A “Deeper” Look at Detecting Cyberbullying in Social Networks | IEEE Conference Publication | IEEE Xplore

A “Deeper” Look at Detecting Cyberbullying in Social Networks

Publisher: IEEE

Abstract:

As cyberbullying becomes more and more frequent in social networks, automatically detecting it and pro-actively acting upon it becomes of the utmost importance. In this w...View more

Abstract:

As cyberbullying becomes more and more frequent in social networks, automatically detecting it and pro-actively acting upon it becomes of the utmost importance. In this work, a detailed look at the current state-of-the-art in cyberbullying detection reveals that deep learning techniques have seldom been used to tackle this problem, despite growing reputation in other text-based classification tasks. Motivated by neural networks' documented success, three architectures are implemented from similar works: a simple CNN, a hybrid CNN-LSTM and a mixed CNN-LSTM-DNN. In addition, three text representations are trained from three different sources, via the word2vec model: Google-News, Twitter and Formspring. The experiment shows that these models with one of the above embeddings beat other benchmark classifiers (Support Vector Machines and Logistic Regression) both in an unbalanced and balanced version of the same dataset.
Date of Conference: 08-13 July 2018
Date Added to IEEE Xplore: 14 October 2018
ISBN Information:
Electronic ISSN: 2161-4407
Publisher: IEEE
Conference Location: Rio de Janeiro, Brazil

References

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