Abstract
Nowadays, false news can be created and disseminated easily through the many social media platforms, resulting in a widespread real-world impact. Modeling and characterizing how false information proliferates on social platforms and why it succeeds in deceiving readers are critical to develop efficient algorithms and tools for their early detection. A recent surge of researching in this area has aimed to address the key issues using methods based on machine learning, deep learning, feature engineering, graph mining, image and video analysis, together with newly created data sets and web services to identify deceiving content. Majority of the research has been targeting fake reviews, biased messages, and against-facts information (false news and hoaxes). In this work, we present a survey on the state of the art concerning types of fake news and the solutions that are being proposed. We focus our survey on content analysis, network propagation, fact-checking and fake news analysis and emerging detection systems. We also discuss the rationale behind successfully deceiving readers. Finally, we highlight important challenges that these solutions bring.

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The analysis of sentiments, emotions, and opinions in texts is increasingly important in the current digital world. The existing lexicons with emotional annotations for the Portuguese language are oriented to polarities, classifying words as positive, negative, or neutral. To identify the emotional load intended by the author, it is necessary to also categorize the emotions expressed by individual words. EmoSpell is an extension of a morphological analyzer with semantic annotations of the emotional value of words. It uses Jspell as the morphological analyzer and a new dictionary with emotional annotations. This dictionary incorporates the lexical base EMOTAIX. PT, which classifies words based on three different levels of emotions-global, specific, and intermediate. This paper describes the generation of the EmoSpell dictionary using three sources: the Jspell Portuguese dictionary and the lexical bases EMOTAIX. PT and SentiLex-PT. Additionally, this paper details the Web application and Web service that exploit this dictionary. It also presents a validation of the proposed approach using a corpus of student texts with different emotional loads. The validation compares the analyses provided by EmoSpell with the mentioned emotional lexical bases on the ability to recognize emotional words and extract the dominant emotion from a text.

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The society’s requirement for constant connectivity, leads to the need for an increasing number of available Wi-Fi Access Points (APs). These can be located almost everywhere: schools, coffee shops, shopping malls, airports, trains, buses. This proliferation raises problems of trustworthiness and cost-effective difficulties for verifying such security. In order to address these issues, it is necessary to detect effectively Rogue Access Points (RAPs). There are open source solutions and others developed within enterprises for commercial purposes. Relative to the latter, it has become obvious that they are not accessible to everyone due to their high costs, and the former do not address all the types of RAPs. In this paper, we research these solutions and do a thorough survey study of the most commonly used and recent Wi-Fi type of attacks. Based on this knowledge we developed a solution to detect RAPs, which covers the most commonly known attacks. This proposed solution, is a modular framework composed of Scanners, Detectors and Actuators, which are responsible for scanning for available APs, apply a set of heuristics to detect them and apply a countermeasure mechanism.

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