Active Surveying: A Probabilistic Approach for Identifying Key Opinion Leaders
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 1485-1489.
Abstract
Opinion leaders play an important role in influencing people’s beliefs, actions and behaviors. Although a number of methods have been proposed for identifying influentials using secondary sources of information, the use of primary sources, such as surveys, is still favored in many domains. In this work we present a new surveying method which combines secondary data with partial knowledge from primary sources to guide the information gathering process. We apply our proposed active surveying method to the problem of identifying key opinion leaders in the medical field, and show how we are able to accurately identify the opinion leaders while minimizing the amount of primary data required, which results in significant cost reduction in data acquisition without sacrificing its integrity.
@inproceedings{ijcai2011p250,
title = {Active Surveying: A Probabilistic Approach for Identifying Key Opinion Leaders},
author = {Sharara, Hossam and Getoor, Lise and Norton, Myra},
booktitle = {Proceedings of the Twenty-Second International Joint Conference on
Artificial Intelligence, {IJCAI-11}},
publisher = {AAAI Press/International Joint Conferences on Artificial Intelligence},
editor = {Toby Walsh},
pages = {1485--1489},
year = {2011},
month = {7},
doi = {10.5591/978-1-57735-516-8/IJCAI11-250},
url = {https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-250},
}
TY - CPAPER TI - Active Surveying: A Probabilistic Approach for Identifying Key Opinion Leaders AU - Sharara, Hossam AU - Getoor, Lise AU - Norton, Myra T2 - Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11 PB - AAAI Press/International Joint Conferences on Artificial Intelligence PY - 2011 SP - 1485 EP - 1489 DO - 10.5591/978-1-57735-516-8/IJCAI11-250 UR - https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-250 ER -Copied
Machine-readable versions of this paper, for tools and AI agents: