Abstract
Electronic identity (eID) schemes are key enablers of secure digital services. eIDs have been adopted in several European countries using smart-cards for secure authentication and authorization. Towards achieving a European digital single market where European citizens can seamlessly access cross-border public services using their national eIDs, the European Union (EU) developed the electronic IDentification, Authentication and trust Services (eIDAS) regulation. eIDAS creates an interoperable framework that integrates the eIDs adopted in the EU Member States (MS). It is also an enabler of a cross-border operation, harmonized with the General Data Protection Regulation (GDPR) regulation by protecting the privacy of personal data. If one can use the same procedure for authentication and authorization abroad, one can better understand new services that use eIDs. This paper provides a comparative analysis of eID cards adopted in EU MS and their privacy features in preparedness for eIDs cross-border interoperation.

Abstract
The benefits of blockchain go beyond its applicability in finance. Electronic Voting Systems (EVS) are considered as a way to achieve a more effective act of voting. EVS are expected to be verifiable and tamper resistant. The blockchain partially fulfills this requirements of EVS by being an immutable, verifiable and distributed record of transactions. The adoption of EVS has been hampered mainly by cultural and political issues rather than technological ones. The authors believe that blockchain is the technology that, due to the overall attention it has been receiving, is capable of fostering the adoption of EVS. In the current work we compare blockchain-based EVS, identifying their strengths and shortcomings.

Abstract
The digital economy, online presence and the increasing number of phishing attacks, are common realities in today's operations of a significant number of companies. Some of these attacks resulted in significant financial losses and reputational damage. Companies do not address the problem before- hand. The first step should be the assessment of the exposure of the company to phishing attacks. An assessment methodology is proposed, evaluated and tested using two complete, and real, runs of the methodology.

Abstract
The burst of social networks and the possibility of being continuously connected has provided a fast way for information diffusion. More specifically, real-time posting allowed news and events to be reported quicker through social networks than traditional news media. However, the massive data that is daily available makes newsworthy information a needle in a haystack. Therefore, our goal is to build models that can detect journalistic relevance automatically in social networks. In order to do it, it is essential to establish a ground truth with a large number of entries that can provide a suitable basis for the learning algorithms due to the difficulty inherent to the ambiguity and wide scope associated with the concept of relevance. In this paper, we propose and compare two different methodologies to annotate posts regarding their relevance: automatic and human annotation. Preliminary results show that supervised models trained with the automatic annotation methodology tend to perform better than using human annotation in a test dataset labeled by experts.

Abstract
Sentiment lexicons are an essential component on most state-of-the-art sentiment analysis methods. However, the terms included are usually restricted to verbs and adjectives because they (1) usually have similar meanings among different domains and (2) are the main indicators of subjectivity in the text. This can lead to a problem in the classification of short informal texts since sometimes the absence of these types of parts of speech does not mean an absence of sentiment. Therefore, our hypothesis states that knowledge of terms regarding certain events and respective sentiment (public opinion) can improve the task of sentiment analysis. Consequently, to complement traditional sentiment dictionaries, we present a system for lexicon expansion that extracts the most relevant terms from news and assesses their positive or negative score through Twitter. Preliminary results on a labelled dataset show that our complementary lexicons increase the performance of three state-of-the-art sentiment systems, therefore proving the effectiveness of our approach.

Abstract
Misinformation propagation on social media has been significantly growing, reaching a major exposition in the 2016 United States Presidential Election. Since then, the scientific community and major tech companies have been working on the problem to avoid the propagation of misinformation. For this matter, research has been focused on three major sub-fields: the identification of fake news through the analysis of unreliable posts, the propagation patterns of posts in social media, and the detection of bots and spammers. However, few works have tried to identify the characteristics of a post that shares unreliable content and the associated behaviour of its account. This work presents four main contributions for this problem. First, we provide a methodology to build a large knowledge database with tweets who disseminate misinformation links. Then, we answer research questions on the data with the goal of bridging these problems to similar problem explored in the literature. Next, we focus on accounts which are constantly propagating misinformation links. Finally, based on the analysis conducted, we develop a model to detect social media accounts that spread unreliable content. Using Decision Trees, we achieved 96% in the F1-score metric, which provides reliability on our approach.