8,530 research outputs found

    Detecting Online Harassment in Social Networks

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    Online Harassment is the process of sending messages over electronic media to cause psychological harm to a victim. In this paper, we propose a pattern-based approach to detect such messages. Since user generated texts contain noisy language, we perform a normalization step first to transform the words into their canonical forms. Additionally, we introduce a person identification module that marks phrases which relate to a person. Our results show that these preprocessing steps increase the classification performance. The pattern-based classifier uses the information provided by the preprocessing steps to detect patterns that connect a person to profane words. This technique achieves a substantial improvement compared to existing approaches. Finally, we discuss the portability of our approach to Social Networks and its possible contribution to tackle the abuse of such applications for the distribution of Online Harassment

    Applications of Artificial Intelligence and Graphy Theory to Cyberbullying

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    Cyberbullying is an ongoing and devastating issue in today\u27s online social media. Abusive users engage in cyber-harassment by utilizing social media to send posts, private messages, tweets, or pictures to innocent social media users. Detecting and preventing cases of cyberbullying is crucial. In this work, I analyze multiple machine learning, deep learning, and graph analysis algorithms and explore their applicability and performance in pursuit of a robust system for detecting cyberbullying. First, I evaluate the performance of the machine learning algorithms Support Vector Machine, Naïve Bayes, Random Forest, Decision Tree, and Logistic Regression. This yielded positive results and obtained upwards of 86% accuracy. Further enhancements were achieved using Evolutionary Algorithms, improving the overall results of the machine learning models. Deep Learning algorithms was the next experiment in which efficiency was monitored in terms of training time and performance. Next, analysis of Recurrent Neural Networks and Hierarchical Attention Networks was conducted, achieving 82% accuracy. The final research project used graph analysis to explore the relation among different social media users, and analyze the connectivity and communities of users who were discovered to have posted offensive messages

    Cyberpsychology and Human Factors

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    The online environment has become a significant focus of the everyday behaviour and activities of individuals and organisations in contemporary society. The increasing mediation of communication has led to concerns about the potential risks and associated negative experiences which can occur to users, particularly children and young people. This is related to the emergence of the online environment as a location for criminal and abusive behaviour (e.g., harassment, sexual exploitation, fraud, hacking, malware). One of the key aspects of understanding online victimisation and engagement in criminal behaviours is the characteristics of online communication that are related to the affordances of the technologies, services and applications which constitute digital environments. The aim of this paper is to examine the influence of these characteristics on individual and group behaviour, as well as the associated opportunities for victimisation and criminal behaviour. These issues are of relevance for those involved in the design and implementation of technologies and services, as the ability to assess their potential use in this way can enhance strategies for improving the security of systems and users. It can also inform educational strategies for increasing user understanding of potential informational, privacy and personal risks, and associated steps to improve their security and privacy. Each of the main characteristics of mediated communication is examined, as well as their potential impact on individual and group behaviour, and associated opportunities for victimisation and offending. The article ends by considering the importance of recognising these issues when designing and implementing new technologies, services and applications

    Hate is not Binary: Studying Abusive Behavior of #GamerGate on Twitter

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    Over the past few years, online bullying and aggression have become increasingly prominent, and manifested in many different forms on social media. However, there is little work analyzing the characteristics of abusive users and what distinguishes them from typical social media users. In this paper, we start addressing this gap by analyzing tweets containing a great large amount of abusiveness. We focus on a Twitter dataset revolving around the Gamergate controversy, which led to many incidents of cyberbullying and cyberaggression on various gaming and social media platforms. We study the properties of the users tweeting about Gamergate, the content they post, and the differences in their behavior compared to typical Twitter users. We find that while their tweets are often seemingly about aggressive and hateful subjects, "Gamergaters" do not exhibit common expressions of online anger, and in fact primarily differ from typical users in that their tweets are less joyful. They are also more engaged than typical Twitter users, which is an indication as to how and why this controversy is still ongoing. Surprisingly, we find that Gamergaters are less likely to be suspended by Twitter, thus we analyze their properties to identify differences from typical users and what may have led to their suspension. We perform an unsupervised machine learning analysis to detect clusters of users who, though currently active, could be considered for suspension since they exhibit similar behaviors with suspended users. Finally, we confirm the usefulness of our analyzed features by emulating the Twitter suspension mechanism with a supervised learning method, achieving very good precision and recall.Comment: In 28th ACM Conference on Hypertext and Social Media (ACM HyperText 2017

    Comparative Studies of Detecting Abusive Language on Twitter

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    The context-dependent nature of online aggression makes annotating large collections of data extremely difficult. Previously studied datasets in abusive language detection have been insufficient in size to efficiently train deep learning models. Recently, Hate and Abusive Speech on Twitter, a dataset much greater in size and reliability, has been released. However, this dataset has not been comprehensively studied to its potential. In this paper, we conduct the first comparative study of various learning models on Hate and Abusive Speech on Twitter, and discuss the possibility of using additional features and context data for improvements. Experimental results show that bidirectional GRU networks trained on word-level features, with Latent Topic Clustering modules, is the most accurate model scoring 0.805 F1.Comment: ALW2: 2nd Workshop on Abusive Language Online to be held at EMNLP 2018 (Brussels, Belgium), October 31st, 201

    Towards Measuring Adversarial Twitter Interactions against Candidates in the US Midterm Elections

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    Adversarial interactions against politicians on social media such as Twitter have significant impact on society. In particular they disrupt substantive political discussions online, and may discourage people from seeking public office. In this study, we measure the adversarial interactions against candidates for the US House of Representatives during the run-up to the 2018 US general election. We gather a new dataset consisting of 1.7 million tweets involving candidates, one of the largest corpora focusing on political discourse. We then develop a new technique for detecting tweets with toxic content that are directed at any specific candidate.Such technique allows us to more accurately quantify adversarial interactions towards political candidates. Further, we introduce an algorithm to induce candidate-specific adversarial terms to capture more nuanced adversarial interactions that previous techniques may not consider toxic. Finally, we use these techniques to outline the breadth of adversarial interactions seen in the election, including offensive name-calling, threats of violence, posting discrediting information, attacks on identity, and adversarial message repetition
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