TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Rezapour, Rezvaneh
A1  - Bopp, Jutta
A1  - Fiedler, Norman
A1  - Steffen, Diana
A1  - Witt, Andreas
A1  - Diesner, Jana
ED  - Calzolari, Nicoletta
ED  - Béchet, Frédéric
ED  - Blache, Philippe
ED  - Choukri, Khalid
ED  - Cieri, Christopher
ED  - Declerck, Thierry
ED  - Goggi, Sara
ED  - Isahara, Hitoshi
ED  - Maegaard, Bente
ED  - Mariani, Joseph
ED  - Mazo, Hélène
ED  - Moreno, Asuncion
ED  - Odijk, Jan
ED  - Piperidis, Stelios
T1  - Beyond Citations: Corpus-based Methods for Detecting the Impact of Research Outcomes on Society
T2  - Proceedings of the 12th International Conference on Language Resources and Evaluation (LREC), May 11-16, 2020, Palais du Pharo, Marseille, France
N2  - 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.
KW  - impact assessment
KW  - natural language processing
KW  - machine learning
KW  - corpus analysis
KW  - category detection
KW  - Natürliche Sprache
KW  - Maschinelles Lernen
KW  - Öffentlichkeit
KW  - Information Retrieval
KW  - Wissenschaft
KW  - Rezeption
Y1  - 2020
U6  - https://nbn-resolving.org/urn:nbn:de:bsz:mh39-98422
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:mh39-98422
UR  - http://www.lrec-conf.org/proceedings/lrec2020/index.html#6777
SN  - 979-10-95546-34-4
SB  - 979-10-95546-34-4
SP  - 6777
EP  - 6785
PB  - European Language Resources Association
CY  - Paris
ER  -