Presenting a Labelled Dataset for Real-Time Detection of Abusive User Posts

Social media sites facilitate users in posting their own personal comments online. Most support free format user posting, with close to real-time publishing speeds. However, online posts generated by a public user audience carry the risk of containing inappropriate, potentially abusive content. To detect such content, the straightforward approach is to filter against blacklists of profane terms. However, this lexicon filtering approach is prone to problems around word variations and lack of context. Although recent methods inspired by machine learning have boosted detection accuracies, the lack of gold standard labelled datasets limits the development of this approach. In this work, we present a dataset of user comments, using crowdsourcing for labelling. Since abusive content can be ambiguous and subjective to the individual reader, we propose an aggregated mechanism for assessing different opinions from different labellers. In addition, instead of the typical binary categories of abusive or not, we introduce a third class of ‘undecided’ to capture the real life scenario of instances that are neither blatantly abusive nor clearly harmless. We have performed preliminary experiments on this dataset using best practice techniques in text classification. Finally, we have evaluated the detection performance of various feature groups, namely syntactic, semantic and context-based features. Results show these features can increase our classifier performance by 18% in detection of abusive content

The Bullying Game: Sexism Based Toxic Language Analysis on Online Games Chat Logs by Text Mining

As a unique type of social network, the online gaming industry is a fast-growing, changing, and men-dominated field which attracts diverse backgrounds. Being dominated by male users, game developers, game players, game investors, the non-inclusiveness and gender inequality reside as salient problems in the community. In the online gaming communities, most women players report toxic and offensive language or experiences of verbal abuse. Symbolic interactionists and feminists assume that words matter since the use of particular language and terms can dehumanize and harm particular groups such as women. Identifying and reporting the toxic behavior, sexism, and harassment that occur in online games is a critical need in preventing cyberbullying, and it will help gender diversity and equality grow in the online gaming industry. However, the research on this topic is still rare, except for some milestone studies. This paper aims to contribute to the theory and practice of sexist toxic language detection in the online gaming community, through the automatic detection and analysis of toxic comments in online games chat logs. We adopted the MaXQDA tool as a data visualization technique to reveal the most frequently used toxic words used against women in online gaming communities. We also applied the Naïve Bayes Classifier for text mining to classify if a chat log content is sexist and toxic. We also refined the text mining model Laplace estimator and re-tested the model’s accuracy. The study also revealed that the accuracy of the Naïve Bayes Classifier did not change by the Laplace estimator. The findings of the study are expected to raise awareness about the use of gender-based toxic language in the online gaming community. Moreover, the proposed mining model can inspire similar research on practical tools to help moderate the use of sexist toxic language and disinfect these communities from gender-based discrimination and sexist bullying

Abusive Text Detection Using Neural Networks

eural network models have become increasingly popular for text classification in recent years. In particular, the emergence of word embeddings within deep learning architectures has recently attracted a high level of attention amongst researchers. In this paper, we focus on how neural network models have been applied in text classification. Secondly, we extend our previous work [4, 3] using a neural network strategy for the task of abusive text detection. We compare word embedding features to the traditional feature representations such as n-grams and handcrafted features. In addition, we use an off-the-shelf neural network classifier, FastText[16]. Based on our results, the conclusions are: (1) Extracting selected manual features can increase abusive content detection over using basic ngrams; (2) Although averaging pre-trained word embeddings is a naive method, the distributed feature representation has better performance to ngrams in most of our datasets; (3) While the FastText classifier works efficiently with fast performance, the results are not remarkable as it is a shallow neural network with only one hidden layer; (4) Using pre-trained word embeddings does not guarantee better performance in the FastText classifie

Abusive Text Detection Using Neural Networks

Neurall network models have become increasingly popular for text classification in recent years. In particular, the emergence of word embeddings within deep learning architecture has recently attracted a high level of attention amongst researchers

Detecting Hacker Threats: Performance of Word and Sentence Embedding Models in Identifying Hacker Communications

Abstract—Cyber security is striving to find new forms of protection against hacker attacks. An emerging approach nowadays is the investigation of security-related messages exchanged on deep/dark web and even surface web channels. This approach can be supported by the use of supervised machine learning models and text mining techniques. In our work, we compare a variety of machine learning algorithms, text representations and dimension reduction approaches for the detection accuracies of software-vulnerability-related communications. Given the imbalanced nature of the three public datasets used, we investigate appropriate sampling approaches to boost detection accuracies of our models. In addition, we examine how feature reduction techniques such as Document Frequency Reduction, Chi-square and Singular Value Decomposition (SVD) can be used to reduce the number of features of the model without impacting the detection performance. We conclude that: (1) a Support Vector Machine (SVM) algorithm used with traditional Bag of Words achieved highest accuracies (2) The increase of the minority class with Random Oversampling technique improves the detection performance of the model by 5% on average, and (3) The number of features of the model can be reduced by up to 10% without affecting the detection performance. Also, we have provided the labelled dataset used in this work for further research. These findings can be used to support Cyber Security Threat Intelligence (CTI) with respect to the use of text mining techniques for detecting security-related communication

