezapour, Rezvaneh; Bopp, Jutta; Fiedler, Norman; Steffen, Diana; Witt, Andreas; Diesner, Jana

Namensnennung - Nicht kommerziell 4.0 International

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Sprache
Englisch

Thema
Natürliche Sprache
Maschinelles Lernen
Öffentlichkeit
Information Retrieval
Wissenschaft
Rezeption
Sprache

Ereignis
Geistige Schöpfung
(wer)
Rezapour, Rezvaneh
Bopp, Jutta
Fiedler, Norman
Steffen, Diana
Witt, Andreas
Diesner, Jana
Ereignis
Veröffentlichung
(wer)
Paris : European Language Resources Association
(wann)
2020-05-21

URN
urn:nbn:de:bsz:mh39-98422
Letzte Aktualisierung
06.03.2025, 09:00 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
Leibniz-Institut für Deutsche Sprache - Bibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Konferenzbeitrag

Beteiligte

  • Rezapour, Rezvaneh
  • Bopp, Jutta
  • Fiedler, Norman
  • Steffen, Diana
  • Witt, Andreas
  • Diesner, Jana
  • Paris : European Language Resources Association

Entstanden

  • 2020-05-21

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