Introduction
The aim of this project is to address the problems related to online social network analysis with a focus on the limitations of the Twitter API. We first proposed a novel ontology- based approach for spam detection over Twitter during events by analyzing the relationship between user tweets versus spams. In this context, ontologies are derived and used to generate a dictionary that validates real tweet messages from random topics. Afterwards, we proposed a new framework for sampling online social network. Domain knowledge was used to define tailored strategies that can decrease the budget and time required for mining while increasing the recall. An ontology supported our filtering layer in evaluating the relatedness of nodes.
Contributions
Datasets
Dataset 1
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Dataset 1
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Dataset 1
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