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Detection of Offensive Language in Online Communication

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Nikola S. Nikolov
added a research item
Offensive content on social media such as verbal attacks, demeaning comments or hate speech has many negative effects on its users. The automatic detection of offensive language on Arabic social media is an important step towards the regulation of such content for Arabic speaking users of social media. This paper presents the results of evaluating the performance of four different neural network architectures for this task: Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (Bi-LSTM), Bi-LSTM with attention mechanism, and a combined CNN-LSTM architecture. These networks are trained and tested on a labeled dataset of Arabic YouTube comments. We run this dataset through a series of pre-processing steps and use Arabic word embeddings to represent the comments. We also apply Bayesian optimization techniques to tune the hyperparameters of the neural network models. We train and test each network using 5-fold cross validation. The CNN-LSTM achieves the highest recall (83.46%), followed by the CNN (82.24%), the Bi-LSTM with attention (81.51%) and the Bi-LSTM (80.97%).
Nikola S. Nikolov
added 2 research items
The goal of the study is to develop data mining and information visualisation techniques which can form the base of a visual analytics solution for effective detection of cyberbullying.
The exponential growth of various social media platforms in recent years has created the opportunity for people to interact and communicate with each other to a degree unprecedented before the invention of the Web. This development is without doubt beneficial for society; however, it has also been associated with an escalation of cyberbullying activities with unacceptable consequences. The goal of this study is to develop data mining and information visualisation techniques which can form the base of a visual analytics solution for effective detection of cyberbullying. In this paper we summarise the main algorithms for text mining with a focus on cyberbullying detection.
Nikola S. Nikolov
added a research item
The goal of our study is to develop techniques based on Natural Language Processing (NLP) for detection of offensive language on a social media platform. This work aims at identifying the characterristics of the language generated on social media by Arabs from multiple countries in the Arab region, and finding proper solutions based on machine learning techniques.