The Journal of Machine Learning Research
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Formal models for expert finding in enterprise corpora
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Expertise modeling for matching papers with reviewers
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
ArnetMiner: extraction and mining of academic social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Constrained multi-aspect expertise matching for committee review assignment
Proceedings of the 18th ACM conference on Information and knowledge management
TwitterRank: finding topic-sensitive influential twitterers
Proceedings of the third ACM international conference on Web search and data mining
Probabilistic models for expert finding
ECIR'07 Proceedings of the 29th European conference on IR research
Everyone's an influencer: quantifying influence on twitter
Proceedings of the fourth ACM international conference on Web search and data mining
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The emergence of social media has created new ways to publish scientific work, foster collaboration, and build professional connections in the research community. The rich data collected in social media platforms has provided new opportunities for assessing scholars' impact other than the traditional citation-based approach. In this paper, we investigate the measures of scholars' influence in academic social media platforms, taking both academic and social impact into account. A real-life dataset collected from Mendeley is used to apply different influence metrics. We first assess the academic influence of scholars based on the scientific impact of their publications using three different measures. Then we investigate their social influence using network centrality metrics. The experiments show that top influencers with high academic impact tend to be senior scholars with many coauthors. Furthermore, academic influence and social influence measures do not strongly correlate with each other, and thus scholars with high academic impact are not necessarily influential from a social point of view. Adding the social dimension could enhance the traditional impact metrics that only take academic influence into account.