Smart Cyber Victimization Discovery on Twitter

[EN] The advancement of technologies, the promotion of smart-phones, and social networking have led to a high tendency among users to spend more time online interacting with each other via the available technologies. This is because they help overcome physical limitations and save time and energy by doing everything online. The rapid growth in this tendency has created the need for extra protection, by creating new rules and policies. However, sometimes users interrupt these rules and policies through unethical behavior. For example, bullying on social media platforms is a type of cyber victimization that can cause serious harm to individuals, leading to suicide. A firm step towards protecting the cyber society from victimization is to detect the topics that trigger the feeling of being a victim. In this paper, the focus is on Twitter, but it can be expanded to other platforms. The proposed method discovers cyber victimization by detecting the type of behavior leading to them being a victim. It consists of a text classification model, that is trained with a collected dataset of the official news since 2000, about suicide, self-harm, and cyberbullying. Results show that LinearSVC performs slightly better with an accuracy of 96%.This research has been supported by the project "Intelligent and sustainable mobility supported by multi-agent systems and edge computing (InEDGE-Mobility): Towards Sustainable Intelligent Mobility: Blockchain-based framework for IoT Security", Reference: RTI2018-095390-B-C31/32/33, financed by the Spanish Ministry of Science, Innovation and Universities (MCIU), the State Research Agency (AEI) and the European Regional Development Fund (FEDER).Shoeibi, N.; Shoeibi, N.; Julian, V.; Ossowski, S.; González Arrieta, A.; Chamoso, P. (2021). Smart Cyber Victimization Discovery on Twitter. Springer. 289-299. https://doi.org/10.1007/978-3-030-78901-5_2528929

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Applications of Artificial Intelligence and Graphy Theory to Cyberbullying

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Relationship Between Personality Patterns and Harmfulness : Analysis and Prediction Based on Sentence Embedding

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Aggressive, Repetitive, Intentional, Visible, and Imbalanced: Refining Representations for Cyberbullying Classification

Cyberbullying is a pervasive problem in online communities. To identify cyberbullying cases in large-scale social networks, content moderators depend on machine learning classifiers for automatic cyberbullying detection. However, existing models remain unfit for real-world applications, largely due to a shortage of publicly available training data and a lack of standard criteria for assigning ground truth labels. In this study, we address the need for reliable data using an original annotation framework. Inspired by social sciences research into bullying behavior, we characterize the nuanced problem of cyberbullying using five explicit factors to represent its social and linguistic aspects. We model this behavior using social network and language-based features, which improve classifier performance. These results demonstrate the importance of representing and modeling cyberbullying as a social phenomenon.Comment: 12 pages, 5 figures, 22 tables, Accepted to the 14th International AAAI Conference on Web and Social Media, ICWSM'2

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There is growing interest in automating crime detection and prevention for large populations as a result of the increased usage of social media for victimization and criminal activities. This area is frequently researched due to its potential for enabling criminals to reach a large audience. While several studies have investigated specific crimes on social media, a comprehensive review paper that examines all types of social media crimes, their similarities, and detection methods is still lacking. The identification of similarities among crimes and detection methods can facilitate knowledge and data transfer across domains. The goal of this study is to collect a library of social media crimes and establish their connections using a crime taxonomy. The survey also identifies publicly accessible datasets and offers areas for additional study in this area

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Challenges in Modifying Existing Scales for Detecting Harassment in Individual Tweets

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