TY - JOUR
T1 - Examining Characteristics of Traditional and Twitter Citation
AU - Jung, Hyojung
AU - Lee, Keeheon
AU - Song, Min
N1 - Publisher Copyright:
Copyright © 2016 Jung, Lee and Song.
PY - 2016
Y1 - 2016
N2 - Social media has attracted the attention of the academic community as an emerging communication channel. This channel opens a new opportunity to measure the impact of social use of scholarly publications in social media (altmetrics) that supplements our understanding on the scholarly impact of publications (bibliometrics). Two different channels, social media and journal, are known to establish various citation patterns statistically. However, thematic difference between altmetrics and bibliometrics structurally and contextually is unknown. Therefore, we perform document co-citation network analysis for structural comparison and topic modeling for contextual comparison. We also suggest Spearman’s correlation for statistical comparison. A case study is done for the publications from Journal of the Association for Information Science and Technology and the tweets mentioning the publications. We identified a weak correlation between scholarly impact and social use of these publications. We also found the structures of the traditional citations and Twitter citations share common but high interest in information retrieval system and impact analysis, while Twitter citations have diverse interest in data mining, network analysis, and information behavior as well. In addition, from content analysis, we found the two citation patterns to have both common and distinct characteristics. Specifically, the topics covered by both citation patterns show intersections and exclusive contexts. In conclusion, the traditional citation patterns and the Twitter citation patterns in Information Science are different statistically, structurally, and contextually. We suspect that intentional and unintentional citing behaviors are the main factor for the thematic difference and will be examined on the future.
AB - Social media has attracted the attention of the academic community as an emerging communication channel. This channel opens a new opportunity to measure the impact of social use of scholarly publications in social media (altmetrics) that supplements our understanding on the scholarly impact of publications (bibliometrics). Two different channels, social media and journal, are known to establish various citation patterns statistically. However, thematic difference between altmetrics and bibliometrics structurally and contextually is unknown. Therefore, we perform document co-citation network analysis for structural comparison and topic modeling for contextual comparison. We also suggest Spearman’s correlation for statistical comparison. A case study is done for the publications from Journal of the Association for Information Science and Technology and the tweets mentioning the publications. We identified a weak correlation between scholarly impact and social use of these publications. We also found the structures of the traditional citations and Twitter citations share common but high interest in information retrieval system and impact analysis, while Twitter citations have diverse interest in data mining, network analysis, and information behavior as well. In addition, from content analysis, we found the two citation patterns to have both common and distinct characteristics. Specifically, the topics covered by both citation patterns show intersections and exclusive contexts. In conclusion, the traditional citation patterns and the Twitter citation patterns in Information Science are different statistically, structurally, and contextually. We suspect that intentional and unintentional citing behaviors are the main factor for the thematic difference and will be examined on the future.
KW - Twitter citation
KW - altmetrics
KW - document co-citation analysis
KW - text mining
KW - topic modeling
UR - http://www.scopus.com/inward/record.url?scp=84999689709&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84999689709&partnerID=8YFLogxK
U2 - 10.3389/frma.2016.00006
DO - 10.3389/frma.2016.00006
M3 - Article
AN - SCOPUS:84999689709
SN - 2504-0537
VL - 1
JO - Frontiers in Research Metrics and Analytics
JF - Frontiers in Research Metrics and Analytics
M1 - 6
ER -