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
As organizations are entering social media, determining their current strategy will allow to combine monitoring and benchmarking methods to foster the identification of opportunities and threats, which can serve as inputs for the evaluation of social media strategies' and eventual readjustments, and a subsequent efficiency measurement. Higher Educational Institutions (HEI) are not different from other organizations in which concerns these problems. To address these challenges, we propose an automatic procedure to assess the posting behavior and strategy identification for each higher educational institution. We used a sample of the 10-top worldwide ranked educational institutions in this study and collected the posts from their official Facebook pages during an entire school year. Our study was conducted on the frequency and intensity of publications by universities, which included an analysis of the number of responses to 'posts' over time in the form of 'shares'. Finally, the content of the posts was analyzed according to the topics covered in the messages. This process allowed us to identify the editorial areas that each university uses the most and in which are more active. © 2018 The Authors. Published by Elsevier Ltd..

Abstract
Organizations are rushing into social media networks following a worldwide trend to create a social presence in multiple media channels. However, a social media strategy needs to be aligned with and framed in the overall organizational strategic management goals. Higher Educational Institutions (HEI) are not different from other organizations in which concerns these problems. Determining the organizational positioning of an organization current strategy will allow to combine monitoring and benchmarking methods to foster the identification of opportunities and threats, which can serve as inputs for the internal evaluation of social media strategies', for the necessary strategic readjustments and a subsequent efficiency measurement. In order to address these challenges, we propose a three-step automatic data-mining procedure to assess the posting behavior and strategy of HEI, understand the editorial policy behind it, and predict the future HEI engagement. We used a sample of the 5-top ranked educational institutions in 2017. We collected the posts from each HEI official Facebook page during an entire school year. Our method showed high degree of accuracy and is also capable of describing which topics are most common in each university's social media content strategy and relate them to the corresponding response from their publics.

Abstract
A few years back organizations were rushing into social media environments following the worldwide trend to create a social presence in multiple channels and / or to explore their potential. Currently, after having gone through a period of experimentation and consolidation of that presence, it is important to understand and to report on how the performance and communication efficiency of organizations has evolved. On previous studies, where we focused on the public higher education sector, we have identified a set of organizations that presented behaviour which was typical from yearly social media adopters, with very low relative performance and communication efficiency. Using data and text mining tools, and techniques, we showed that these organizations revealed very low frequency of publication of messages and very low engagement among their audiences. At the time, the analysis of this sector posed challenges to the confirmation of whether these content strategies were representative enough and if they were a result of an effective and permanent organizational behaviour on social media, or just a result of a stage of social media adoption. In this paper, we present a longitudinal study that portrays the evolution of the organizational behaviour of these organizations on social media, concerning their relative performance and their communication efficiency after a four-year period. Our analysis is based on how and if they have evolved from that stage by fine-tuning their social media communications. We also present findings concerning the content strategy structure evolution along the past four years, concerning the type of content used in higher education institutions' social media strategies, to obtain the best possible return on engagement from the publics (fans), demonstrating how these organizations have either dropped Facebook or optimized their type of content to foster higher return. Thus, on this longitudinal study we present and benchmark the current state of performance of public higher education institutions, concerning the path they undertook in the past four years.

Abstract