Networks of keywords for the bibliometric analysis

EUSN 2021

Carlos G. Figuerola [figue@usal.es]
Modesto Escobar [modesto@usal.es]

University of Salamanca, 2021

authors describe the content of their academic articles
they are free vocabulary
- no constraints
- even no normalization
- very wide range of words (most of them with only one use)

low utility for retrieval pourposes
most of author’s keywords occur also inside the title and/or the abstract
journals often include another subject headings, topic classification, etc.
current retrieval technology performs better than searching by such author’s keywords

however
- keywords give a good idea about how authors themselves see their own work
- as keywords connect (co occur) each other, they can show how authors are related with another diciplines, knowledge subfields, etc.
- as science fields are not monolithic, and academic articles are stored in bibliographic databases, keywords analysis can show byass or different ways of view specific disciplines.

internet	1092	social_network	483
social_media	1048	information_system	482
knowledge_management	1034	knowledge_sharing	481
bibliometrics	927	innovation	457
information_retrieval	896	china	457
academic_library	834	trust	429
library	716	information_and_communication_technology	426
information_technology	571	collaboration	425
digital_library	553	electronic_commerce	412
citation_analysis	552	e_government	403
information_literacy	535	research	390
qualitative	512	public_library	368
case_study	489

keywords can be represented by a network in which:
- every keyword is a node
- nodes are connected if they cooccur in the same article
- weigth of connection (edges) is proportional to the frequency of the coincidence
- edges are not directed

Community: some nodes links strongly between them and weakly with outsiders
when nodes are keywords
- a community is a bunch of keywords which are used together frequently
- a community can show thematic clustering of keywords
- the community structure of such a network can show the thematic structure of the research field

several algorithms available
- infomap (Rosvall & Bergstrom …)
- Leiden (Traag, Waltman & van Eck …)

library digital_library academic_library interlending worldwide_web document_delivery electronic_book
knowledge_management knowledge information_system knowledge_sharin case_study outsourcing knowledge_transfer
e_government information_technology information_and_communication_technology telecommunication digital_divide broadband
bibliometrics citation_analysis citation h_index open_access research impact_factor
electronic_commerce trust privacy technology_acceptance_model culture ethic security supply_chain
social_media web_2.0 social_network collaboration network blog facebook twitter
information_retrieval search_engine user_interface world_wide_web user design precision text_mining
internet cd_rom web_survey standard computer_mediated_communication expert_system online_searching
information_literacy e_learning education multimedia training learning information_seeking distance_learning
ontology metadata evaluation semantic_web classification quality preservation
information implementation decision_support_system enterprise_resource_planning_system information_quality
geographical_information_system open_source_software data software_development uncertainty digital_elevation_model map
intellectual_capital data_mining business_intelligence decision_support data_warehouse real_option

network analysis techniques can help in bibliometrics studies
network of author’s keywords can be useful for analizing thematic development of scientific disciplines
community structure of a network of keywords can show the topic’s structure of a scientific field
dynamic networks can be useful to analize the evolution over time of topics inside a scientific discipline

figue@usal.es