Technical Papers Parallel Session-I: Evolving HMM for ranking Twitter influence
Abstract/Description
Identifying influence of users in a Twitter network has been researched from quite some time. Many researchers have proposed different models for calculating influence of a particular user in a Twitter network. The motivation has been to target such users for digital marketing or to solicit users who might be performing terrorist activities. The static influence of user has been captured through topology based methods and temporal influence is captured through HMM model. In this research an evolutionary based HMM model for capturing the temporal influence of a Twitter user has been proposed. The reason is Baum Welch algorithm normally used to determine the emission and transition probabilities may converge to a local optimum point. Evolutionary algorithms search random portions of entire solution space and the probability of finding global optima increases.
Keywords
Location
C-9, AMAN CED
Session Theme
Technical Papers Parallel Session-I (Artificial Intelligence)
Session Type
Parallel Technical Session
Session Chair
Dr. Jawwad Shamsi
Start Date
12-12-2015 3:50 PM
End Date
12-12-2015 4:10 PM
Recommended Citation
Thawerani, A. A., & Ghani, S. (2015). Technical Papers Parallel Session-I: Evolving HMM for ranking Twitter influence. International Conference on Information and Communication Technologies. Retrieved from https://ir.iba.edu.pk/icict/2015/2015/6
COinS
Technical Papers Parallel Session-I: Evolving HMM for ranking Twitter influence
C-9, AMAN CED
Identifying influence of users in a Twitter network has been researched from quite some time. Many researchers have proposed different models for calculating influence of a particular user in a Twitter network. The motivation has been to target such users for digital marketing or to solicit users who might be performing terrorist activities. The static influence of user has been captured through topology based methods and temporal influence is captured through HMM model. In this research an evolutionary based HMM model for capturing the temporal influence of a Twitter user has been proposed. The reason is Baum Welch algorithm normally used to determine the emission and transition probabilities may converge to a local optimum point. Evolutionary algorithms search random portions of entire solution space and the probability of finding global optima increases.
