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Cyberbullying refers to the use of text, images, audio and video to harass or harm individuals or groups on a repetitive and non–stop basis in online social networks. The phenomenon has emerged as a serious societal and public health problem that demands accurate methods for the detection of cyberbullying instances to mitigate the consequences. We perform a detailed analysis of a large–scale real–world...
In this paper, we propose a new discriminative dictionary learning framework, called robust Label Embedding Projective Dictionary Learning (LE-PDL), for data classification. LE-PDL can learn a discriminative dictionary and the blockdiagonal representations without using the l0-norm or l1-norm sparsity regularization, since the l0 or l1-norm constraint on the coding coefficients used in the existing...
We introduce a system for automatically generating warnings of imminent or current cyber-threats. Our system leverages the communication of malicious actors on the darkweb, as well as activity of cyber security experts on social media platforms like Twitter. In a time period between September, 2016 and January, 2017, our method generated 661 alerts of which about 84% were relevant to current or imminent...
The large adoption of Twitter during electioneering has created a valuable opportunity to monitor political deliberation nationwide. Recent work has analyzed online attention to forecast elections results addressing some limitations of opinion polling. However, the reproducibility of such methods remains a challenge given that most of them rely on the number of political parties or candidates mentions...
Automatic creation of polarity dictionaries is an important issue, as explanations of prediction models are often required in the financial industry. This paper proposes a novel method of developing an interpretable and predictable neural network model. The neural network model we built can extract polarity scores of concepts from documents. Furthermore, we can detect pairwise interactions between...
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