A Multidimensional Nonnegative Matrix Factorization Model for Retweeting Behavior Prediction

Mathematical Problems in Engineering, Mar 2015

Today microblogging has increasingly become a means of information diffusion via user’s retweeting behavior. As a consequence, exploring on retweeting behavior is a better way to understand microblog’s transmissibility in the network. Hence, targeted at online microblogging, a directed social network, along with user-based features, this paper first built content-based features, which consisted of URL, hashtag, emotion difference, and interest similarity, based on time series of text information that user posts. And then we measure relationship-based factor in social network according to frequency of interactions and network structure which blend with temporal information. Finally, we utilize nonnegative matrix factorization to predict user’s retweeting behavior from user-based dimension and content-based dimension, respectively, by employing strength of social relationship to constrain objective function. The results suggest that our proposed method effectively increases retweeting behavior prediction accuracy and provides a new train of thought for retweeting behavior prediction in dynamic social networks.

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A Multidimensional Nonnegative Matrix Factorization Model for Retweeting Behavior Prediction

A Multidimensional Nonnegative Matrix Factorization Model for Retweeting Behavior Prediction Mengmeng Wang,1,2 Wanli Zuo,1,2 and Ying Wang1,2,3 1College of Computer Science and Technology, Jilin University, Changchun 130012, China 2Key Laboratory of Symbolic Computation and Knowledge Engineering (Jilin University), Ministry of Education, Changchun 130012, China 3College of Mathematics, Jilin University, Changchun 130012, China Received 22 October 2014; Accepted 6 February 2015 Academic Editor: Sergio Preidikman Copyright © 2015 Mengmeng Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Today microblogging has increasingly become a means of information diffusion via user’s retweeting behavior. As a consequence, exploring on retweeting behavior is a better way to understand microblog’s transmissibility in the network. Hence, targeted at online microblogging, a directed social network, along with user-based features, this paper first built content-based features, which consisted of URL, hashtag, emotion difference, and interest similarity, based on time series of text information that user posts. And then we measure relationship-based factor in social network according to frequency of interactions and network structure which blend with temporal information. Finally, we utilize nonnegative matrix factorization to predict user’s retweeting behavior from user-based dimension and content-based dimension, respectively, by employing strength of social relationship to constrain objective function. The results suggest that our proposed method effectively increases retweeting behavior prediction accuracy and provides a new train of thought for retweeting behavior prediction in dynamic social networks. 1. Introduction With the development of Internet, microblogging has become a novel social media [1, 2]. As a platform, which shares, disseminates, and accesses information based on relationships between users, microblogging is originality, timeliness, grassroots, randomness, debris, and so forth. It is not only a tool for communication and self-expression, but also a means of information releasing and public relations marketing for governments, enterprises, and organizations. The emergence of microblogging greatly speeded up transmission of information in the network. For an instance, in order to share information with friends, a user can quickly copy information which he/she is interested in to his/her own microblogging space through retweeting function which provides a convenient way to air user’s opinion, as well as a way for users to communicate with each other. Thanks to its great flexibility, this way of information transmission is favored by communicators; meanwhile, it brings a viral spread of a microblog through retweeting behavior between different users for its being nonmandatory, targeted and personalized. Moreover, social shared content (such as retweeting a microblog) is not random but depends on the transmissibility of its own. As a consequence, exploring on microblogging retweeting behavior can make us better understand diffusion of information in the network, as well as help identify information credibility [3], reorder user’s tweets [4], and identify interesting tweet [5]. And it can also be used to recommend microblogs to users according to their interested topics which may be reflected in their retweeting microblogs. Furthermore, research has shown that users were more inclined to share contents that can stimulate their emotions [6]. Hence, retweeting behavior prediction is of great significance for emotion analysis and public opinion monitoring. Our work on predicting user’s retweeting behavior is motivated by its broad application prospect. At present, users’ propensities on retweeting remain unclear. Retweeting behavior prediction is in the stage of development; consequently, there are still some unsolved problems in this field. To this end, we proposed a multidimensional nonnegative matrix factorization model for retweeting behavior prediction in social networks (denoted as MNMFRP), and our main contributions are summarized next. (1) Different from previous methods which predicted retweeting behavior without taking user’s emotions into consideration, we put forward a new concept, emotion difference, which represented difference between the emotion reflected in user’s recent contents and a certain microblog’s sentiment. And along with URL, hashtag, and interest similarity, emotion difference was regarded as a content-based factor in the problem of retweeting behavior prediction so as to gain performance. (2) In order to be applied to dynamic networks better, on the basis of time series of user’s contents and user’s network topological information, we considered network as a dynamic flow of time sl (...truncated)


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Mengmeng Wang, Wanli Zuo, Ying Wang. A Multidimensional Nonnegative Matrix Factorization Model for Retweeting Behavior Prediction, Mathematical Problems in Engineering, 2015, 2015, DOI: 10.1155/2015/936397