Open AccessPredicting Individual’s Posting Behaviors in Social Network with Markov Chain
Author(s) -
Fengcai Qiao,
He Su,
Jinsheng Deng
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1646/1/012030
Subject(s) - markov chain , computer science , contrast (vision) , social network (sociolinguistics) , social media , machine learning , artificial intelligence , world wide web
Online social networks like twitter are self-organized systems with emergent behaviors from the individual interactions. Predicting the users’ behaviors like posting, replying or retweeting is one of the most important issue. In the paper, we present a method to model the twitter users’ posting behavior with the other users’ response. We also contrast the simulation precision with the model that does not involve the other users’ response. We make use of a stochastic model with Markov chain to predict a specific user’s next tweeting behavior. From the result by contrasting the simulation data and the real data, we demonstrate that the engineering method is able to predict individual posting behaviors based on time windows. In addition, the proposed model involving other users’ response is better than that only considering posting time sequences.