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Heat diffusion approach for scientific impact analysis in social media

Authors: 
Publication Type: 
Refereed Original Article
Abstract: 
The advent of social media has enabled us to explore the impact of academic entities beyond the conventional bibliometric community. The traditional bibliometric indicators such as citation count, h-index and SNIP metrics aim to represent the propagation of knowledge in the academic world rather than the impact of the research on the wider world. In this study, we are interested in measuring the impact of the academic world on social media. We first create the 2-layered graph where the first layer is the bipartite graph between academic and social media entities and a second layer is the graph between social media entities. Then, we employed the heat diffusion-based metrics to measure the influence of academic entities in social media. Thus, we evaluate the academic entities by (1) predicting links between academic entities and social media and (2) suggesting memes for the academic entities. Our analysis on predicting links between scientist and social media entities showed the AUC-ROC score of 0.73 and the AUC-PR score of 0.30. Similarly, predicting links between scientific publications and social media entities showed the AUC-ROC score of 0.80 and the AUC-PR score of 0.19. Our approach can also suggest textual memes for scientific publications.
Digital Object Identifer (DOI): 
10.1007/s13278-019-0560-3
Publication Status: 
Published
Publication Date: 
25/04/2019
Journal: 
Social Network Analysis and Mining
Volume: 
9
Issue: 
16
Research Group: 
Institution: 
National University of Ireland, Galway (NUIG)
Open access repository: 
No