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. Copyright 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

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. Copyright

Abstract
Purpose The purpose of this paper is to empirically test the relations among different knowledge management (KM) factors, such as human capital (HC), processes and information systems (IS) on organizational sustainable competitive advantage (CA), within the SMEs context. Design/methodology/approach Structured questionnaires were distributed to CEOs and managers of Portuguese organizations through an electronic survey. Partial least squares software was utilized to analyze the data. Findings The measurement model results identify and validate the dimensions of HC, processes and IS representing the KM construct. The structural model results demonstrate that HC and processes have a direct and significant impact on organizational CA, on the customer and financial dimensions, respectively. IS indirectly and significantly influence organizational CA, mediated by HC and processes. Research limitations/implications The sample size includes mostly service business and SMEs. Other organizations sectors, such as industry, should be analyzed in order to develop a comparative cross-sectorial study. Practical implications This study establishes suggestions for managers to make legitimate decisions concerning investments on knowledge assets and organizational capabilities that can foster business growth and sustainable CA within a SMEs context. Originality/value The authors propose a mediation mechanism showing that the relationship between IS and sustainable CA is not direct, but it is mediated by HC and processes. This mechanism points out some critical issues for the strategic knowledge and intellectual capital assets, as a source of organizational CA.

Abstract

Abstract