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The identification of influential users in social media communities has been recently of major concern, since these users can contribute to viral marketing campaigns. In our approach we extend the notion of influence from users to networks and consider personality as a key characteristic for identifying influential networks. We describe the Twitter Personality based Influential Communities Extraction...
High dimensionality (the presence of too many features) is a problem which plagues many datasets, including mining from personality profiles. Feature selection can be used to reduce the number of features, and many strategies have been proposed to help select the most important features from a larger group. Feature rankers will produce a metric for each feature and return the best for a given subset...
Many important datasets are affected by the problem of high dimensionality (having a large number of attributes or features), which can result in complex and time-consuming classification models. Feature selection techniques try to identify an optimal subset of features which may show improved classification performance as well as identify important features for the application at hand. Wrapper feature...
The popularity of the Twitter social networking site has made it a target for social bots, which use increasingly-complex algorithms to engage users and pretend to be humans. While much research has studied how to identify such bots in the process of spam detection, little research has looked at the other side of the question — detecting users likely to be fooled by bots. In this paper, we examine...
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