Detecting hate speech on twitter using a convolution-GRU based deep neural network

In recent years, the increasing propagation of hate speech on social media and the urgent need for effective counter-measures have drawn significant investment from governments, companies, as well as empirical research. Despite a large number of emerging scientific studies to address the problem, existing methods are limited in several ways, such as the lack of comparative evaluations which makes it difficult to assess the contribution of individual works. This paper introduces a new method based on a deep neural network combining convolutional and long short term memory networks, and conducts an extensive evaluation of the method against several baselines and state of the art on the largest collection of publicly available datasets to date. We show that our proposed method outperforms state of the art on 6 out of 7 datasets by between 0.2 and 13.8 points in F1. We also carry out further analysis using automatic feature selection to understand the impact of the conventional manual feature engineering process that distinguishes most methods in this field. Our findings challenge the existing perception of the importance of feature engineering, as we show that: the automatic feature selection algorithm drastically reduces the original feature space by over 90% and selects predominantly generic features from datasets; nevertheless, machine learning algorithms perform better using automatically selected features than the original features

Detecting Inappropriate Comments to News

Inappropriate comments, defined as deliberately offensive, off-topic, troll-like, or direct attacks based on religious, sexual, racial, gender, or ethnic posts, are becoming increasingly problematic in user-generated content on the internet, because they can either derail the conversation or spread out harassment. Furthermore, the computational analysis of this kind of content, posted in response to professional news-papers, is not well investigated yet. To such an extent, the most predictive linguistic and cognitive features were seldom been addressed, and inappropriateness was not investigated deeply. After collecting a new dataset of inappropriate comments, three classic machine learning models were tested over two possible representations for the data to fed in: normal and distorted. Text distortion technique, thanks to its ability to mask thematic information, enhanced classification performance resulting in the valuable ground in which extract features from. Lexicon based features showed to be the most valuable characteristics to consider. Logistic regression turned out to be the most efficient algorithm

Misogynistic tweet detection: Modelling CNN with small datasets

Online abuse directed towards women on the social media platform such as Twitter has attracted considerable attention in recent years. An automated method to effectively identify misogynistic abuse could improve our understanding of the patterns, driving factors, and effectiveness of responses associated with abusive tweets over a sustained time period. However, training a neural network (NN) model with a small set of labelled data to detect misogynistic tweets is difficult. This is partly due to the complex nature of tweets which contain misogynistic content, and the vast number of parameters needed to be learned in a NN model. We have conducted a series of experiments to investigate how to train a NN model to detect misogynistic tweets effectively. In particular, we have customised and regularised a Convolutional Neural Network (CNN) architecture and shown that the word vectors pre-trained on a task-specific domain can be used to train a CNN model effectively when a small set of labelled data is available. A CNN model trained in this way yields an improved accuracy over the state-of-the-art models