Influential users play an important role in online social networks since users tend to have an impact on one other. Therefore, the proposed work analyzes users and their behavior in order to identify influential users and predict user participation. Normally, the success of a social media site is dependent on the activity level of the participating users. For both online social networking sites and individual users, it is of interest to find out if a topic will be interesting or not. In this article, we propose association learning to detect relationships between users. In order to verify the findings, several experiments were executed based on social network analysis, in which the most influential users identified from association rule learning were compared to the results from Degree Centrality and Page Rank Centrality. The results clearly indicate that it is possible to identify the most influential users using association rule learning. In addition, the results also indicate a lower execution time compared to state-of-the-art methods.
Other popular areas of research focus on popularity prediction in social media based on comment mining @cite_35 , predicting information cascade on social media @cite_2 , and predicting patterns of diffusion processes in social network @cite_14 . An important factor is often the user's role in the different processes. As such, identifying influential users are of interest to understand and or affect the spread of information, e.g., viral marketing. The ability to identify influential users might also affect the research into other areas of related work (e.g., ii or iii).
@cite_35 @cite_14 @cite_2
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Using comment information available from Digg we define a co-participation network between users. We focus on the analysis of this implicit network, and study the behavioral characteristics of users. Using an entropy measure, we infer that users at Digg are not highly focused and participate across a wide range of topics. We also use the comment data and social network derived features to predict the popularity of online content linked at Digg using a classification and regression framework. We show promising results for predicting the popularity scores even after limiting our feature extraction to the first few hours of comment activity that follows a Digg submission. Viral campaigns on the Internet may follow variety of models, depending on the content, incentives, personal attitudes of sender and recipient to the content and other factors. Due to the fact that the knowledge of the campaign specifics is essential for the campaign managers, researchers are constantly evaluating models and real-world data. The goal of this article is to present the new knowledge obtained from studying two viral campaigns that took place in a virtual world which followed the branching process. The results show that it is possible to reduce the time needed to estimate the model parameters of the campaign and, moreover, some important aspects of time-generations relationship are presented. Twitter is one of the very popular micro-blogging platforms for people to share content and information. Information propagates through the interaction between users with many different ways, such as retweet, mention or reply. With those abilities, Twitter has become one of the medium for advertisers to perform the marketing campaign. Sometimes in their campaign, advertisers hire several buzzers to make the campaign activity running more organically. In this paper we will discuss how to predict information cascades, in term of number interaction over the network that will happen just after buzzer doing its campaign. We formulate the task into a regression problem and define a feature-set, then extract the features on initial interaction data to build a model for prediction. Our experiment shows that Support Vector Regression (SVR) is better than Linear Regression (LR) algorithm. SVR has Mean Absolute Error (MAE) ranged from 1.54 to 33.93. We also found that the optimal setting of initial interaction data is 2 hours time lag which hit the lowest MAE 1.54.