Abstract
Predicting when an individual will adopt a new behavior is an important problem in application domains such as marketing and public health. This paper examines the performance of a wide variety of social network based measurements proposed in the literature - which have not been previously compared directly. We study the probability of an individual becoming influenced based on measurements derived from neighborhood (i.e. number of influencers, personal network exposure), structural diversity, locality, temporal measures, cascade measures, and metadata. We also examine the ability to predict influence based on choice of classifier and how the ratio of positive to negative samples in both training and testing affect prediction results - further enabling practical use of these concepts for social influence applications.
Original language | English (US) |
---|---|
Title of host publication | Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1329-1336 |
Number of pages | 8 |
ISBN (Electronic) | 9781509028467 |
DOIs | |
State | Published - Nov 21 2016 |
Event | 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 - San Francisco, United States Duration: Aug 18 2016 → Aug 21 2016 |
Other
Other | 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 |
---|---|
Country | United States |
City | San Francisco |
Period | 8/18/16 → 8/21/16 |
ASJC Scopus subject areas
- Computer Networks and Communications
- Sociology and Political Science
- Communication