Abstract
Information propagation plays a significant role in online social networks, mining the latent information produced became crucial to understand how information is disseminated. It can be used for market prediction, rumor controlling, and opinion monitoring among other things. Thus, in this paper, an information dissemination model based on dynamic individual interest is proposed. The basic idea of this model is to extract effective topic of interest of each user overtime and identify the most relevant topics with respect to seed users. A set of experiments on real twitter dataset showed that the proposed dynamic prediction model which applies machine learning techniques outperformed traditional models that only rely on words extracted from tweets.
Highlights
Billions of users are using different social networks (SN), SNs have proven to be effective in communication
Information propagation depends on the users profiles which is represented by their interests, behaviour, and their position in the network which will affect their influence among other users
The number of www.ijacsa.thesai.org iterations was set to 2000 to refine the results as much as possible, 19,272,829 tokens were found in all the tweets collected and used to train the model and create the 500 different topics
Summary
Billions of users are using different social networks (SN), SNs have proven to be effective in communication. Targeted advertisement along with many business applications in the past few years have proved to be very effective and to ensure the maximum efficiency researches have been studying information propagation in major social networks. This effort yields to develop different models that aim to predict how the information would propagate and its speed along with which users could be good candidates of becoming seeds for the information to propagate. Other attributes that represent dynamic features with tagged time slots such as posts, comments and check-ins Such information can be analysed in order to be used in different research areas such as: community detection, user recommendation(Abel et al, 2011; Blanco-Fernández et al, 2011).
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