A Martingale Approach to Detect Peak of News in Social Network
Nowadays, social medias such as Twitter, Memetracker and Blogs have become powerful tools to propagate information. They facilitate quick dissemination sequence of information such as news article, blog posts, users interests and thoughts through large- scale. Providing strong means to analyzing social networks structure and how information diffuse through them is essential. Many recent studies emphasize on modeling information diffusion and their patterns to gain some useful knowledge.
In this paper, we propose a statistical approach to online detect peak points of news when spread over social networks, to the best of our knowledge has never investigated before. The proposed model use martingale approach to predict peak points (when news reached the peak of its popularity). Experimental results on real datasets show good performance of our approach to online detect these peak points.
Keywords: social network, information diffusion, martingale, peak points
Download Full-Text
ABOUT THE AUTHORS
Saba Babakhani
S.Babakhani is the MSc student in information technology (management information system) of CSE and IT Department of Shiraz University. As one of her research interests, she focuses on social networks specially information diffusion domain.
Niloofar Mozaffari
N.Mozaffari is the Ph.D. student in artificial intelligence of CSE and IT Department of Shiraz University. As one of her research interests, she focuses on social networks specially information diffusion domain.
Ali Hamzeh
A. Hamzeh received his Ph.D. in artificial intelligence from Iran University of Science and Technology (IUST) in 2007. Since then, he has been working as assistant professor in CSE and IT Department of Shiraz University. There, he is one of the founders of local CERT center which serves as the security and protection service provider in its local area. As one of his research interests, he recently focuses on cryptography and steganography area and works as a team leader in CERT center to develop and break steganography method, especially in image spatial domain. Also, he works as one of team leaders of Soft Computing group of Shiraz University working on bio-inspired optimization algorithms. He is co-author of several articles in security and optimization.
Saba Babakhani
S.Babakhani is the MSc student in information technology (management information system) of CSE and IT Department of Shiraz University. As one of her research interests, she focuses on social networks specially information diffusion domain.
Niloofar Mozaffari
N.Mozaffari is the Ph.D. student in artificial intelligence of CSE and IT Department of Shiraz University. As one of her research interests, she focuses on social networks specially information diffusion domain.
Ali Hamzeh
A. Hamzeh received his Ph.D. in artificial intelligence from Iran University of Science and Technology (IUST) in 2007. Since then, he has been working as assistant professor in CSE and IT Department of Shiraz University. There, he is one of the founders of local CERT center which serves as the security and protection service provider in its local area. As one of his research interests, he recently focuses on cryptography and steganography area and works as a team leader in CERT center to develop and break steganography method, especially in image spatial domain. Also, he works as one of team leaders of Soft Computing group of Shiraz University working on bio-inspired optimization algorithms. He is co-author of several articles in security and optimization.
