TY - GEN
T1 - Towards predicting academic impact from mainstream news and weblogs
T2 - 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
AU - Timilsina, Mohan
AU - Davis, Brian
AU - Taylor, Mike
AU - Hayes, Conor
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/11/21
Y1 - 2016/11/21
N2 - The realization that scholarly publications are discussed and have influence on discourse outside scientific and academic domains has given rise to area of scientometrics called alternative metrics or 'altmetrics'. Furthermore, researchers in this field tend to focus primarily on measuring scientific activity on social media platforms such as Twitter, however these count-based metrics are vulnerable to gaming because they tend to lack concrete justification or reference to the primary source. In this collaboration with Elsevier, we extend the conventional citation graph to a heterogeneous graph of publications, scientists, venues, organizations and more authoritative media sources such as mainstream news and weblogs. Our approach consists of two parts: one is integrating the bibliometric data with the social data such as blogs, mainstream news. The other involves understanding how standard graph-based metrics can be used to predict the academic impact. Our result showed the computed graph-based metrics can reasonably predict the academic impact of early stage researchers.
AB - The realization that scholarly publications are discussed and have influence on discourse outside scientific and academic domains has given rise to area of scientometrics called alternative metrics or 'altmetrics'. Furthermore, researchers in this field tend to focus primarily on measuring scientific activity on social media platforms such as Twitter, however these count-based metrics are vulnerable to gaming because they tend to lack concrete justification or reference to the primary source. In this collaboration with Elsevier, we extend the conventional citation graph to a heterogeneous graph of publications, scientists, venues, organizations and more authoritative media sources such as mainstream news and weblogs. Our approach consists of two parts: one is integrating the bibliometric data with the social data such as blogs, mainstream news. The other involves understanding how standard graph-based metrics can be used to predict the academic impact. Our result showed the computed graph-based metrics can reasonably predict the academic impact of early stage researchers.
UR - https://www.scopus.com/pages/publications/85006700515
U2 - 10.1109/ASONAM.2016.7752425
DO - 10.1109/ASONAM.2016.7752425
M3 - Conference Publication
T3 - Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
SP - 1388
EP - 1389
BT - Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
A2 - Kumar, Ravi
A2 - Caverlee, James
A2 - Tong, Hanghang
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 18 August 2016 through 21 August 2016
ER -