Seminar Users in the Arabic Twitter Sphere

We introduce the notion of "seminar users", who are social media users engaged in propaganda in support of a political entity. We develop a framework that can identify such users with 84.4% precision and 76.1% recall. While our dataset is from the Arab region, omitting language-specific features has only a minor impact on classification performance, and thus, our approach could work for detecting seminar users in other parts of the world and in other languages. We further explored a controversial political topic to observe the prevalence and potential potency of such users. In our case study, we found that 25% of the users engaged in the topic are in fact seminar users and their tweets make nearly a third of the on-topic tweets. Moreover, they are often successful in affecting mainstream discourse with coordinated hashtag campaigns.Comment: to appear in SocInfo 201

Comparison Support Vector Machine and Naive Bayes Methods for Classifying Cyberbullying in Twitter

Twitter users in Indonesia in 2019 were recorded at 6.43 million. The high level of Twitter users makes it allows for free opinion to anyone, it can cause cyberbullying. Victims of cyberbullying experienced higher levels of depression than other verbal acts of violence. The forms of cyberbullying that occurs on Twitter are Flamming, Denigration, and Body Shaming. The research contribution is able to make social media developers and users more aware of the type of cyberbullying that social media users sometimes do without realizing it. Social media developers can prevent cyberbullying by using policies such as word detection and filtering features that indicate cyberbullying more accurately by classifying it by type and using the most accurate method. To classify cyberbullying forms in twitter, in this study we use the Naïve Bayes method and Support Vector Machine (SVM) and compare them based on classification accuracy. This research will also identify words that are characteristic of each category of cyberbullying so that each category is easy to identify by social media users and makes it easier to avoid cyberbullying. The results of this study are the classification accuracy of Naïve Bayes of 97.99% and the classification accuracy of SVM of 99.60%. It means that SVM is better than Naïve Bayes for classifying the forms of cyberbullying in Twitter

Detecting psycho-anomalies on the world-wide web: current tools and challenges

The rise of the use of Social Media and the overall progress of technology has unfortunately opened new ways for criminals such as paedophiles, serial killers and rapists to exploit the powers that the technology offers in order to lure potential victims. It is of great need to be able to detect extreme criminal behaviours on the World-Wide Web and take measures to protect the general public from the effects of such behaviours. The aim of this chapter is to examine the current data analysis tools and technologies that are used to detect extreme online criminal behaviour and the challenges that exist associated with the use of these technologies. Specific emphasis is given to extreme criminal behaviours such as paedophilia and serial killing as these are considered the most dangerous behaviours. A number of conclusions are drawn in relation to the use and challenges of technological means in order to face such criminal behaviours

Artificial Intelligence Adoption in Criminal Incestigations: Challenges and Opportunities for Research

Artificial Intelligence (AI) offers the potential to transform organisational decision-making and knowledge-sharing processes that support criminal investigations. Yet, there is still limited evidence-based knowledge concerning the successful use of AI for criminal investigations in literature. This paper identifies the main areas and current dynamics of the adoption of AI in criminal investigations using bibliometric analysis. We synthesise existing research by identifying key themes researchers have delved into on AI in criminal investigations. The themes include crime prediction and human-centred issues relating to AI use in criminal investigations. Finally, the paper elaborates on the challenges that may influence AI adoption in criminal investigations by police professionals. These challenges include possible laggard effects with AI adoption, implementation challenges, lack of government oversight, and a skills gap

Exploring Text Mining and Analytics for Applications in Public Security: An in-depth dive into a systematic literature review

Text mining and related analytics emerge as a technological approach to support human activities in extracting useful knowledge through texts in several formats. From a managerial point of view, it can help organizations in planning and decision-making processes, providing information that was not previously evident through textual materials produced internally or even externally. In this context, within the public/governmental scope, public security agencies are great beneficiaries of the tools associated with text mining, in several aspects, from applications in the criminal area to the collection of people's opinions and sentiments about the actions taken to promote their welfare. This article reports details of a systematic literature review focused on identifying the main areas of text mining application in public security, the most recurrent technological tools, and future research directions. The searches covered four major article bases (Scopus, Web of Science, IEEE Xplore, and ACM Digital Library), selecting 194 materials published between 2014 and the first half of 2021, among journals, conferences, and book chapters. There were several findings concerning the targets of the literature review, as presented in the results of this article