Title :
User behavior prediction: A combined model of topic level influence and contagion interaction
Author :
Peng Wang ; Qianni Deng
Author_Institution :
Shanghai Jiaotong Univ., Shanghai, China
Abstract :
People post, share and adopt short text/multimedia messages in OSNs every day. Understanding and being able to predict user behaviors in OSNs can be helpful for several areas such as viral marketing and advertisement. In this paper we propose a probabilistic model which combines the impacts from message interactions and topic level social influence to predict the user behavior of adopting contagions. Using two datasets: a collected Weibo data and a DBLP citation network, we testify that the combined model could predict user behavior more accurately.
Keywords :
electronic messaging; human factors; multimedia computing; probability; social networking (online); DBLP citation network; OSNs; Weibo data; advertisement; contagion interaction; message interactions; multimedia messages; probabilistic model; short text messages; topic level social influence; user behavior prediction; viral marketing; Bayes methods; Computational modeling; Data models; Mathematical model; Multimedia communication; Predictive models; Probabilistic logic;
Conference_Titel :
Parallel and Distributed Systems (ICPADS), 2014 20th IEEE International Conference on
DOI :
10.1109/PADSW.2014.7097895
