| dc.description.abstract | Social networking web sites (SNWs) have become one of the several main sites where people spend most of their time. People can present themselves on their individual profiles, make links to other users, and communicate with them on SNWs. Facebook is one of the most popular media of SNWs and becomes the top most-trafficked website in the world. In order to contact with on-line customers, many corporations have their own page on Facebook.
In this study, we focus on Facebook and build an ensemble model to predict the influence of posts in the future based on content features, temporal features, and authorial features. The ensemble model integrates results from Neural Network, Decision Tree (C5.0), Logistic Regression, Naive Bayes, and Support Vector Machines (SVM) by voting method. Different from previous research in predicting influence of posts on SNWs which only consider their access counts and neglect different influence of individual user, this work assumes that each user is associated with a weight to reflect his influence in social network and the influence of a post on Facebook is defined as the weighted sum of the influence of the users who clicked “like.
Our experiments are executed by the data mining tool, Clementine, and performed by a 10-fold cross-validation. Experiment results show that the predicting performance of our ensemble model outperforms each individual classifier and the features we propose can significantly improve the prediction of posts’’ influence. The results also show that our model, which considers different weights of users, can achieve higher accuracy than traditional model, which treats all users the same, in predicting influence of posts.
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