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Rosa, H., Matos, D., Ribeiro, R., Coheur, L. & Carvalho, J. P. (2018). A “deeper” look at detecting cyberbullying in social networks. In 2018 International Joint Conference on Neural Networks (IJCNN). Rio de Janeiro: IEEE.

H. Rosa et al.,  "A “deeper” look at detecting cyberbullying in social networks", in 2018 Int. Joint Conf. on Neural Networks (IJCNN), Rio de Janeiro, IEEE, 2018

@inproceedings{rosa2018_1713502323072,
	author = "Rosa, H. and Matos, D. and Ribeiro, R. and Coheur, L. and Carvalho, J. P.",
	title = "A “deeper” look at detecting cyberbullying in social networks",
	booktitle = "2018 International Joint Conference on Neural Networks (IJCNN)",
	year = "2018",
	editor = "",
	volume = "",
	number = "",
	series = "",
	doi = "10.1109/IJCNN.2018.8489211",
	publisher = "IEEE",
	address = "Rio de Janeiro",
	organization = "",
	url = "https://ieeexplore.ieee.org/document/8489211"
}

TY  - CPAPER
TI  - A “deeper” look at detecting cyberbullying in social networks
T2  - 2018 International Joint Conference on Neural Networks (IJCNN)
AU  - Rosa, H.
AU  - Matos, D.
AU  - Ribeiro, R.
AU  - Coheur, L.
AU  - Carvalho, J. P.
PY  - 2018
SN  - 2161-4407
DO  - 10.1109/IJCNN.2018.8489211
CY  - Rio de Janeiro
UR  - https://ieeexplore.ieee.org/document/8489211
AB  - 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.
ER  -