The big five: Discovering linguistic characteristics that typify distinct personality traits across Yahoo! answers members

Indexación: Scopus.This work was partially supported by the project FONDECYT “Bridging the Gap between Askers and Answers in Community Question Answering Services” (11130094) funded by the Chilean Government.In psychology, it is widely believed that there are five big factors that determine the different personality traits: Extraversion, Agreeableness, Conscientiousness and Neuroticism as well as Openness. In the last years, researchers have started to examine how these factors are manifested across several social networks like Facebook and Twitter. However, to the best of our knowledge, other kinds of social networks such as social/informational question-answering communities (e.g., Yahoo! Answers) have been left unexplored. Therefore, this work explores several predictive models to automatically recognize these factors across Yahoo! Answers members. As a means of devising powerful generalizations, these models were combined with assorted linguistic features. Since we do not have access to ask community members to volunteer for taking the personality test, we built a study corpus by conducting a discourse analysis based on deconstructing the test into 112 adjectives. Our results reveal that it is plausible to lessen the dependency upon answered tests and that effective models across distinct factors are sharply different. Also, sentiment analysis and dependency parsing proven to be fundamental to deal with extraversion, agreeableness and conscientiousness. Furthermore, medium and low levels of neuroticism were found to be related to initial stages of depression and anxiety disorders. © 2018 Lithuanian Institute of Philosophy and Sociology. All rights reserved.https://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/275

A Comparison of Classical Versus Deep Learning Techniques for Abusive Content Detection on Social Media Sites

The automated detection of abusive content on social media websites faces a variety of challenges including imbalanced training sets, the identification of an appropriate feature representation and the selection of optimal classifiers. Classifiers such as support vector machines (SVM), combined with bag of words or ngram feature representation, have traditionally dominated in text classification for decades. With the recent emergence of deep learning and word embeddings, an increasing number of researchers have started to focus on deep neural networks. In this paper, our aim is to explore cutting-edge techniques in automated abusive content detection. We use two deep learning approaches: convolutional neural networks (CNNs) and recurrent neural networks (RNNs). We apply these to 9 public datasets derived from various social media websites. Firstly, we show that word embeddings pre-trained on the same data source as the subsequent classification task improves the prediction accuracy of deep learning models. Secondly, we investigate the impact of different levels of training set imbalances on classifier types. In comparison to the traditional SVM classifier, we identify that although deep learning models can outperform the classification results of the traditional SVM classifier when the associated training dataset is seriously imbalanced, the performance of the SVM classifier can be dramatically improved through the use of oversampling, surpassing the deep learning models. Our work can inform researchers in selecting appropriate text classification strategies in the detection of abusive content, including scenarios where the training datasets suffer from class imbalance

Eavesdropping Hackers: Detecting Software Vulnerability Communication on Social Media Using Text Mining

Abstract—Cyber security is striving to find new forms of protection against hacker attacks. An emerging approach nowadays is the investigation of security-related messages exchanged on Deep/Dark Web and even Surface Web channels. This approach can be supported by the use of supervised machine learning models and text mining techniques. In our work, we compare a variety of machine learning algorithms, text representations and dimension reduction approaches for the detection accuracies of software-vulnerability-related communications. Given the imbalanced nature of the three public datasets used, we investigate appropriate sampling approaches to boost detection accuracies of our models. In addition, we examine how feature reduction techniques, such as Document Frequency Reduction, Chi-square and Singular Value Decomposition (SVD) can be used to reduce the number of features of the model without impacting the detection performance. We conclude that: (1) a Support Vector Machine (SVM) algorithm used with traditional Bag of Words achieved highest accuracies (2) The increase of the minority class with Random Oversampling technique improves the detection performance of the model by 5% on average, and (3) The number of features of the model can be reduced by up to 10% without affecting the detection performance. Also, we have provided the labelled dataset used in this work for further research. These findings can be used to support Cyber Security Threat Intelligence (CTI) with respect to the use of text mining techniques for detecting security-related communicatio