Modelling content lifespan in online social networks using data mining Online publication date: Thu, 01-Oct-2015
by John W. Gibbons; Arvin Agah
International Journal of Web Based Communities (IJWBC), Vol. 11, No. 3/4, 2015
Abstract: Online social networks (OSNs) are integrated into business, entertainment, politics, and education; they are integrated into nearly every facet of our everyday lives. They have played essential roles in milestones for humanity, such as the social revolutions in certain countries, to more day-to-day activities, such as streaming entertaining or educational materials. Not surprisingly, social networks are the subject of study, not only for computer scientists, but also for economists, sociologists, political scientists, and psychologists, among others. In this paper, we build a model that is used to classify content on the OSNs of Reddit, 4chan, Flickr, and YouTube - according the types of lifespan their content have and the popularity tiers that the content reaches. The proposed model is evaluated using ten-fold cross-validation, using data mining techniques of sequential minimal optimisation (based on support vector machine), decision table, Naïve Bayes, and random forest. The run times and accuracies are compared across OSNs, models, and data mining algorithms.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Web Based Communities (IJWBC):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook