Fake News on the Covid-19 outbreak: a new metadata-based dataset for the analysis of Brazilian and British Twitter posts

Abstract. The dissemination of fake news is a problem that has already been ad-dressed but by no means is solved. After the manipulation made by Cambridge Analytica which was based on classifying users by their political views and tar-geting specific political propaganda on the Brexit campaign, the Trump election and the Bolsonaro election, there is no doubt this issue can have a real impact on society. During a pandemic, any type of fake news can be the difference between life and death when the data shared can directly hurt the people who are believing in it. Moreover, there is also a new trend of using artificial robots to disseminate such news with a special target on Twitter which can be linked with political campaigns. Thus, it is essential that we identify and understand what kind of news is selected to be dressed as fake and how it is disseminated. This paper aims to investigate the dissemination of fake news related withCovid-19 in the UK and Brazil in order to understand the impact of fake news on public sector actions, social isolation and quarantine imposition. Those two case studies are well versed on the fake news dissemination. Our initial dataset of Twitter posts has focused on posts from four different cities (Natal, Sao Paulo, Sheffield and London) and has shown interesting pointers that will be discussed

Investigating the use of feature selection techniques for gender prediction systems based on keystroke dynamics

Biometric-based solutions keep expanding with new modalities, techniques and systems being proposed every so often. However, the first ones that were used for authentication, such as handwritten signature and keystroke dynamics, continue to be relevant in our digital world, despite their analogical origin. In special, keystroke dynamics has had an increase in popularity with the advent of social networks, making the need to continue to authenticate in desktop or game-based user verification more prevalent and this became an open door to risky situations such as paedophilia, sexual abuse, harassment among others. One of the ways to combat this type of crime is to be able to verify the legitimacy of the gender of the person the user is typing with. Despite the fact that keystroke dynamics is well accepted and reliable, this technique can have far too many attributes to be analysed which can lead to the use of redundant or irrelevant information. Therefore, propose a comparative study between two features selection approaches, hybrid (filter + wrapper) and wrapper. They will be tested by using a genetic algorithm, a particle swarm optimisation, a k -NN, a SVM, and a Naive Bayes as classifiers, as well as, the Correlation and Relief filters. From the results obtained, it can be said that the two proposed hybrid approaches reduce the number of attributes, without negatively impacting the accuracy of the classification, and being less costly than the traditional PSO

An investigation of the predictability of the Brazilian three-modal hand-based behavioural biometric: a feature selection and feature-fusion approach

Abstract: New security systems, methods or techniques need to have their performance evaluated in conditions that closely resemble a real-life situation. The effectiveness with which individual identity can be predicted in different scenarios can benefit from seeking a broad base of identity evidence. Many approaches to the implementation of biometric-based identification systems are possible, and different configurations are likely to generate significantly different operational characteristics. The choice of implementational structure is, therefore, very dependent on the performance criteria, which is most important in any particular task scenario. The issue of improving performance can be addressed in many ways, but system configurations based on integrating different information sources are widely adopted in order to achieve this. Thus, understanding how each data information can influence performance is very important. The use of similar modalities may imply that we can use the same features. However, there is no indication that very similar (such as keyboard and touch keystroke dynamics, for example) basic biometrics will perform well using the same set of features. In this paper, we will evaluate the merits of using a three-modal hand-based biometric database for user prediction focusing on feature selection as the main investigation point. To the best of our knowledge, this is the first thought-out analysis of a database with three modalities that were collected from the same users, containing keyboard keystroke, touch keystroke and handwritten signature. First, we will investigate how the keystroke modalities perform, and then, we will add the signature in order to understand if there is any improvement in the results. We have used a wide range of techniques for feature selection that includes filters and wrappers (genetic algorithms), and we have validated our findings using a clustering technique