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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.

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Metadaten
Author:Rezvaneh Rezapour, Jutta BoppGND, Norman FiedlerGND, Diana Steffen, Andreas WittORCiDGND, Jana Diesner
URN:urn:nbn:de:bsz:mh39-98422
URL:http://www.lrec-conf.org/proceedings/lrec2020/index.html#6777
ISBN:979-10-95546-34-4
Parent Title (English):Proceedings of the 12th International Conference on Language Resources and Evaluation (LREC), May 11-16, 2020, Palais du Pharo, Marseille, France
Publisher:European Language Resources Association
Place of publication:Paris
Editor:Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Document Type:Conference Proceeding
Language:English
Year of first Publication:2020
Date of Publication (online):2020/05/21
Publicationstate:Zweitveröffentlichung
Reviewstate:Peer-Review
Tag:category detection; corpus analysis; impact assessment; machine learning; natural language processing
GND Keyword:Information Retrieval; Maschinelles Lernen; Natürliche Sprache; Rezeption; Wissenschaft; Öffentlichkeit
First Page:6777
Last Page:6785
DDC classes:400 Sprache / 400 Sprache, Linguistik
Open Access?:ja
Leibniz-Classification:Sprache, Linguistik
Linguistics-Classification:Computerlinguistik
Linguistics-Classification:Korpuslinguistik
Licence (English):License LogoCreative Commons - Attribution-NonCommercial 4.0 International